Authors: Amirhesam Aghanouri, Mohamed Sabry, Joshua Cherian Varughese, Cristina Olaverri-Monreal
This paper presents an experimental investigation and performance evaluation of a hybrid electric radio-controlled car powered by a Nickel-Metal Hydride battery combined with a renewable Proton Exchange Membrane Fuel Cell system. The study evaluates the performance of the system under various load-carrying scenarios and varying environmental conditions, simulating real-world operating conditions including throttle operation. In order to build a predictive model, gather operational insights, and detect anomalies, data-driven analyses using signal processing and modern machine learning techniques were employed. Specifically, machine learning techniques were used to distinguish throttle levels with high precision based on the operational data. Anomaly and change point detection methods enhanced voltage stability, resulting in fewer critical faults in the hybrid system compared to battery-only operation. Temporal Convolutional Networks were effectively employed to predict voltage behavior, demonstrating potential for use in planning the locations of fueling or charging stations. Moreover, integration with a solar-powered electrolyzer confirmed the system's potential for off-grid, renewable hydrogen use. The results indicate that integrating a Proton Exchange Membrane Fuel Cell with Nickel-Metal Hydride batteries significantly improves electrical performance and reliability for small electric vehicles, and these findings can be a potential baseline for scaling up to larger vehicles.
Authors: Shumaila Javaid, Nasir Saeed
Integrated Satellite Aerial Terrestrial Networks (ISATNs) are envisioned as key enablers of 6G, providing global connectivity for applications such as autonomous transportation, Industrial IoT, and disaster response. Their large-scale deployment, however, risks unsustainable energy use and carbon emissions. This work advances prior energy-aware studies by proposing a carbon-aware orchestration framework for ISATNs that leverages Digital Twin (DT) technology. The framework adopts grams of CO$_2$-equivalent per bit (gCO$_2$/bit) as a primary sustainability metric and implements a multi timescale Plan Do Check Act (PDCA) loop that combines day-ahead forecasting with real-time adaptive optimization. ISATN-specific control knobs, including carbon-aware handovers, UAV duty cycling, and renewable-aware edge placement, are exploited to reduce emissions. Simulation results with real carbon intensity data show up to 29\% lower gCO$_2$/bit than QoS-only orchestration, while improving renewable utilization and resilience under adverse events.
Authors: Yiheng Wang
Intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) systems provide a new paradigm for reconfigurable and flexible wireless communications. To enable more energy efficient and spectrum efficient IRS assisted UAV wireless communications, this paper introduces a novel IRS-assisted UAV enabled spectrum sharing system with orthogonal frequency division multiplexing (OFDM). The goal is to maximize the energy efficiency (EE) of the secondary network by jointly optimizing the beamforming, subcarrier allocation, IRS phase shifts, and the UAV trajectory subject to practical transmit power and passive reflection constraints as well as UAV physical limitations. A physically grounded propulsion-energy model is adopted, with its tight upper bound used to form a tractable EE lower bound for the spectrum sharing system. To handle highly non convex, time coupled optimization problems with a mixed continuous and discrete policy space, we develop a deep reinforcement learning (DRL) approach based on the actor critic framework. Extended experiments show the significant EE improvement of the proposed DRL-based approach compared to several benchmark schemes, thus demonstrating the effectiveness and robustness of the proposed approach with mobility.
Authors: Yangjun Zeng (1), Yiwei Qiu (1), Li Jiang (1), Jie Zhu (1), Yi Zhou (1), Jiarong Li (2), Shi Chen (1), Buxiang Zhou (1) ((1) College of Electrical Engineering, Sichuan University, (2) Harvard John A. Paulson School of Engineering and Applied Sciences)
Thyristor rectifiers (TRs) are cost-effective power supplies for hydrogen electrolyzers (ELZs) but introduce harmonic distortion that may violate grid codes. This letter proposes a self-governing harmonic mitigation strategy through coordinated operation of multiple ELZs in large power-to-hydrogen (P2H) plants. First, the harmonic model of TR-powered ELZs is derived, revealing a natural harmonic cancellation mechanism among them. Based on this, a system-level operation scheme based on phasor modulation is developed and integrated into plant scheduling. Case studies demonstrate that the proposed method reduces harmonic currents by 21.2%-39.7% and ensures grid-code compliance, with only a 0.25% loss in hydrogen output, while increasing total revenue by over 21\% compared to production-oriented strategies.
Authors: Weijie Xia, Chenguang Wang, Peter Palensky, Pedro P. Vergara
Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.
Authors: Emmanuel O. Badmus, Peng Sang, Dimitrios Stamoulis, Amritanshu Pandey
Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.
Authors: Carsten Andrich, Isabella Varga, Tobias F. Nowack, Alexander Ihlow, Sebastian Giehl, Michael Schubert, Reiner S. Thomä, Matthias A. Hein
Bistatic radar measurements offer unique spatial diversity and enhanced target characterization capabilities, rendering them increasingly vital for contemporary sensing application research. The reliability of such measurements is contingent upon precise system and antenna calibration. The prevailing technique is the substitution method, which involves the use of known reference objects. We propose an over-the-air calibration algorithm for spherical bistatic measurement systems. Our method is both significantly simpler and twice as fast as existing algorithms. The application of our technique to reflectivity measurements of a metal sphere from 76 to 81 GHz demonstrates a dynamic range enhancement of up to 40 dB when compared with uncalibrated data. A comparison with simulation data demonstrates a high degree of agreement between measurement and simulation.
Authors: Muhammad Hamza Ali, Amritanshu Pandey
The high penetration of distributed energy resources, resulting in backfeed of power at the transmission and distribution interface, is causing conventional underfrequency load shedding (UFLS) schemes to become nonconforming. Adaptive schemes that update UFLS relay settings recursively in time offer a solution, but existing adaptive techniques that obtain UFLS relay settings with linearized or reduced-order model formulations fail to capture AC nonlinear network behavior. In practice, this will result in relays unable to restore system frequency during adverse disturbances. We formulate an adaptive UFLS problem as a trajectory optimization and include the full AC nonlinear network dynamics to ensure AC feasibility and time-coordinated control actions. We include binary decisions to model relay switching action and time-delayed multi-stage load-shedding. However, this formulation results in an intractable MINLP problem. To enforce model tractability, we relax these binary variables into continuous surrogates and reformulate the MINLP as a sequence of NLPs. We solve the NLPs with a homotopy-driven method that enforces near-integer-feasible solutions. We evaluate the framework on multiple synthetic transmission systems and demonstrate that it scales efficiently to networks exceeding 1500+ nodes with over 170k+ continuous and 73k+ binary decision variables, while successfully recovering binary-feasible solutions that arrest the frequency decline during worst-case disturbance.
Authors: Sayak Mukherjee, Himanshu Sharma, Wenceslao Shaw Cortez, Genevieve Starke, Michael Sinner, Brooke J. Stanislawski, Zachary Tully, Paul Fleming, Sonja Glavaski
This research investigates designing a supervisory feedback controller for a hybrid power plant that coordinates the wind, solar, and battery energy storage plants to meet the desired power demands. We have explored an online feedback control design that does not require detailed knowledge about the models, known as feedback optimization. The control inputs are updated using the gradient information of the cost and the outputs with respect to the input control commands. This enables us to adjust the active power references of wind, solar, and storage plants to meet the power generation requirements set by grid operators. The methodology also ensures robust control performance in the presence of uncertainties in the weather. In this paper, we focus on describing the supervisory feedback optimization formulation and control-oriented modeling for individual renewable and storage components of the hybrid power plant. The proposed supervisory control has been integrated with the hybrid plant co-simulation engine, Hercules, demonstrating its effectiveness in more realistic simulation scenarios.
Authors: Sen Zhan, Lingkang Jin, Haoyang Zhang, Nikolaos G. Paterakis
The secure operation of power distribution systems is challenged by the growing integration of distributed energy resources. Leveraging the flexibility of battery storage offers a cost-effective alternative to measures like generation curtailment, which results in energy losses. However, developing an effective operational model for battery storage is hindered by inaccurate grid models, unavailability of load data, nonlinear relationship between power injections and network states, intertemporal constraints, and complex electrochemical and thermal dynamics. To address these challenges, this paper proposes a data-driven operational control scheme for battery storage in distribution systems. Linear and convex quadratic operational constraints are constructed based on real-time distribution system and battery storage measurements. Lyapunov optimization decouples multi-period battery operation, enabling a real-time, forecast-free control strategy with low computational complexity. Numerical studies using nonlinear distribution system and battery storage simulators validate the effectiveness of the approach in ensuring secure distribution system operation and satisfaction of voltage and thermal constraints of battery storage.
Authors: Christoph Kaufmann, Georg Pangalos, Gerwald Lichtenberg, Oriol Gomis-Bellmunt
This paper proposes a new approach to perform small-signal stability analysis based on linearization of implicit multilinear models. Multilinear models describe the system dynamics by multilinear functions of state, input, and algebraic variables. Using suitable transformations of variables, they can also represent trigonometric functions, which often occur in power systems modeling. This allows tensor representations of grid-following and grid-forming power converters. This paper introduces small-signal stability analysis of equilibrium points based on implicit multilinear models using generalized eigenvalues. The generalized eigenvalues are computed from linear descriptor models of the linearized implicit multilinear model. The proposed approach is tested using a 3-bus network example, first by comparing time-domain simulations of the implicit multilinear model with those of the nonlinear model, and second by comparing the generalized eigenvalues with those of the linearized nonlinear model. The results show that the decomposed tensor representation of the implicit multilinear model allows for a faster linearization compared to conventional methods in MATLAB Simulink.
Authors: Samuel Talkington, Daniel Turizo, Sergio A. Dorado-Rojas, Rahul K. Gupta, Daniel K. Molzahn
The power flow equations relate bus voltage phasors to power injections via the network admittance matrix. These equations are central to the key operational and protection functions of power systems (e.g., optimal power flow scheduling and control, state estimation, protection, and fault location, among others). As control, optimization, and estimation of network admittance parameters are central to multiple avenues of research in electric power systems, we propose a linearization of power flow solutions obtained by implicitly differentiating them with respect to the network admittance parameters. This is achieved by utilizing the implicit function theorem, in which we show that such a differentiation is guaranteed to exist under mild conditions and is applicable to generic power systems (radial or meshed). The proposed theory is applied to derive sensitivities of complex voltages, line currents, and power flows. The developed theory of linearizing the power flow equations around changes in the complex network admittance parameters has numerous applications. We demonstrate several of these applications, such as predicting the nodal voltages when the network topology changes without solving the power flow equations. We showcase the application for continuous admittance control, which is used to increase the hosting capacity of a given distribution network.
Authors: Michael Nestor, Jiaxin Wang, Ning Zhang, Fei Teng
The increasing penetration of inverter-based resources into the power grid, with often only black-box models available, challenges long-standing frequency control methods. Most recent works take a decentralized approach without online device coordination via communication. This paper considers both dynamic behavior and communication within secondary frequency control on an intermediate timescale. We develop a distributed data-driven approach that utilizes peer-to-peer communication between inverters to avoid the need for a central control center. To enable a trade off between communication network requirements and control performance, we present a framework to guide communication topology design for secondary frequency regulation. Following design of the inter-agent information exchange scheme, we design a controller that is structured according to the communication topology with a closed-loop stability guarantee. Case studies on the IEEE 39-bus system validate the framework and illustrate the trade-off between communication requirements and control performance that is enabled by our approach.
Authors: Ziqing Zhu
Virtual power plants (VPPs) are becoming a cornerstone of future grids, aggregating distributed PV, wind, storage, and flexible loads for market participation and real-time balancing. As operations move to minute-- and second--level feedback, communication security shifts from a compliance item to an operational constraint: latency, reliability, and confidentiality jointly determine whether dispatch, protection, and settlement signals arrive on time. Conventional PKI and key-rotation schemes struggle with cross-domain, high-frequency messaging and face long-term quantum threats. Quantum key distribution (QKD) offers information-theoretic key freshness, but its key yield is scarce and stochastic, often misaligned with bursty VPP traffic. This paper proposes a key-aware priority and quota framework that treats quantum keys as first-class scheduling resources. The design combines (i) forecast-driven long-term quotas and short-term tokens, (ii) key-aware deficit-round-robin arbitration, (iii) a preemptive emergency key reserve, and (iv) graceful degradation via encryption-mode switching and controlled down-sampling for non-critical traffic. A drift-plus-penalty analysis establishes strong stability under average supply--demand balance with quantifiable bounds on backlog and tail latency, providing interpretable operating guarantees. We build a reproducible testbed on IEEE 33- and 123-bus VPP systems and evaluate normal, degraded, and outage regimes with industry-consistent message classes and TTLs. Against FIFO, fixed-priority, and static-quota baselines, the proposed scheme consistently reduces tail delay and passive timeouts for critical messages, improves per-bit key utility, and enhances power-tracking reliability during key scarcity and regime switches.
Authors: Zhe Yu, Chuang Yang, Qin Wang
With the large-scale integration of electric vehicles (EVs) in the distribution grid, the unpredictable nature of EV charging introduces considerable uncertainties to the grid's real-time operations. This can exacerbate load fluctuations, compromise power quality, and pose risks to the grid's stability and security. However, due to their dual role as controllable loads and energy storage devices, EVs have the potential to mitigate these fluctuations, balance the variability of renewable energy sources, and provide ancillary services that support grid stability. By leveraging the bidirectional flow of information and energy in smart grids, the adverse effects of EV charging can be minimized and even converted into beneficial outcomes through effective real-time management strategies. This paper explores the negative impacts of EV charging on the distribution system's real-time operations and outlines methods to transform these challenges into positive contributions. Additionally, it provides an in-depth analysis of the real-time management system for EV charging, focusing on state estimation and management strategies.
Authors: Avishkar Behera, Riya Ann Easow, Venkatesh Parvathala, K. Sri Rama Murty
This paper introduces a novel application of Test-Time Training (TTT) for Speech Enhancement, addressing the challenges posed by unpredictable noise conditions and domain shifts. This method combines a main speech enhancement task with a self-supervised auxiliary task in a Y-shaped architecture. The model dynamically adapts to new domains during inference time by optimizing the proposed self-supervised tasks like noise-augmented signal reconstruction or masked spectrogram prediction, bypassing the need for labeled data. We further introduce various TTT strategies offering a trade-off between adaptation and efficiency. Evaluations across synthetic and real-world datasets show consistent improvements across speech quality metrics, outperforming the baseline model. This work highlights the effectiveness of TTT in speech enhancement, providing insights for future research in adaptive and robust speech processing.
Authors: Ziqing Zhu
The rapid decentralization and digitalization of local electricity markets have introduced new cyber-physical vulnerabilities, including key leakage, data tampering, and identity spoofing. Existing blockchain-based solutions provide transparency and traceability but still depend on classical cryptographic primitives that are vulnerable to quantum attacks. To address these challenges, this paper proposes Q-EnergyDEX, a zero-trust distributed energy trading framework driven by quantum key distribution and blockchain. The framework integrates physical-layer quantum randomness with market-level operations, providing an end-to-end quantum-secured infrastructure. A cloud-based Quantum Key Management Service continuously generates verifiable entropy and regulates key generation through a rate-adaptive algorithm to sustain high-quality randomness. A symmetric authentication protocol (Q-SAH) establishes secure and low-latency sessions, while the quantum-aided consensus mechanism (PoR-Lite) achieves probabilistic ledger finality within a few seconds. Furthermore, a Stackelberg-constrained bilateral auction couples market clearing with entropy availability, ensuring both economic efficiency and cryptographic security. Simulation results show that Q-EnergyDEX maintains robust key stability and near-optimal social welfare, demonstrating its feasibility for large-scale decentralized energy markets.
Authors: Tina Gao, Shimiao Li, Lawrence Pileggi
Advances in leveraging Gaussian processes (GP) have enabled learning and inferring dynamic grid behavior from scarce PMU measurements. However, real measurements can be corrupted by various random and targeted threats, leading to inaccurate and meaningless results. This paper develops robust transient learning to overcome this challenge by exploiting the sparse corruption patterns in the data flow. Specifically, we integrate sparse optimization with method of moments (MoM) to make learning robust to a sparse distribution of data corruptions; then, we optimize sparse weights to identify corrupted meter locations. To improve inference speed on large-scale systems, we further adopt K-medoid clustering of locations to develop dimension reduction (DR) and aggregate representation (AR) heuristics. Experimental results demonstrate robustness against random large errors, targeted false data injections, and local PMU clock drifts. On a 1354-bus system, inference turns out to be 18x faster using DR and 400x faster when further combined with AR heuristics.
Authors: Ziqing Zhu
The proliferation of distributed energy resources (DERs) and demand-side flexibility has made virtual power plants (VPPs) central to modern grid operation. Yet their end-to-end business pipeline, covering bidding, dispatch, metering, settlement, and archival, forms a tightly coupled cyber-physical-economic system where secure and timely communication is critical. Under the combined stress of sophisticated cyberattacks and extreme weather shocks, conventional cryptography offers limited long-term protection. Quantum key distribution (QKD), with information-theoretic guarantees, is viewed as a gold standard for securing critical infrastructures. However, limited key generation rates, routing capacity, and system overhead render key allocation a pressing challenge: scarce quantum keys must be scheduled across heterogeneous processes to minimize residual risk while maintaining latency guarantees. This paper introduces a quantum-authenticated aggregation and settlement framework for VPPs. We first develop a system-threat model that connects QKD key generation and routing with business-layer security strategies, authentication strength, refresh frequency, and delay constraints. Building on this, we formulate a key-budgeted risk minimization problem that jointly accounts for economic risk, service-level violations, and key-budget feasibility, and reveal a threshold property linking marginal security value to shadow prices. Case studies on a representative VPP system demonstrate that the proposed approach significantly reduces residual risk and SLA violations, enhances key efficiency and robustness, and aligns observed dynamics with the theoretical shadow price mechanism.
Authors: Ziqing Zhu
The accelerating digitalization and decentralization of modern power systems expose critical communication infrastructures to escalating cyber risks, particularly under emerging quantum computing threats. This paper presents an integrated techno-economic framework to evaluate the feasibility of Quantum Key Distribution (QKD) for secure power-system communications. A stochastic system model is developed to jointly capture time-varying key demand, QKD supply under optical-loss constraints, station-side buffering, and post-quantum cryptography (PQC) fallback mechanisms. Analytical conditions are derived for service-level assurance, including buffer stability, outage probability, and availability bounds. Building on this, two quantitative metrics, including the Levelized Cost of Security (LCoSec) and Cost of Incremental Security (CIS), are formulated to unify capital, operational, and risk-related expenditures within a discounted net-present-value framework. Using IEEE 118-bus, 123-node, and 39-bus test systems, we conduct discrete-event simulations comparing PQC-only, QKD-only, and Hybrid architectures across multiple topologies and service profiles. Results show that Hybrid architectures dominated by QKD significantly reduce key-outage probability and SLA shortfalls, achieving near-unit availability for real-time and confidentiality-critical services. Economic analyses reveal clear breakeven zones where QKD-enhanced deployments become cost-effective, primarily in metropolitan and distribution-level networks under moderate optical loss and buffer sizing. The proposed framework provides a reproducible, risk-aware decision tool for guiding large-scale, economically justified QKD adoption in future resilient power-system infrastructures.
Authors: Saeid Bayat, Jerry Zuo, Jing Sun
Offshore renewable energy systems offer promising solutions for sustainable power generation, yet most existing platforms harvest either wind or wave energy in isolation. This study presents a hybrid floating offshore platform that integrates a wind turbine with three oscillating surge wave energy converters (WECs) into a hexagonal semi-submersible structure. In this configuration, the flaps are integrated with the platform geometry to provide both energy extraction and hydrodynamic stability. A modeling and simulation framework was developed using WEC-Sim and benchmarked against the NREL 5 MW semisubmersible reference. Metacentric height analysis confirmed hydrostatic stability across a range of prescribed flap angles. Sensitivity analysis of twelve geometric variables identified flap dimensions and tower length as dominant drivers of stability, energy capture, and tower stress. Time-domain simulations revealed dependence on wave incidence angle, with variations in flap power sharing, capture width ratio (CWR), and platform response. The feasibility of using flap sweeps to modulate pitch motion was also demonstrated. Annual energy production (AEP) estimates based on site-specific data indicate 16.86 GWh from wind and 3.65 GWh from wave energy, with WECs contributing about 18% of the total. These results highlight the potential of integrated wind-wave platforms and point toward future studies on structural modeling and advanced control.
Authors: Clément Moureau, Thomas Stegen, Mevludin Glavic, Bertrand Cornélusse
This paper presents a predictive control strategy to manage low-voltage distribution systems. The proposed approach relies on an aggregate of the flexibility at the residential unit level into a three-dimensional chart that represents the injected active and reactive power, and the flexibility cost. First, this method solves a multiparametric optimization problem offline at the residential unit level to aggregate the flexibility of the assets. Then, a semi-explicit model predictive control problem is solved to account for forecasts. By combining the results of these problems with measurements, the method generates the desired flexibility chart. The proposed approach is compatible with realtime control requirements, as heavy computations are performed offline locally, making it naturally parallelizable. By linking realtime flexibility assessment with energy scheduling, our approach enables efficient, low-cost, and privacy-preserving management of low-voltage distribution systems. We validate this method on a low-voltage network of 5 buses by comparing it with an ideal technique.
Authors: Sheng Wang, Muhammad Maladoh Bah
European countries are ambitious in both the net-zero transition and offshore energy resource development. The Irish and UK governments announced their commitments to offshore wind capacities - 37 and 125 GW, respectively, in 2050, more than two times higher than their projected power demands. While other continental countries, such as Germany, are calling for cleaner fuel resources. Exporting surplus offshore green hydrogen and bridging supply and demand could be pivotal in carbon emission mitigation for Europe. Yet, the potentials of these Island countries, are usually underestimated. This paper developed a bottom-up method to investigate the role of offshore hydrogen from Ireland and the UK in the decarbonisation of the entire Europe. We evaluate the future hydrogen/ammonia trading and the contributions of each country in carbon emission mitigation, considering their relative cost-competitiveness in offshore hydrogen production, domestic hourly power and gas system operation, and international shipping costs. Results indicate that the offshore green hydrogen could reduce 175.16 Mt/year of carbon dioxide emissions in Europe. The UK will be the largest hydrogen supplier from 2030 to 2040, while surpassed by Ireland in 2050, with 161 TWh of hydrogen exports to France and Spain. The offshore green hydrogen can contribute to 175.16 Mt of annual carbon dioxide emission reductions in total. This general flow of hydrogen from the West to the East not only facilitates Europe's net-zero progress, but also reshapes the energy supply structure and helps to ensure energy security across the European continent.
Authors: Geon Roh, Jip Kim
Dynamic line rating (DLR) enables greater utilization of existing transmission lines by leveraging real-time weather data. However, the elevated temperature operation (ETO) of conductors under DLR is often overlooked, despite its long-term impact on conductor health. This paper addresses this issue by 1) quantifying depreciation costs associated with ETO and 2) proposing a Conductor Health-Aware Unit Commitment (CHA-UC) that internalizes these costs in operational decisions. The CHA-UC incorporates a robust linear approximation of conductor temperature and integration of expected depreciation costs due to hourly ETO into the objective function. Case studies on the Texas 123-bus backbone test system using NOAA weather data demonstrate that the proposed CHA-UC model reduces the total cost by 0.8% and renewable curtailment by 84%compared to static line rating (SLR), while conventional DLR operation without risk consideration resulted in higher costs due to excessive ETO. Further analysis of the commitment decisions and the line temperature statistics confirms that the CHA-UC achieves safer line flows by shifting generator commitments. Finally, we examine the emergent correlation between wind generation and DLR forecast errors, and show that CHA-UC adaptively manages this effect by relaxing flows for risk-hedging conditions while tightening flows for risk-amplifying ones.
Authors: Rui Yuan, Hossein Ranjbar, S. Ali Pourmousavi, Wen L. Soong, Andrew J. Black, Jon A. R. Liisberg, Julian Lemos-Vinasco
Many smart grid applications involve data mining, clustering, classification, identification, and anomaly detection, among others. These applications primarily depend on the measurement of similarity, which is the distance between different time series or subsequences of a time series. The commonly used time series distance measures, namely Euclidean Distance (ED) and Dynamic Time Warping (DTW), do not quantify the flexible nature of electricity usage data in terms of temporal dynamics. As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e.g., demand response and load profiling. This paper introduces a novel distance measure to compare electricity usage patterns. The method consists of two phases that quantify the effort required to reshape one time series into another, considering both amplitude and temporal changes. The proposed method is evaluated against ED and DTW using real-world data in three smart grid applications. Overall, the proposed measure outperforms ED and DTW in accurately identifying the best load scheduling strategy, anomalous days with irregular electricity usage, and determining electricity users' behind-the-meter (BTM) equipment.
Authors: Kaidi Huang, Lin Cheng, Ning Qi, David Wenzhong Gao, Asad Mujeeb, Qinglai Guo
This paper proposes a novel prediction-free two-stage coordinated dispatch framework for the real-time dispatch of grid-connected microgrid with generalized energy storages (GES). The proposed framework explicitly addresses grid awareness, non-anticipativity constraints, and the time-coupling characteristics of GES, providing microgrid operators with a near-optimal, reliable, and adaptable dispatch tool. In the offline stage, we generate the hindsight state-of-charge (SoC) trajectories of GES by solving the multi-period economic dispatch with historical scenarios. Subsequently, leveraging this historical information (SoC trajectories, net loads, and electricity prices), we synthesize and dynamically update online references for both SoC and opportunity cost through kernel regression. We propose an adaptive Lagrange multiplier-based online convex optimization algorithm, which innovatively incorporates reference tracking for global vision and expert-tracking for step-size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of both dynamic regret and time-varying hard constraint violation. Numerical studies using ground-truth data from the Australian Energy Market Operator demonstrate that the proposed method outperforms state-of-the-art methods, reducing operational costs by 5.0-6.2% and voltage violations by 0.8-9.1%. These improvements mainly result from mitigating myopia by reference tracking and the adaptive capability provided by dynamically updated references and adaptive Lagrange multipliers. Sensitivity analysis demonstrates the robustness, computational efficiency, and scalability of the proposed method.
Authors: Emmanuel O. Badmus, Peng Sang, Dimitrios Stamoulis, Amritanshu Pandey
Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning, necessitating advanced computational analyses to ensure reliability and resilience. These analyses depend on disparate workflows comprising complex models, function calls, and data pipelines that require substantial expert knowledge and remain difficult to automate. Workforce and budget constraints further limit utilities' ability to apply such analyses at scale. To address this gap, we build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses. Existing agentic AI systems are typically developed in a bottom-up manner with customized context for predefined analysis tasks; therefore, they do not generalize to tasks that the agent has never seen. In comparison, to generalize to unseen DG analysis tasks, PowerChain dynamically generates structured context by leveraging supervisory signals from self-contained power systems tools (e.g., GridLAB-D) and an optimized set of expert-annotated and verified reasoning trajectories. For complex DG tasks defined in natural language, empirical results on real utility data demonstrate that PowerChain achieves up to a 144/% improvement in performance over baselines.
Authors: Jiadong Hong, Lei Liu, Xinyu Bian, Wenjie Wang, Zhaoyang Zhang
We propose the Soft Graph Transformer (SGT), a soft-input-soft-output neural architecture designed for MIMO detection. While Maximum Likelihood (ML) detection achieves optimal accuracy, its exponential complexity makes it infeasible in large systems, and conventional message-passing algorithms rely on asymptotic assumptions that often fail in finite dimensions. Recent Transformer-based detectors show strong performance but typically overlook the MIMO factor graph structure and cannot exploit prior soft information. SGT addresses these limitations by combining self-attention, which encodes contextual dependencies within symbol and constraint subgraphs, with graph-aware cross-attention, which performs structured message passing across subgraphs. Its soft-input interface allows the integration of auxiliary priors, producing effective soft outputs while maintaining computational efficiency. Experiments demonstrate that SGT achieves near-ML performance and offers a flexible and interpretable framework for receiver systems that leverage soft priors.
Authors: Shimiao Li, Guannan Qu, Bryan Hooi, Vyas Sekar, Soummya Kar, Larry Pileggi
Power grids increasingly need real-time situational awareness under the ever-evolving cyberthreat landscape. Advances in snapshot-based system identification approaches have enabled accurately estimating states and topology from a snapshot of measurement data, under random bad data and topology errors. However, modern interactive, targeted false data can stay undetectable to these methods, and significantly compromise estimation accuracy. This work advances system identification that combines snapshot-based method with time-series model via Bayesian Integration, to advance cyber resiliency against both random and targeted false data. Using a distance-based time-series model, this work can leverage historical data of different distributions induced by changes in grid topology and other settings. The normal system behavior captured from historical data is integrated into system identification through a Bayesian treatment, to make solutions robust to targeted false data. We experiment on mixed random anomalies (bad data, topology error) and targeted false data injection attack (FDIA) to demonstrate our method's 1) cyber resilience: achieving over 70% reduction in estimation error under FDIA; 2) anomalous data identification: being able to alarm and locate anomalous data; 3) almost linear scalability: achieving comparable speed with the snapshot-based baseline, both taking <1min per time tick on the large 2,383-bus system using a laptop CPU.
Authors: Qinghua Ma, Reetam Sen Biswas, Denis Osipov, Guannan Qu, Soummya Kar, Shimiao Li
Existing or planned power grids need to evaluate survivability under extreme events, like a number of peak load overloading conditions, which could possibly cause system collapses (i.e. blackouts). For realistic extreme events that are correlated or share similar patterns, it is reasonable to expect that the dominant vulnerability or failure sources behind them share the same locations but with different severity. Early warning diagnosis that proactively identifies the key vulnerabilities responsible for a number of system collapses of interest can significantly enhance resilience. This paper proposes a multi-period sparse optimization method, enabling the discovery of {persistent failure sources} across a sequence of collapsed systems with increasing system stress, such as rising demand or worsening contingencies. This work defines persistency and efficiently integrates persistency constraints to capture the ``hidden'' evolving vulnerabilities. Circuit-theory based power flow formulations and circuit-inspired optimization heuristics are used to facilitate the scalability of the method. Experiments on benchmark systems show that the method reliably tracks persistent vulnerability locations under increasing load stress, and solves with scalability to large systems ({on average} taking {around} 200 s per scenario on 2000+ bus systems).
Authors: Milad Hoseinpour, Vladimir Dvorkin
The optimal power flow (OPF) is a multi-valued, non-convex mapping from loads to dispatch setpoints. The variability of system parameters (e.g., admittances, topology) further contributes to the multiplicity of dispatch setpoints for a given load. Existing deep learning OPF solvers are single-valued and thus fail to capture the variability of system parameters unless fully represented in the feature space, which is prohibitive. To solve this problem, we introduce a diffusion-based OPF solver, termed \textit{DiffOPF}, that treats OPF as a conditional sampling problem. The solver learns the joint distribution of loads and dispatch setpoints from operational history, and returns the marginal dispatch distributions conditioned on loads. Unlike single-valued solvers, DiffOPF enables sampling statistically credible warm starts with favorable cost and constraint satisfaction trade-offs. We explore the sample complexity of DiffOPF to ensure the OPF solution within a prescribed distance from the optimization-based solution, and verify this experimentally on power system benchmarks.
Authors: Kevin Wu, Rabab Haider, Pascal Van Hentenryck
Transmission Expansion Planning (TEP) optimizes power grid upgrades and investments to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid constraints. To support growing demand and renewable energy integration, energy storage is emerging as a pivotal asset that provides temporal flexibility and alleviates congestion. This paper develops a multiperiod, two-stage PTDF formulation that co-optimizes transmission upgrades and storage siting/sizing. To ensure scalability, a trust-region, multicut Benders scheme warm-started from per-representative-day optima is proposed. Applied to a 2,000-bus synthetic Texas system under high-renewable projections, the method attains final optimality gaps below 1% and yields a plan with storage at about 180 nodes (32% of peak renewable capacity). These results demonstrate that the proposed PTDF-based methodology efficiently handles large distributed storage fleets, demonstrating scalability at high spatial resolution
Authors: Daniel Russell, Dakota Hamilton, Mads R. Almassalkhi, Hamid R. Ossareh
Integration of distributed energy resources has created a need for autonomous, dynamic voltage regulation. Decentralized Volt-VAr Control (VVC) of grid-connected inverters presents a unique opportunity for voltage management but, if designed poorly, can lead to unstable behavior when in feedback with the grid. We model the grid-VVC closed-loop dynamics with a linearized power flow approach, leveraging historical data, which shows improvement over the commonly used LinDistFlow model. This model is used to design VVC slopes by minimizing steady-state voltage deviation from the nominal value, subject to a non-convex spectral radius stability constraint, which has not been previously implemented within this context. We compare this constraint to existing convex restrictions and demonstrate, through simulations on a realistic feeder, that using the spectral radius results in more effective voltage regulation.
Authors: Ziqing Zhu
To address growing cybersecurity challenges in modern power dispatch systems, this paper proposes a multi-layer modeling and optimization framework for SCADA systems enhanced with quantum key distribution (QKD). While most existing applications of QKD in the power sector focus on building secure point-to-point communication tunnels, they rarely consider the system-level coupling between key dynamics and control scheduling. In contrast, our approach integrates quantum key generation, consumption, inventory prediction, and control latency into a unified model, enabling key-aware reconfiguration of SCADA control chains based on task security demands and real-time resource constraints. To resolve conflicts in key resource allocation between transmission system operators (TSOs) and distribution system operators (DSOs), we formulate a bi-level Stackelberg game and transform it into a mathematical program with complementarity constraints (MPCC). We further develop an efficient Level Decomposition-Complementarity Pruning (LD-CP) algorithm to solve the problem. To support reproducible evaluation, we build an end-to-end co-simulation platform that integrates physical-layer disruptions via OpenQKD-Sim, Q3P/IEC-104 protocol stack binding, and real-time control-chain monitoring through Grafana. Experimental results on the IEEE 39- and 118-bus systems show that our method increases task success rate by 25%, reduces peak frequency deviation by 70%, and improves key utilization to 83%. This work lays the foundation for future quantum-secure control systems in power grid operations.
Authors: Yingrui Zhuang, Lin Cheng, Can Wan, Rui Xie, Ning Qi, Yue Chen
Prediction deviations of different uncertainties have varying impacts on downstream decision-making. Improving the prediction accuracy of critical uncertainties with significant impacts on decision-making quality yields better optimization results. Motivated by this observation, this paper proposes a novel weighted predict-and-optimize (WPO) framework for decision-making under multiple uncertainties. Specifically, we incorporate an uncertainty-aware weighting mechanism into the predictive model to capture the relative impact of each uncertainty on specific optimization tasks, and introduce a problem-driven prediction loss (PDPL) to quantify the suboptimality of the weighted predictions relative to perfect predictions in downstream optimization. By optimizing the uncertainty weights to minimize the PDPL, the proposed WPO framework enables adaptive assessment of uncertainty impacts and joint learning of prediction and optimization. Furthermore, to facilitate weight optimization, we develop a surrogate model that establishes a direct mapping between the uncertainty weights and the PDPL, where enhanced graph convolutional networks and multi-task learning are adopted for efficient surrogate model construction and training. Numerical experiments on the modified IEEE 33-bus and 123-bus systems demonstrate that the proposed WPO framework outperforms the traditional predict-then-optimize paradigm, reducing the PDPL by an average of 55% within acceptable computational time.
Authors: Anna Maddux, Marko Maljkovic, Nikolas Geroliminis, Maryam Kamgarpour
We consider the problem of efficiently learning to play single-leader multi-follower Stackelberg games when the leader lacks knowledge of the lower-level game. Such games arise in hierarchical decision-making problems involving self-interested agents. For example, in electric ride-hailing markets, a central authority aims to learn optimal charging prices to shape fleet distributions and charging patterns of ride-hailing companies. Existing works typically apply gradient-based methods to find the leader's optimal strategy. Such methods are impractical as they require that the followers share private utility information with the leader. Instead, we treat the lower-level game as a black box, assuming only that the followers' interactions approximate a Nash equilibrium while the leader observes the realized cost of the resulting approximation. Under kernel-based regularity assumptions on the leader's cost function, we develop a no-regret algorithm that converges to an $\epsilon$-Stackelberg equilibrium in $O(\sqrt{T})$ rounds. Finally, we validate our approach through a numerical case study on optimal pricing in electric ride-hailing markets.
Authors: Mehron Talebi, Sultan Mahmud, Adam Khalifa, Md Jahidul Islam
We present the design, development, and experimental validation of BlueME, a compact magnetoelectric (ME) antenna array system for underwater robot-to-robot communication. BlueME employs ME antennas operating at their natural mechanical resonance frequency to efficiently transmit and receive very-low-frequency (VLF) electromagnetic signals underwater. We outline the design, simulation, fabrication, and integration of the proposed system on low-power embedded platforms, focusing on portable and scalable applications. For performance evaluation, we deployed BlueME on an autonomous surface vehicle (ASV) and a remotely operated vehicle (ROV) in open-water field trials. Ocean trials demonstrate that BlueME maintains reliable signal transmission at distances beyond 700 meters while consuming only 10 watts of power. Field trials show that the system operates effectively in challenging underwater conditions such as turbidity, obstacles, and multipath interference -- conditions that generally affect acoustics and optics. Our analysis also examines the impact of complete submersion on system performance and identifies key deployment considerations. This work represents the first practical underwater deployment of ME antennas outside the laboratory and implements the largest VLF ME array system to date. BlueME demonstrates significant potential for marine robotics and automation in multi-robot cooperative systems and remote sensor networks.
Authors: Chenyu Tang, Ruizhi Zhang, Shuo Gao, Zihe Zhao, Zibo Zhang, Jiaqi Wang, Cong Li, Junliang Chen, Yanning Dai, Shengbo Wang, Ruoyu Juan, Qiaoying Li, Ruimou Xie, Xuhang Chen, Xinkai Zhou, Yunjia Xia, Jianan Chen, Fanghao Lu, Xin Li, Ninglli Wang, Peter Smielewski, Yu Pan, Hubin Zhao, Luigi G. Occhipinti
At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Moreover, the lack of integrated solutions capable of simultaneously monitoring motor recovery and providing intelligent assistance in home environments hampers rehabilitation outcomes. Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation. A plantar pressure insole equipped with a machine learning pipeline classifies users into motor recovery stages with up to 94\% accuracy, enabling quantitative tracking of walking patterns during daily activities. An optional head-mounted eye-tracking module, together with ambient sensors such as cameras and microphones, supports seamless hands-free control of household devices with a 100\% success rate and sub-second response time. These data streams are fused locally via a hierarchical Internet of Things (IoT) architecture, ensuring low latency and data privacy. An embedded large language model (LLM) agent, Auto-Care, continuously interprets multimodal data to provide real-time interventions -- issuing personalized reminders, adjusting environmental conditions, and notifying caregivers. Implemented in a post-stroke context, this integrated smart home platform increased mean user satisfaction from 3.9 $\pm$ 0.8 in conventional home environments to 8.4 $\pm$ 0.6 with the full system ($n=20$). Beyond stroke, the system offers a scalable, patient-centered framework with potential for long-term use in broader neurorehabilitation and aging-in-place applications.
Authors: Alistair Brash, Junyi Lu, Bruce Stephen, Blair Brown, Robert Atkinson, Craig Michie, Fraser MacIntyre, Christos Tachtatzis
Limited visibility of power distribution network power flows at the low voltage level presents challenges to both distribution network operators from a planning perspective and distribution system operators from a congestion management perspective. Forestalling these challenges through scenario analysis is confounded by the lack of realistic and coherent load data across representative distribution feeders. Load profiling approaches often rely on summarising demand through typical profiles, which oversimplifies the complexity of substation-level operations and limits their applicability in specific power system studies. Sampling methods, and more recently generative models, have attempted to address this through synthesising representative loads from historical exemplars; however, while these approaches can approximate load shapes to a convincing degree of fidelity, the co-behaviour between substations, which ultimately impacts higher voltage level network operation, is often overlooked. This limitation will become even more pronounced with the increasing integration of low-carbon technologies, as estimates of base loads fail to capture load diversity. To address this gap, a Conditional Diffusion model for synthesising daily active and reactive power profiles at the low voltage distribution substation level is proposed. The evaluation of fidelity is demonstrated through conventional metrics capturing temporal and statistical realism, as well as power flow modelling. The results show synthesised load profiles are plausible both independently and as a cohort in a wider power systems context. The Conditional Diffusion model is benchmarked against both naive and state-of-the-art models to demonstrate its effectiveness in producing realistic scenarios on which to base sub-regional power distribution network planning and operations.
Authors: Sanjay Johnson, Dirk Lauinger, Sungho Shin, François Pacaud
As GPU-accelerated mathematical programming techniques mature, there is growing interest in utilizing them to address the computational challenges of power system optimization. This paper introduces this http URL, an open-source modeling library for creating GPU-compatible nonlinear AC optimal power flow models. Built on this http URL, this http URL provides a high-level interface that automatically generates all necessary callback functions for GPU solvers. The library is designed for large-scale problem instances, which may include multiple time periods and security constraints. Using this http URL, we benchmark GPU and CPU solvers on open-source test cases. Our results show that GPU solvers can deliver up to two orders of magnitude speedups compared to alternative tools on CPU for problems with more than 20,000 variables and a solution precision of up to $10^{-4}$, while performance for smaller instances or tighter tolerances may vary.
Authors: Zhiyuan Fan, Elizabeth Dentzer, James Glynn, David S. Goldberg, Julio Friedmann, Bolun Xu
Current decarbonization efforts are falling short of meeting the net-zero greenhouse gas (GHG) emission target, highlighting the need for substantial carbon dioxide removal methods such as direct air capture (DAC). However, integrating DACs poses challenges due to their enormous power consumption. This study assesses the commercial operation of various DAC technologies that earn revenue using monetized carbon incentives while purchasing electricity from wholesale power markets. We model four commercial DAC technologies and examine their operation in three representative locations including California, Texas, and New York. Our findings reveal that commercial DAC operations can take financial advantage of the volatile power market to operate only during low-price periods strategically, offering a pathway to facilitate a cost-efficient decarbonization transition. The ambient operational environment such as temperature and relative humidity has non-trivial impact on abatement capacity. Profit-driven decisions introduce climate-economic trade-offs that might decrease the capacity factor of DAC and reduce total CO2 removal. These implications extend throughout the entire lifecycle of DAC developments and influence power systems and policies related to full-scale DAC implementation. Our study shows that DAC technologies with shorter cycle spans and higher flexibility can better exploit the electricity price volatility, while power markets demonstrate persistent low-price windows that often synergize with low grid emission periods, like during the solar "duck curve" in California. An optimal incentive design exists for profit-driven operations while carbon-tax policy in electricity pricing is counterproductive for DAC systems.
Authors: Levi D. Reyes Premer, Elias N. Pergantis, Leo Semmelmann, Davide Ziviani, Kevin J. Kircher
Electric heat-pump water heaters (HPWHs) could reduce the energy costs, emissions, and power grid impacts associated with water heating, the second-largest energy use in United States housing. However, most HPWHs today require 240 V circuits to power the backup resistance heating elements they use to maintain comfort during large water draws. Installing a 240 V circuit can increase the up-front cost of a HPWH by half or more. This paper develops and field-tests the first control system that enables a 120 V HPWH to efficiently maintain comfort without resistance heating elements. The novel model predictive control (MPC) system enables pre-heating in anticipation of large water draws, which it forecasts using an ensemble of machine learning predictors. By shifting electrical load over time, MPC also reduces energy costs on average by 23% and 28% under time-of-use pricing and hourly pricing, respectively, relative to a 240 V HPWH with standard controls. Compared to the increasingly common practice in 120 V HPWHs of storing water at a constant, high temperature (60 °C) to ensure comfort, MPC saves 37% energy on average. In addition to demonstrating MPC's benefits in a real, occupied house, this paper discusses implementation challenges and costs. A simple payback analysis suggests that a 120 V HPWH, operated by the MPC system developed here, would be economically attractive in most installation scenarios.
Authors: The Minh Nguyen, Nagisa Sugishita, Margarida Carvalho, Amira Dems
Electric vehicle (EV) public charging infrastructure planning faces significant challenges in competitive markets, where multiple service providers affect congestion and user behavior. This work extends existing modeling frameworks by incorporating the presence of competitors' stations and more realistic queueing systems. First, we analyze three finite queueing systems, M/M/1/K, M/M/s/K, and M/Er/s/K, with varying numbers of servers (charging outlets) and service time distributions, deriving analytic expressions for user behavior metrics. Second, we embed the queueing-based user behavior model into a bilevel program, where the upper level locates new charging stations to maximize accessibility (throughput), and the lower level captures users' station choices via a user equilibrium. Third, we apply a reformulation from competitive congested user-choice facility location models to approximately solve the bilevel problem and introduce a surrogate-based heuristic to enhance scalability. Fourth, we showcase our methodology on a real-world case study of an urban area in Montreal (Canada), offering managerial insights into how user-choice behavior assumptions and competition affect throughput and location decisions. The results demonstrate that our model yields (re)location strategies that outperform the existing network. More broadly, this approach provides a tool for incorporating charging service quality-through queueing metrics-and existing competition into station planning.
Authors: Giacomo Bastianel, Dirk Van Hertem, Hakan Ergun, Line Roald
Rising electricity demand and the growing integration of renewables are intensifying congestion in transmission grids. Grid topology optimization through busbar splitting (BuS) and optimal transmission switching can alleviate grid congestion and reduce the generation costs in a power system. However, BuS optimization requires a large number of binary variables, and analyzing all the substations for potential new topological actions is computationally intractable, particularly in large grids. To tackle this issue, we propose a set of metrics to identify and rank promising candidates for BuS, focusing on finding buses where topology optimization can reduce generation costs. To assess the effect of BuS on the identified buses, we use a combined mixed-integer convex-quadratic BuS model to compute the optimal topology and test it with the non-linear non-convex AC optimal power flow (OPF) simulation to show its AC feasibility. By testing and validating the proposed metrics on test cases of different sizes, we show that they are able to identify busbars that reduce the total generation costs when their topology is optimized. Thus, the metrics enable effective selection of busbars for BuS, with no need to test every busbar in the grid, one at a time.
Authors: Yifu Ding, Ruicheng Ao, Pablo Duenas-Martinez, Thomas Magnanti
Many industrial sites rely on diesel-powered light-duty trucks to transport workers and small-scale facilities, which has resulted in a significant amount of greenhouse emissions (GHGs). To address this, we developed a two-stage robust charging infrastructure planning model for electrifying light-duty trucks at industrial sites. The model is formulated as a mixed-integer linear programming (MILP) that optimizes the charging infrastructure, selected from multiple charger types and potential locations, and determines opportunity charging schedules for each truck based on the chosen infrastructure. Given the strict stopping points and schedules at industrial sites, we introduced a scheduling problem with abandonment, where trucks forgo charging if their waiting times exceed a maximum threshold. We also further incorporated the impacts of overnight charging and range anxiety on waiting and abandonment behaviors. To represent the stochastic and heterogeneous parking durations of trucks, we constructed a decision-dependent robust uncertainty set in which parking time variability flexibly depends on charging choices. We applied the model in a case study of an open-pit mining site, which plans charger installations in eight zones and schedules a fleet of around 200 trucks. By decomposing the problem into monthly subproblems and using heuristic approaches, for the whole-year dataset, the model achieves an optimality gap of less than 0.1 % within a reasonable computation time under diverse uncertainty scenarios.
Authors: Jan Brändle, Julie Rousseau, Pulkit Nahata, Gabriela Hug
The growing integration of distributed renewable generation and the electrification of heating and transportation are rapidly increasing the number of flexible devices within modern distribution grids. Leveraging the aggregated flexibility of these small-scale distributed resources is essential to maintaining future grid-wide stability. This work uses the Swiss distribution grid of Walenstadt as a case study to provide insights into the aggregated flexibility potential of distribution grids. It demonstrates that incorporating devices such as heat pumps and photovoltaic systems significantly enhances distribution grid flexibility. It investigates the time-varying nature of aggregated flexibility and highlights how it can vary seasonally. Furthermore, simulations of future scenarios reveal that aggregated flexibility does not increase linearly or monotonically with higher levels of flexible device penetration. This is primarily due to the overloading of individual feeders, which underscores the impact of grid topology and network constraints on the aggregated flexibility potential.
Authors: Andreas C. Makrides, Adam Suski, Elina Spyrou
The transition to a fully decarbonised electricity system depends on integrating new technologies that ensure reliability alongside sustainability. However, missing risk markets hinder investment in reliability-enhancing technologies by exposing investors to revenue uncertainty. This study provides the first quantitative assessment of how missing risk markets affect investment decisions in power systems that depend on long-duration energy storage (LDES) for reliability. We develop a two-stage stochastic equilibrium model with risk-averse market participants, which independently sizes power and energy capacity. We apply the method to a case study of a deeply decarbonised power system in Great Britain. The results show that incomplete risk markets reduce social welfare, harm reliability, and discourage investment in LDES and other technologies with volatile revenue streams. Revenue volatility leads to substantial risk premiums and higher financing costs for LDES, creating a barrier to its large-scale deployment. These findings demonstrate the importance of policy mechanisms that hedge revenue risk to lower the cost of capital and accelerate investment in reliability-enhancing, zero-carbon technologies
Authors: Parikshit Pareek, Sidhant Misra, Deepjyoti Deka
The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy. In this work, we present a probabilistic guarantee for power flow learning and voltage risk estimation, derived through the framework of Gaussian Process (GP) regression. Specifically, we establish a bound on the expected estimation error that connects the GP's predictive variance to confidence in voltage risk estimates, ensuring statistical equivalence with Monte Carlo-based ACPF risk quantification. To enhance model learnability in the low-data regime, we first design the Vertex-Degree Kernel (VDK), a topology-aware additive kernel that decomposes voltage-load interactions into local neighborhoods for efficient large-scale learning. Building on this, we introduce a network-swipe active learning (AL) algorithm that adaptively samples informative operating points and provides a principled stopping criterion without requiring out-of-sample validation. Together, these developments mitigate the principal bottleneck of ML-based power flow-its lack of guaranteed reliability-by combining data efficiency with analytical assurance. Empirical evaluations across IEEE 118-, 500-, and 1354-bus systems confirm that the proposed VDK-GP achieves mean absolute voltage errors below 1E-03 p.u., reproduces Monte Carlo-level voltage risk estimates with 15x fewer ACPF computations, and achieves over 120x reduction in evaluation time while conservatively bounding violation probabilities.
Authors: Mohammad Javad Najafirad, Shirantha Welikala
This paper presents a novel dissipativity-based distributed droop-free control approach for voltage regulation and current sharing in DC microgrids (MGs) comprised of an interconnected set of distributed generators (DGs), loads, and power lines. First, we describe the closed-loop DC MG as a networked system where the DGs and lines (i.e., subsystems) are interconnected via a static interconnection matrix. This interconnection matrix demonstrates how the inputs, outputs, and disturbances of DGs and lines are connected in a DC MG. Each DG is equipped with a local controller for voltage regulation and a distributed global controller for current sharing, where the local controllers ensure individual voltage tracking while the global controllers coordinate among DGs to achieve proportional current sharing. To design the distributed global controllers, we use the dissipativity properties of the subsystems and formulate a linear matrix inequality (LMI) problem. To support the feasibility of this problem, we identify a set of necessary local and global conditions to enforce in a specifically developed LMI-based local controller design process. In contrast to existing DC MG control solutions, our approach proposes a unified framework for co-designing the distributed controller and communication topology. As the co-design process is LMI-based, it can be efficiently implemented and evaluated using existing convex optimization tools. The effectiveness of the proposed solution is verified by simulating an islanded DC MG in a MATLAB/Simulink environment under different scenarios, such as load changes and topological constraint changes, and then comparing the performance with the droop control algorithm.
Authors: Paul Bannmüller, Périne Cunat, Ali Rajaei, Jochen Cremer
The ongoing energy transition places significant pressure on the transmission network due to increasing shares of renewables and electrification. To mitigate grid congestion, transmission system operators need decision support tools to suggest remedial actions, such as transmission network reconfigurations or redispatch. However, these tools are prone to model inaccuracies and may not provide relevant suggestions with regard to important unmodeled constraints or operator preferences. We propose a human-in-the-loop modeling-to-generate alternatives (HITL-MGA) approach to address these shortcomings by generating diverse topology reconfiguration alternatives. Case studies on the IEEE 57-bus and IEEE 118-bus systems show the method can leverage expert feedback and improve the quality of the suggested topology reconfigurations.
Authors: Dirk Lauinger, Deepjyoti Deka, Sungho Shin
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market participation could deliver similar savings as peak demand reduction. Under current conditions, market participation does not increase storage investment, but at very low storage costs, could incentivize deployment beyond local distribution needs. This might run contrary to the separation of distribution from generation in deregulated markets. Our framework can identify investment levels appropriate for local distribution needs.
Authors: Efstratios Reppas, Ali Wadi, Brendan Gould, Kyriakos G. Vamvoudakis
In this paper, we develop a framework for deception in quantum games, extending the Honey-X paradigm from classical zero-sum settings into the quantum domain. Building on a view of deception in classical games as manipulation of a player's perception of the payoff matrix, we formalize quantum deception as controlled perturbations of the payoff Hamiltonian subject to a deception budget. We show that when victims are aware of possible deception, their equilibrium strategies surprisingly coincide with those of naive victims who fully trust the deceptive Hamiltonian. This equivalence allows us to cast quantum deception as a bilevel optimization problem, which can be reformulated into a bilinear semidefinite program. To illustrate the framework, we present simulations on quantum versions of the Penny Flip game, demonstrating how quantum strategy spaces and non-classical payoffs can amplify the impact of deception relative to classical formulations.
Authors: Zelin Gan, Henrique Buglia, Romulo Aparecido, Mindaugas Jarmolovičius, Eric Sillekens, Jiaqian Yang, Ronit Sohanpal, Robert I. Killey, Polina Bayvel
We present a novel closed-form model for nonlinear interference (NLI) estimation in low-dispersion O-band transmission systems. The formulation incorporates the four-wave mixing (FWM) efficiency term as well as the coherent contributions of self- and cross-phase modulation (SPM/XPM) across multiple identical spans. This extension enables accurate evaluation of the NLI in scenarios where conventional closed-form Gaussian Noise (GN) models are limited. The proposed model is validated against split-step Fourier method (SSFM) simulations and numerical integration across 41-161 channels, with a 96 GBaud symbol rate, bandwidths of up to 16.1 THz, and transmission distances from 80 to 800 km. Results show a mean absolute error of the NLI signal-to-noise ratio (SNR) below 0.22 dB. The proposed closed-form model offers an efficient and accurate tool for system optimisation in O-band coherent transmission.
Authors: Pål Forr Austnes, Matthieu Jacobs, Lu Wang, Mario Paolone
Since the 1990s, widespread introduction of central (wholesale) electricity markets has been seen across multiple continents, driven by the search for efficient operation of the power grid through competition. The increase of renewables has made significant impacts both on central electricity markets and distribution-level grids as renewable power generation is often connected to the latter. These stochastic renewable technologies have both advantages and disadvantages. On one hand they offer very low marginal cost and carbon emissions, while on the other hand, their output is uncertain, requiring flexible backup power with high marginal cost. Flexibility from end-prosumers or smaller market participants is therefore seen as a key enabler of large-scale integration of renewables. However, current central electricity markets do not directly include uncertainty into the market clearing and do not account for physical constraints of distribution grids. In this paper we propose a local electricity market framework based on probabilistic locational marginal pricing, effectively accounting for uncertainties in production, consumption and grid variables. The model includes a representation of the grid using the lindistflow equations and accounts for the propagation of uncertainty using general Polynomial Chaos (gPC). A two-stage convex model is proposed; in the day-ahead stage, probability distributions of prices are calculated for every timestep, where the expected values represent the day-ahead (spot) prices. In the real-time stage, uncertainties are realized (measured) and a trivial calculation reveals the real-time price. Through four instructive case-studies we highlight the effectiveness of the method to incentivize end-prosumers' participation in the market, while ensuring that their behavior does not have an adverse impact on the operation of the grid.
Authors: Stavros Orfanoudakis, Frans Oliehoek, Peter Palesnky, Pedro P. Vergara
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging often disregard physical grid constraints or have limited performance for complex large-scale tasks, limiting their scalability and real-world applicability. This paper introduces a physics-informed (PI) RL algorithm that integrates a differentiable power flow model and voltage-based reward design into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, enabling EVs to deliver real-time voltage support while meeting user demands. The resulting PI-TD3 algorithm achieves faster convergence, improved sample efficiency, and reliable voltage magnitude regulation under uncertain and overloaded conditions. Benchmarks on the IEEE 34-bus and 123-bus networks show that the proposed PI-TD3 outperforms both model-free RL and optimization-based baselines in grid constraint management, user satisfaction, and economic metrics, even as the system scales to hundreds of EVs. These advances enable robust, scalable, and practical EV charging strategies that enhance grid resilience and support distribution networks operation.
Authors: Mohamed Abdalmoaty, Verena Häberle, Xiuqiang He, Florian Dörfler
We propose a non-parametric frequency-domain method to identify small-signal $dq$-asymmetric grid impedances, over a wide frequency band, using grid-connected converters. Existing identification methods are faced with significant trade-offs: e.g., passive approaches rely on ambient harmonics and rare grid events and thus can only provide estimates at a few frequencies, while many active approaches that intentionally perturb grid operation require long time series measurement and specialized equipment. Although active time-domain methods reduce the measurement time, they either make crude simplifying assumptions or require laborious model order tuning. Our approach effectively addresses these challenges: it does not require specialized excitation signals or hardware and achieves ultrafast ($<1$ s) identification, drastically reducing measurement time. Being non-parametric, our approach also makes no assumptions on the grid structure. A detailed electromagnetic transient simulation is used to validate the method and demonstrate its clear superiority over existing alternatives.
Authors: Honglin Wen, Pierre Pinson
Wind power producers can benefit from forming coalitions to participate cooperatively in electricity markets. To support such collaboration, various profit allocation rules rooted in cooperative game theory have been proposed. However, existing approaches overlook the lack of coherence among producers regarding forecast information, which may lead to ambiguity in offering and allocations. In this paper, we introduce a ``reconcile-then-optimize'' framework for cooperative market offerings. This framework first aligns the individual forecasts into a coherent joint forecast before determining market offers. With such forecasts, we formulate and solve a two-stage stochastic programming problem to derive both the aggregate offer and the corresponding scenario-based dual values for each trading hour. Based on these dual values, we construct a profit allocation rule that is budget-balanced and stable. Finally, we validate the proposed method through empirical case studies, demonstrating its practical effectiveness and theoretical soundness.
Authors: Hadi Nemati, Álvaro Ortega, Pedro Sánchez-Martín, Lukas Sigrist, Luis Rouco, Ignacio Egido
In the transition toward a sustainable power system, renewable-based Virtual Power Plants (RVPPs) have emerged as a promising solution to the challenges of integrating renewable energy sources into electricity markets. Their viability, however, depends on effective market participation strategies and the ability to manage uncertainties while leveraging flexible resources. This paper analyzes the impact of different flexible resources - such as concentrated solar power plants, hydro plants, biomass plants, and flexible demand - on the participation of RVPPs in energy and reserve markets. Multiple sources of uncertainty in generation, consumption, and electricity prices are addressed using a two-stage robust optimization approach. The contribution of different technologies to RVPP profitability is evaluated through a marginal contribution method, ensuring fair allocation of profits among them according to their actual role in energy and reserve provision across markets. Simulations for an RVPP in southern Spain demonstrate how strategic decisions and the availability of flexible resources influence viability, market participation, and unit scheduling.
Authors: Dirk Lauinger, Deepjyoti Deka, Sungho Shin
Electricity distribution companies deploy battery storage to defer grid upgrades by reducing peak demand. In deregulated jurisdictions, such storage often sits idle because regulatory constraints bar participation in electricity markets. Here, we develop an optimization framework that, to our knowledge, provides the first formal model of market participation constraints within storage investment and operation planning. Applying the framework to a Massachusetts case study, we find that market participation could deliver similar savings as peak demand reduction. Under current conditions, market participation does not increase storage investment, but at very low storage costs, could incentivize deployment beyond local distribution needs. This might run contrary to the separation of distribution from generation in deregulated markets. Our framework can identify investment levels appropriate for local distribution needs.
Authors: Noah Rhodes
Data visualization is essential for developing an understanding of a complex system. The power grid is one of the most complex systems in the world and effective power grid research visualization software must 1) be easy to use, 2) support unique data that may arise in research, and 3) be capable of creating custom figures for publication and presentation. However, no current software addresses all three of these needs. PowerPlots is an open-source data visualization tool for power grids that does address these needs. In addition, several tools created to support this software facilitate the analysis of power grid data by transforming the data into graph topology or data-frame data formats that are more compatible for some analyses. In this work, we use PowerPlots to investigate several case studies that involve exploring power grid data. These case studies demonstrate the valuable insights that are possible when using network visualization and how it can be applied to research applications.
Authors: Yuechen Liu, Boqi Meng
Fiber optic current sensors (FOCS) are widely adopted in modern power grids due to high sensitivity, excellent insulation, and strong immunity to electromagnetic interference. This prominence necessitates precise investigation into their error sources and corresponding optimization. This study examines reflective FOCS based on the Faraday effect. A theoretical model is established to simulate phase error caused by linear birefringence from the quarter-wave plate. Conventional methods using circular birefringence are analyzed, revealing inherent limitations. Innovatively, a compensation strategy employing high-order quarter-wave plates is proposed to effectively eliminate linear birefringence effects. This approach significantly enhances the accuracy and practicality of FOCS in precision metrology.
Authors: Jialin Zheng, Zhong Liu, Xiaonan Lu
Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To solve this, this letter proposes a latent-feature-informed neural ordinary differential equation (LFI-NODE) modeling method that can achieve lightweight stability evaluation directly from trajectory data.} LFI-NODE parameterizes the entire system ODE with a single continuous-time neural network, allowing each new sample to refine a unified global model. It faithfully captures nonlinear large-signal dynamics to preserve uniform predictive accuracy as the inverter transitions between operating points. Meanwhile, latent perturbation features distilled from every trajectory steer the learning process and concurrently reveal the small-signal eigenstructure essential for rigorous stability analysis. Validated on a grid-forming inverter, {The LFI-NODE requires one to two orders of magnitude fewer training samples compared with traditional methods, collected from short time-domain trajectories instead of extensive frequency-domain measurements.} {Furthermore, the LFI-NODE requires only 48 short transients to achieve a trajectory prediction error at the hundredth level and an eigenvalue estimation error at the tenth level, outperforming benchmark methods by one to two orders of magnitude.} This makes LFI-NODE a practical and lightweight approach for achieving high-fidelity stability assessment of complex black-box power-electronic systems.
Authors: Carsten Hartmann, Edoardo De Din, Daniele Carta, Florian Middelkoop, Arndt Neubauer, Johannes Kruse, Ulrich Oberhofer, Richard Jumar, Benjamin Schäfer, Thiemo Pesch, Andrea Benigni, Dirk Witthaut
Electric power systems are undergoing fundamental change. The shift to inverter-based generation challenges frequency stability, while growing digitalisation heightens vulnerability to errors and attacks. Here we identify an emerging risk at the intersection of cyber-physical coupling and control system design. We show that grid frequency time series worldwide exhibit a persistent one-minute oscillatory pattern, whose origin has remained largely unexplained. We trace this pattern back to the energy management systems of battery electric storage systems and demonstrate that the pattern amplitude has increased substantially in the Nordic and British grids. We argue that this effect is a potential burden for stability in future grids with low inertia and an increasing penetration with batteries and smart devices, though it can be mitigated by a revision of battery control algorithms.
Authors: Luiz Fernando M. Arruda, Moises Ferber, Diego Greff
This article presents a study on the application of artificial neural networks (ANNs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems using low-cost pyranometer sensors. The proposed approach integrates pyranometers, temperature sensors, and an ANN to estimate the duty cycle of a DC/DC converter, enabling the system to consistently operate at its maximum power point. The strategy was implemented in the local control of a Cuk converter and experimentally validated against the conventional Perturb and Observe (P&O) method. Results demonstrate that the ANN-based technique, leveraging affordable sensor technology, achieves accurate MPPT performance with reduced fluctuations, enhancing the responsiveness and efficiency of PV tracking systems.
Authors: Rafael R. Yumul, Enalyn T. Domingo
The study is anchored to the principles of Nearly-Zero Energy Building (NZEB). It aimed to transform the Tarlac State University Gymnasium into a facility with energy-efficient equipment to contribute to reducing carbon footprints by integrating a solar PV system as its renewable energy source. The researchers found out that the electrical infrastructure of the Gym was outdated, and the lighting was not energy efficient, and there were too few convenience or power outlets. There was also insufficient cooling equipment to maintain a comfortable temperature. Analysis shows that the payback period is within the average range, making it a cost-effective investment for the University. Aside from the cost of the PV System, adherence to engineering design standards will mean additional costs to replace the metal halides with LED high bay lamps, installation of additional air conditioning units, and provision of additional convenience outlets. These additional costs should be considered when evaluating the feasibility of the project. It is recommended that the integrity of the existing roof system of the Gymnasium be considered. The total cost of putting up the whole electrical system, including new lighting, cooling, and convenience loads, must be calculated to determine the total cost of implementing the whole NZEB project. Other factors in the economic evaluation may be considered to determine a more stringent result.
Authors: Mohammad Hassan, Mads R. Almassalkhi
Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibility by sending a request to the DER coordinator to turn-ON or turn-OFF. Much work has focused on modeling and analyzing aggregations of DERs under PEM with fixed packet durations and only turn-ON requests. Different recent efforts to enable variable packet lengths have shown an increase in available flexibility and ramping capability, but have not been modeled in aggregate, which limits systematic analyses. To address this issue, this paper presents a new aggregate bin-based (macro) model of PEM loads that incorporates both turn-ON and turn-OFF request features, enabling the model to accurately characterize the capability of the fleet of DERs to track a power reference signal, population temperature dynamics, aggregate request rates, and variable packet lengths. Simulation-based validation is performed against an agent-based (micro) model to evaluate robustness and quantify model accuracy. Finally, the distribution of variable packet lengths from macro-model simulations are applied to inform past work on PEM with randomized packet lengths
Authors: Dirk Lauinger, Luc Coté, Andy Sun
Electricity storage is used for intertemporal price arbitrage and for ancillary services that balance unforeseen supply and demand fluctuations via frequency regulation. We present an optimization model that computes bids for both arbitrage and frequency regulation and ensures that storage operators can honor their market commitments at all times for all fluctuation signals in an uncertainty set inspired by market rules. This requirement, initially expressed by an infinite number of nonconvex functional constraints, is shown to be equivalent to a finite number of deterministic constraints. The resulting formulation is a mixed-integer bilinear program that admits mixed-integer linear relaxations and restrictions. Empirical tests on European electricity markets show a negligible optimality gap between the relaxation and the restriction. The model can account for intraday trading and, with a solution time of under 5 seconds, may serve as a building block for more complex trading strategies. Such strategies become necessary as battery capacity exceeds the demand for ancillary services. In a backtest from 1 July 2020 through 30 June 2024 joint market participation more than doubles profits and almost halves energy storage output compared to arbitrage alone.
Authors: Jingbo Wang, Harshal D. Kaushik, Jie Zhang
The adoption of electric vehicles (EVs) represents a critical shift in personal mobility, fueled by policy support and advancements in automotive technology. However, the expansion of EVs for long-distance travel is hindered by charging time concerns, the sparse distribution of charging stations, and the worsening waiting times due to congestion. The main objective of this work is two-fold: 1) first, to comprehensively analyze the existing public charging station robustness and effectively strategize for the new ones, and 2) secondly, to select the optimal chargers for long-distance journeys, by estimating the waiting time from current traffic congestion. This is achieved by accompanying effective EV charging strategies, pinpointing on the congestion points from the existing traffic, and the robustness of the current charging station infrastructure. Utilizing a real-time transportation and charging station dataset in Texas, we identify optimal charger placement strategies to minimize travel time by examining the congestion and charging time trade-offs. Our findings suggest that maximizing the constant current phase during charging enhances efficiency, crucial for long-distance travel. On the contrary, we also explore the negative impact of congestion on travel times and we conclude that sometimes it might be beneficial to exceed the constant current phase to avoid the congested charging stations.
Authors: Ahmed Adel Attia, Jing Liu, Carol Espy Wilson
Prior works have investigated the use of articulatory features as complementary representations for automatic speech recognition (ASR), but their use was largely confined to shallow acoustic models. In this work, we revisit articulatory information in the era of deep learning and propose a framework that leverages articulatory representations both as an auxiliary task and as a pseudo-input to the recognition model. Specifically, we employ speech inversion as an auxiliary prediction task, and the predicted articulatory features are injected into the model as a query stream in a cross-attention module with acoustic embeddings as keys and values. Experiments on LibriSpeech demonstrate that our approach yields consistent improvements over strong transformer-based baselines, particularly under low-resource conditions. These findings suggest that articulatory features, once sidelined in ASR research, can provide meaningful benefits when reintroduced with modern architectures.
Authors: Vishakha Lall, Yisi Liu
Detecting psychological stress from speech is critical in high-pressure settings. While prior work has leveraged acoustic features for stress detection, most treat stress as a static label. In this work, we model stress as a temporally evolving phenomenon influenced by historical emotional state. We propose a dynamic labelling strategy that derives fine-grained stress annotations from emotional labels and introduce cross-attention-based sequential models, a Unidirectional LSTM and a Transformer Encoder, to capture temporal stress progression. Our approach achieves notable accuracy gains on MuSE (+5%) and StressID (+18%) over existing baselines, and generalises well to a custom real-world dataset. These results highlight the value of modelling stress as a dynamic construct in speech.
Authors: Mojtaba Amiri, Rouhollah Amiri
This letter proposes an algebraic solution for the problem of 3-D source localization utilizing the minimum number of measurements, i.e., one Time Difference of Arrival (TDOA) and one Angle of Arrival (AOA) pair. The proposed method employs a closed-form weighted least squares estimator and enables the positioning using a single ground station and a cooperative UAV relaying the signal. Analytical derivations and simulation results demonstrate effectiveness of the proposed approach, achieving near-optimal performance aligned with the Cramér-Rao Lower Bound (CRLB) under moderate Gaussian noise conditions.
Authors: Shizhen Jia, Mingjun Ying, Marco Mezzavilla, Doru Calin, Theodore S. Rappaport, Sundeep Rangan
The upper mid-band FR3 spectrum (7-24 GHz) has garnered significant interest for future cellular services. However, utilizing a large portion of this band requires careful interference coordination with incumbent satellite systems. This paper investigates interference from high-power terrestrial base stations (TN-BSs) to satellite downlink receivers. A central challenge is that the victim receivers, i.e., ground-based non-terrestrial user equipment (NTN-UEs) such as satellite customer premises equipment, must first be detected and their channels estimated before the TN-BS can effectively place nulls in their directions. We explore a potential solution where NTN-UEs periodically transmit preambles or beacon signals that TN-BSs can use for detection and channel estimation. The performance of this nulling approach is analyzed in a simplified scenario with a single victim, revealing the interplay between path loss and estimation quality in determining nulling performance. To further validate the method, we conduct a detailed multi-user site-specific ray-tracing (RT) simulation in a rural environment. The results show that the proposed nulling approach is effective under realistic parameters, even with high densities of victim units, although TN-BS may require a substantial number of antennas.
Authors: Omer Gokalp Serbetci, Lei Chu, Andreas F. Molisch
Cognitive radio (CR) is an important technique for improving spectral efficiency, letting a secondary system operate in a wireless spectrum when the primary system does not make use of it. While it has been widely explored over the past 25 years, many common assumptions are not aligned with the realities of 5G networks. In this paper, we consider the CR problem for the following setup: (i) infrastructure-based systems, where downlink transmissions might occur to receivers whose positions are not, or not exactly, known; (ii) multi-beam antennas at both primary and secondary base stations. We formulate a detailed protocol to determine when secondary transmissions into different beam directions can interfere with primary users at potential locations and create probability-based interference rules. We then analyze the "catastrophic interference" probability and the "missed transmission opportunity" probability, as well as the achievable throughput, as a function of the transmit powers of the primary and secondary base stations and the sensing window of the secondary base station. Results can serve to more realistically assess the spectral efficiency gains in 5G infrastructure-based cognitive systems.
Authors: Mehmet Fatih Ozkan, Dennis Kibalama, Jacob Paugh, Marcello Canova, Stephanie Stockar
Connected and Automated Vehicles (CAVs) offer significant potential for improving energy efficiency and lowering vehicle emissions through eco-driving technologies. Control algorithms in CAVs leverage look-ahead route information and Vehicle-to-Everything (V2X) communication to optimize vehicle performance. However, existing eco-driving strategies often neglect macroscopic traffic effects, such as upstream traffic jams, that occur outside the optimization horizon but significantly impact vehicle energy efficiency. This work presents a novel Neural Network (NN)-based methodology to approximate the terminal cost within a model predictive control (MPC) problem framework, explicitly incorporating upstream traffic dynamics. By incorporating traffic jams into the optimization process, the proposed traffic-aware approach yields more energy-efficient speed trajectories compared to traffic-agnostic methods, with minimal impact on travel time. The framework is scalable for real-time implementation while effectively addressing uncertainties from dynamic traffic conditions and macroscopic traffic events.
Authors: Siddarth Marwaha, Pawel Kryszkiewicz, Eduard A. Jorswieck
Although multiple works have proposed energy-efficient resource allocation schemes for Massive Multiple-Input Multiple-Output (M-MIMO) system, most approaches overlook the potential of optimizing Power Amplifier (PA) transmission power while accounting for non-linear distortion effects. Furthermore, most M-MIMO studies assume narrow-band transmission, neglecting subcarrier intermodulations at the non-linear PA for an Orthogonal Frequency Division Multiplexing (OFDM) system. Therefore, this work investigates the energy-efficient power allocation for a single-user equipment (UE) M-MIMO downlink (DL) system employing OFDM with nonlinear PAs. Unlike prior works, we model wide-band transmission using a soft-limiter PA model and derive a closed-form expression for the signal-to-distortion-and-noise ratio (SNDR) under Rayleigh fading and Maximal Ratio Transmission (MRT) precoding. Next, the Energy Efficiency (EE) function is defined considering two PA architectures and a distorted OFDM signal. We then propose a low complexity root-finding algorithm to maximize EE by transmit power adjustment. Simulation results demonstrate significant EE gains over a fixed PA back-off baseline, with over $100\%$ improvement under both low and high path loss. Our findings reveal how the optimal operating point depends on the antenna count, the PA model, and the propagation conditions.
Authors: Siddarth Marwaha, Pawel Kryszkiewicz, Eduard Jorswieck
Maintaining high energy efficiency (EE) in wireless networks is crucial, particularly with the adoption of massive MIMO technology. This work introduces a resource allocation framework that jointly optimizes transmit power assigned to each user and the number of active antennas, while explicitly accounting for a nonlinear Power Amplifier (PA). We consider a downlink MU-MIMO-OFDM transmission with zero forcing (ZF) precoding, Rayleigh fading channels, and soft-limiter PAs, with both ideal and realistic PA architectures. In contrast to existing formulations, our optimization framework avoids imposing an explicit transmit power constraint, since the nonlinear distortion inherently limits the feasible operating region. To solve the resulting non-convex problem, an alternating optimization approach is adopted that, by exploiting properties of the EE function, guarantees convergence to a stationary point. Extensive simulations demonstrate consistent performance gains over distortion-neglecting and power-only optimized baselines. In a scenario of a 5 km radius cell serving 60 randomly distributed users, the median EE gains over the distortion-neglecting allocation reach 40% for ideal PAs and 20% for Class B PAs, confirming high impact of the proposed solution.
Authors: Marwan Soliman, Pauline Kergus, Diego Regruto, Luiz Villa, Zohra Kader
The fundamental role of power converters is to efficiently manage and control the flow of electrical energy, ensuring compatibility between power sources and loads. All these applications of power converters need the design of an appropriate control law. Control of power converters is a challenging problem due to the presence of switching devices which are difficult to handle using traditional control approaches. The objective of this paper is to investigate the use of data-driven techniques, in particular the Virtual References Feedback Tuning (VRFT) method, in the context of power converters feedback control. This study considers a buck \pauline{mode} power converter circuit provided by the OwnTech foundation.
Authors: Yeomoon Kim, Minsoo Kim, Jip Kim
Ensuring both feasibility and efficiency in optimal power flow (OPF) operations has become increasingly important in modern power systems with high penetrations of renewable energy and energy storage. While deep neural networks (DNNs) have emerged as promising fast surrogates for OPF solvers, they often fail to satisfy critical operational constraints, especially those involving inter-temporal coupling, such as generator ramping limits and energy storage operations. To deal with these issues, we propose a Multi-Period Projection-Aware Deep Neural Network (MPA-DNN) that incorporates a projection layer for multi-period dispatch into the network. By doing so, our model enforces physical feasibility through the projection, enabling end-to-end learning of constraint-compliant dispatch trajectories without relying on labeled data. Experimental results demonstrate that the proposed method achieves near-optimal performance while strictly satisfying all constraints in varying load conditions.
Authors: Han Hu, Wenjie Wan, Feiyu Chen, Xiaoyu Liu, Bo Yu, Kequan Zhao
With the increasing complexity of power systems,accurately identifying critical states (the states corresponding to minimal cut sets) and assessing system reliability have become crucial tasks. In this paper, a mathematical lattice structure is employed to represent and partition the state space of power system. Based on this structure, a novel recursive method is proposed to efffciently identify critical states by leveraging lattice partitioning and Optimal Power Flow(OPF) calculations. This method not only enables the extension of failure system states,but also calculates the upper and lower bounds of the Loss of Load Probability (LOLP) in a progressively converging manner. Compared to traditional reliability assessment methods such as State Enumeration (SE) and Monte Carlo Simulation (MCS), this approach offers greater accuracy and efffciency. Experiments conducted on the RBTS and RTS79 systems demonstrate that the proposed method accurately identiffes all critical states up to a preset order, which are high-risk states. The contribution of these critical states to LOLP highlights their signiffcance in the system. Moreover, the proposed method achieves the analytical value with signiffcantly fewer OPF calculations in RBTS system, reaching acceptable precision of LOLP up to 100 times faster than SE in both the RBTS and RTS systems.
Authors: Siphiwe Shandu, Thabiso Moropa, Alain R. Ndjiongue
This letter investigates contactless power line communications (CPLC) for underground mining by modeling power wires as long-wire antennas. A system-level framework is developed, comprising a cascade of RF and power line channels. The model accounts for multipath propagation, frequency-dependent attenuation, and Rician fading. Simulations from 1-20 GHz reveal that the length of the wire significantly affects radiation, directivity, and input impedance. The findings show that CPLC transmits electromagnetic waves without direct electrical contact, offering a robust, cost-effective solution that enhances mobility, reduces maintenance, and ensures compatibility with existing mining power infrastructure.
Authors: Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi
Deep learning models have gained increasing prominence in recent years in the field of solar pho-tovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems across the world. This paper proposes SolNet: a novel, general-purpose, multivariate solar power forecaster, which addresses these challenges by using a two-step forecasting pipeline which incorporates transfer learning from abundant synthetic data generated from PVGIS, before fine-tuning on observational data. Using actual production data from hundreds of sites in the Netherlands, Australia and Belgium, we show that SolNet improves forecasting performance over data-scarce settings as well as baseline models. We find transfer learning benefits to be the strongest when only limited observational data is available. At the same time we provide several guidelines and considerations for transfer learning practitioners, as our results show that weather data, seasonal patterns, amount of synthetic data and possible mis-specification in source location, can have a major impact on the results. The SolNet models created in this way are applicable for any land-based solar photovoltaic system across the planet where simulated and observed data can be combined to obtain improved forecasting capabilities.
Authors: Jihun Lim, Sungwon Lee
To facilitate the widespread adoption of renewable energy, dispatchable, zero-emission power sources are essential for grid stability. This work performs a comprehensive techno-economic analysis of a self-sustainable thermophotovoltaic (TPV) system, an architecture that integrates solar charging to function as a standalone power generation asset. Using theory-based models for conventional air-bridge InGaAs and Si diode cells, our analysis reveals that while the system is not currently competitive from a pure levelized of storage cost (LCOS) perspective due to the high capital expenditure for thermal battery materials, its primary value lies in its competitive levelized cost of electricity (LCOE), which is comparable to that of conventional dispatchable generators such as gas turbines. Furthermore, we show that a full Si-based TPV system, utilizing a 50-{\mu}m-thick air-bridge cell for enhanced photon utilization, can also achieve an LCOE that is competitive with such conventional power sources at scales exceeding the gigawatt-hour level, despite its lower conversion efficiency relative to its InGaAs counterpart. This highlights a practical engineering pathway for leveraging the immense manufacturing scalability of Si, offering a lower-risk route to deployment compared to III-V materials. Ultimately, this work establishes the self-sustainable TPV architecture as a compelling pathway toward providing grid-scale, on-demand, zero-emission power.
Authors: Josh A. Taylor, Alejandro D. Domínguez-García
Distance relays detect faults on transmission lines. They face uncertainty from the fault's location and resistance, as well as the current from the line's remote terminal. In this paper, we aggregate this uncertainty with the Minkowski sum. This allows us to explicitly model the power grid surrounding the relay's line, and in turn accommodate any mix of synchronous machines and inverter-based resources. To make the relay's task easier, inverters can inject perturbations, or auxiliary signals, such as negative-sequence current. We use Farkas' lemma to construct an optimization for designing inverter auxiliary signals.
Authors: Ziqi Ling, Minghui Liwang, Xianbin Wang, Seyyedali Hosseinalipour, Zhipeng Cheng, Sai Zou, Wei Ni, Xiaoyu Xia
Timely resource allocation in edge-assisted vehicular networks is essential for compute-intensive services such as autonomous driving and navigation. However, vehicle mobility leads to spatio-temporal unpredictability of resource demands, while real-time double auctions incur significant latency. To address these challenges, we propose a look-ahead contract-based auction framework that shifts decision-making from runtime to planning time. Our approach establishes N-step service contracts between edge servers (ESs) using demand forecasts and modified double auctions. The system operates in two stages: first, an LSTM-based prediction module forecasts multi-slot resource needs and determines ES roles (buyer or seller), after which a pre-double auction generates contracts specifying resource quantities, prices, and penalties. Second, these contracts are enforced in real time without rerunning auctions. The framework incorporates energy costs, transmission overhead, and contract breach risks into utility models, ensuring truthful, rational, and energy-efficient trading. Experiments on real-world (UTD19) and synthetic traces demonstrate that our method improves time efficiency, energy use, and social welfare compared with existing baselines.
Authors: Sebastiano Randino, Lorenzo Schena, Nicolas Coudou, Emanuele Garone, Miguel Alfonso Mendez
This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of tip-speed ratio and upstream conditions. These surrogate models are embedded in a first-order dynamic system suitable for model-based control. Experimental validation is carried out in two wind tunnel configurations: a low-turbulence tandem setup and a high-turbulence wind farm scenario. In the tandem case, the identified model is integrated into an adapted K\omega^2 controller, resulting in improved tip-speed ratio tracking and power stability compared to BEM-based and steady-state models. In the wind farm scenario, the model captures the statistical behavior of the turbines despite unresolved turbulence. The proposed method enables interpretable, adaptive control across a range of operating conditions without relying on black-box learning strategies.
Authors: Jihun Lim, Sungwon Lee
To facilitate the widespread adoption of renewable energy, dispatchable, zero-emission power sources are essential for grid stability. This work performs a comprehensive techno-economic analysis of a self-sustainable thermophotovoltaic (TPV) system, an architecture that integrates solar charging to function as a standalone power generation asset. Using theory-based models for air-bridge InGaAs and Si diode cells, our analysis reveals that while the system is not currently competitive from a pure levelized of storage cost (LCOS) perspective due to the high capital expenditure for thermal battery materials, its primary value lies in its competitive levelized cost of electricity (LCOE). The results demonstrate that the LCOE of this self-sustaining system can be competitive with conventional dispatchable generators, such as gas turbines. Furthermore, at scales exceeding the gigawatt-hour level, a Si-based system can also achieve an LCOE comparable to that of traditional gas-turbine power plants, despite having a lower conversion efficiency than its InGaAs counterpart. This highlights a practical engineering pathway for leveraging silicon's immense manufacturing scalability, offering a lower-risk route to deployment compared to III-V materials. Ultimately, this work establishes the self-sustainable TPV architecture as a compelling pathway toward providing grid-scale, on-demand, zero-emission power.
Authors: Xingtao Yang, Zhenghe Guo, Siyun Liang, Zhaohui Yang, Chen Zhu, Zhaoyang Zhang
This letter investigates the joint sensing problem between unmanned aerial vehicles (UAV) and base stations (BS) in integrated sensing and communication (ISAC) systems with fluid antennas (FA). In this system, the BS enhances its sensing performance through the UAV's perception system. We aim to maximize the communication rate between the BS and UAV while guaranteeing the joint system's sensing capability. By establishing a communication-sensing model with convex optimization properties, we decompose the problem and apply convex optimization to progressively solve key variables. An iterative algorithm employing an alternating optimization approach is subsequently developed to determine the optimal solution, significantly reducing the solution complexity. Simulation results validate the algorithm's effectiveness in balancing system performance.
Authors: Maurizio Vassallo, Adrien Bolland, Alireza Bahmanyar, Louis Wehenkel, Laurine Duchesne, Dong Liu, Sania Khaskheli, Alexis Ha Thuc, Pedro P. Vergara, Amjad Anvari-Moghaddam, Simon Gerard, Damien Ernst
Distributed energy resources (DERs) are transforming power networks, challenging traditional operational methods, and requiring new coordination mechanisms. To address this challenge, this paper introduces SecuLEx (Secure Limit Exchange), a new market-based paradigm to allocate power injection and withdrawal limits that guarantee network security during time periods, called dynamic operating envelopes (DOEs). Under this paradigm, distribution system operators (DSOs) assign initial DOEs to customers. These limits can be exchanged afterward through a market, allowing customers to reallocate them according to their needs while ensuring network operational constraints. We formalize SecuLEx and illustrate DOE allocation and market exchanges on a small-scale low-voltage (LV) network, demonstrating that both procedures are computationally tractable. In this example, SecuLEx reduces renewable curtailment and improves grid utilization and social welfare compared to traditional approaches.
Authors: Wenlong Shi, Hongyi Li, Zhaoyu Wang
Underground power distribution systems (PDSs) are increasingly deployed in urban areas. The integration of smart devices including smart switchgears, pad-mounted distribution transformers and inverter-based resources (IBRs) enhance system resilience, however simultaneously introducing unique challenges. The challenges include inrush currents caused by trapped charges in underground cables, ferroresonance in distribution transformers during energization, and three-phase load imbalance resulting from single-phase underground laterals. To address these issues, this paper proposes an underground PDS restoration framework using IBRs. Firstly, an underground cable energization model is developed to quantify inrush current by analyzing voltage differences across both switchgear terminals. Secondly, a distribution transformer energization model is proposed to evaluate ferroresonance using Q-factor constraints based on underground cable capacitance and damping resistance. Thirdly, a phase-swapping model is proposed to improve load balancing by dynamically reassigning lateral-phase connections through smart switchgears. The proposed models are further integrated into a mixed-integer nonlinear programming (MINLP) formulation to maximize the total weighted restored load while constraining inrush currents, ferroresonance, and phase imbalance. To address the nonlinearity induced by impedance matrix reordering during phase swapping, a permutation-based linearization technique is proposed. Finally, case studies on an underground PDS established based on IEEE 123-Node Test Feeder validate the effectiveness of the proposed strategy in improving uderground PDS restoration performance.
Authors: Wenlong Shi, Dingwei Wang, Liming Liu, Zhaoyu Wang
Electrification and decarbonization are transforming power system demand and recovery dynamics, yet their implications for post-outage load surges remain poorly understood. Here we analyze a metropolitan-scale heterogeneous dataset for Indianapolis comprising 30,046 feeder-level outages between 2020 and 2024, linked to smart meters and submetering, to quantify the causal impact of electric vehicles (EVs), heat pumps (HPs) and distributed energy resources (DERs) on restoration surges. Statistical analysis and causal forest inference demonstrate that rising penetrations of all three assets significantly increase surge ratios, with effects strongly modulated by restoration timing, outage duration and weather conditions. We develop a component-aware multi-task Transformer estimator that disaggregates EV, HP and DER contributions, and apply it to project historical outages under counterfactual 2035 adoption pathways. In a policy-aligned pathway, evening restorations emerge as the binding reliability constraint, with exceedance probabilities of 0.057 when 30\% of system load is restored within the first 15 minutes. Mitigation measures, probabilistic EV restarts, short thermostat offsets and accelerated DER reconnection, reduce exceedance to 0.019 and eliminate it entirely when 20\% or less of system load is restored. These results demonstrate that transition-era surges are asset-driven and causally linked to electrification and decarbonization, but can be effectively managed through integrated operational strategies.
Authors: Shobhit Singhal, Marta Fochesato, Liviu Aolaritei, Florian Dörfler
Wind power producers (WPPs) participating in short-term power markets face significant imbalance costs due to their non-dispatchable and variable production. While some WPPs have a large enough market share to influence prices with their bidding decisions, existing optimal bidding methods rarely account for this aspect. Price-maker approaches typically model bidding as a bilevel optimization problem, but these methods require complex market models, estimating other participants' actions, and are computationally demanding. To address these challenges, we propose an online learning algorithm that leverages contextual information to optimize WPP bids in the price-maker setting. We formulate the strategic bidding problem as a contextual multi-armed bandit, ensuring provable regret minimization. The algorithm's performance is evaluated against various benchmark strategies using a numerical simulation of the German day-ahead and real-time markets.
Authors: Hui Wei, Dong Yoon Lee, Shubham Rohal, Zhizhang Hu, Ryan Rossi, Shiwei Fang, Shijia Pan
Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.
Authors: Vaibhav Srivastav, Steven Zheng, Eric Bezzam, Eustache Le Bihan, Nithin Koluguri, Piotr Żelasko, Somshubra Majumdar, Adel Moumen, Sanchit Gandhi
Despite rapid progress, ASR evaluation remains saturated with short-form English, and efficiency is rarely reported. We present the Open ASR Leaderboard, a fully reproducible benchmark and interactive leaderboard comparing 60+ open-source and proprietary systems across 11 datasets, including dedicated multilingual and long-form tracks. We standardize text normalization and report both word error rate (WER) and inverse real-time factor (RTFx), enabling fair accuracy-efficiency comparisons. For English transcription, Conformer encoders paired with LLM decoders achieve the best average WER but are slower, while CTC and TDT decoders deliver much better RTFx, making them attractive for long-form and offline use. Whisper-derived encoders fine-tuned for English improve accuracy but often trade off multilingual coverage. All code and dataset loaders are open-sourced to support transparent, extensible evaluation.
Authors: Nishant Gadde, Yoshua Alexander, Sarvesh Parthasarthy, Arman Allidina
Load forecasting has always been a challenge for grid operators due to the growing complexity of power systems. The increase in extreme weather and the need for energy from customers has led to load forecasting sometimes failing. This research presents a Support Vector Regression (SVR) framework for electric load forecasting that outperforms the industry standard. The SVR model demonstrates better accuracy across all evaluation metrics that are important for power system operations. The model has a 54.2\% reduction in Mean Squared Error (31.91 vs. 69.63), a 33.5\% improvement in Mean Absolute Error, and performance benefits across other metrics. These improvements show significant benefits when integrated with power forecasting tools and show that the approach provides an additional tool for accuracy checking for system planning and resource allocation in times of need for resource allocation in electric power systems.
Authors: Eren Berk Kama, Murat Babek Salman, Isaac Skog, Emil Björnson
This paper introduces a sensing management method for integrated sensing and communications (ISAC) in cell-free massive multiple-input multiple-output (MIMO) systems. Conventional communication systems employ channel estimation procedures that impose significant overhead during data transmission, consuming resources that could otherwise be utilized for data. To address this challenge, we propose a state-based approach that leverages sensing capabilities to track the user when there is no communication request. Upon receiving a communication request, predictive beamforming is employed based on the tracked user position, thereby reducing the need for channel estimation. Our framework incorporates an extended Kalman filter (EKF) based tracking algorithm with adaptive sensing management to perform sensing operations only when necessary to maintain high tracking accuracy. The simulation results demonstrate that our proposed sensing management approach provides uniform downlink communication rates that are higher than with existing methods by achieving overhead-free predictive beamforming.
Authors: He Huang, Zilong Liu, Zeping Sui, Wei Huang, Md. Noor-A-Rahim, Haishi Wang, Zhiheng Hu
This paper introduces a novel cooperative vehicular communication algorithm tailored for future 6G ultra-massive vehicle-to-everything (V2X) networks leveraging integrated space-air-ground communication systems. Specifically, we address the challenge of real-time information exchange among rapidly moving vehicles. We demonstrate the existence of an upper bound on channel capacity given a fixed number of relays, and propose a low-complexity relay selection heuristic algorithm. Simulation results verify that our proposed algorithm achieves superior channel capacities compared to existing cooperative vehicular communication approaches.
Authors: Yinong Chen, Wenchi Cheng, Jingqing Wang, Xiao Zheng, Jiangzhou Wang
In emergency wireless communications (EWC) scenarios, ensuring reliable, flexible, and high-rate transmission while simultaneously maintaining seamless coverage and rapid response capabilities presents a critical technical challenge. To this end, satellite-aerial-ground integrated network (SAGIN) has emerged as a promising solution due to its comprehensive three-dimensional coverage and capability to meet stringent, multi-faceted quality-of-service (QoS) requirements. Nevertheless, most existing studies either neglected the inherent characteristics of the complex channel conditions due to the terrain changes or analyzed the performance in the absence of QoS constraints, resulting in a mismatch between theoretical analysis and practical performance. To remedy such deficiencies, in this paper we establish a performance modeling framework for SAGIN employing the Fisher-Snedecor $\mathcal{F}$ composite fading model to characterize the air-ground link. In specific, the proposed $\mathcal{F}$ composite fading channel is adopted to accurately describe both multipath fading and shadowing in harsh ground environments. The exact distribution of end-to-end signal-to-noise (SNR) statistics for space-air and air-ground links is developed, enabling theoretical analysis of cascaded channels with fixed-gain amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols, respectively. Furthermore, asymptotic expressions of the derived results are provided to offer concise representations and demonstrate close alignment with theoretical predictions in the high-SNR regime. Finally, the insightful closed-form and asymptotic expressions of effective capacity with QoS provisioning, outage probability, and $\epsilon$-outage capacity are investigated, respectively, followed by both field measurements and Monte Carlo simulations to verify the effectiveness.
Authors: Mahmoud A. Shawky, Syed Tariq Shah, Ahmed G. Abdellatif, Muhammad Ali Imran, Qammer H. Abbasi, Shuja Ansari, Ahmad Taha
Intelligent transportation systems increasingly depend on wireless communication for broadcasting traffic messages and facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS) due to obstructions. This paper proposes a lightweight cross-layer authentication scheme that employs public-key infrastructure (PKI)-based authentication for initial legitimacy detection/handshaking while using key-based physical-layer re-authentication for message verification. This approach reduces signature generation and signaling overheads associated with each transmission, thereby enhancing network scalability. However, the receiver operating characteristic (ROC; Pd: detection vs. PFA: false alarm probabilities) of the latter decreases with lower signal-to-noise ratio (SNR). To address this, we investigate the use of reconfigurable intelligent surfaces (RISs) to strengthen the SNR directed toward the designated vehicle in shadowed areas (i.e., NLoS scenarios), thereby improving the ROC. Theoretical analysis and practical implementation are conducted using a 1-bit RIS consisting of 64 x 64 reflective meta-surfaces. Experimental results show a significant improvement in Pd, increasing from 0.82 to 0.96 at SNR = -6 dB for an orthogonal frequency-division multiplexing (OFDM) system with 128 subcarriers. We also conducted informal and formal security analyses using Burrows-Abadi-Needham (BAN) logic to prove the scheme's ability to resist passive and active attacks.
Authors: Muhammad Irfan Khan, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi
Federated Learning (FL) is a distributed machine learning approach that safeguards privacy by creating an impartial global model while respecting the privacy of individual client data. However, the conventional FL method can introduce security risks when dealing with diverse client data, potentially compromising privacy and data integrity. To address these challenges, we present a differential privacy (DP) federated deep learning framework in medical image segmentation. In this paper, we extend our similarity weight aggregation (SimAgg) method to DP-SimAgg algorithm, a differentially private similarity-weighted aggregation algorithm for brain tumor segmentation in multi-modal magnetic resonance imaging (MRI). Our DP-SimAgg method not only enhances model segmentation capabilities but also provides an additional layer of privacy preservation. Extensive benchmarking and evaluation of our framework, with computational performance as a key consideration, demonstrate that DP-SimAgg enables accurate and robust brain tumor segmentation while minimizing communication costs during model training. This advancement is crucial for preserving the privacy of medical image data and safeguarding sensitive information. In conclusion, adding a differential privacy layer in the global weight aggregation phase of the federated brain tumor segmentation provides a promising solution to privacy concerns without compromising segmentation model efficacy. By leveraging DP, we ensure the protection of client data against adversarial attacks and malicious participants.
Authors: Peng Wang, Luis Badesa
Synchronous Generators (SGs) currently provide important levels of Short-Circuit Current (SCC), a critical ancillary service that ensures line protections trip during short-circuit faults. Given the ongoing replacement of SGs by power-electronics-based generation, which have a hard limit for current injection, it has become relevant to optimize the procurement of SCC provided by remaining SGs. Pricing this service is however challenging due to the integrality constraints in Unit Commitment (UC). Existing methods, e.g., dispatchable pricing, restricted pricing and marginal unit pricing, attempt to address this issue but exhibit limitations in handling binary variables, resulting in SCC prices that either fail to cover the operating costs of units or lack interpretability. To overcome these pitfalls, we propose a primal-dual formulation of the SCC-constrained dispatch that preserves the binary nature of UC while effectively computing shadow prices of SCC services. Using a modified IEEE 30-bus system, a comparison is carried out between the proposed approach and the state-of-the-art pricing schemes, highlighting the advantages of the primal-dual method in preserving UC integrality for SCC pricing.
Authors: Kyung-Bin Kwon, Sayak Mukherjee, Veronica Adetola
This paper investigates the dynamic interactions between large-scale data centers and the power grid, focusing on reliability challenges arising from sudden fluctuations in demand. With the rapid growth of AI-driven workloads, such fluctuations, along with fast ramp patterns, are expected to exacerbate stressed grid conditions and system instabilities. We consider a few open-source AI data center consumption profiles from the MIT supercloud datasets, along with generating a few experimental HPC job-distribution-based inference profiles. Subsequently, we develop analytical methodologies for real-time assessment of grid stability, focusing on both transient and small-signal stability assessments. Energy-flow-like metrics for nonlinear transient stability, formulated by computing localized data center bus kinetic-like flows and coupling interactions with neighboring buses over varying time windows, help provide operators a real-time assessments of the regional grid stress in the data center hubs. On the other hand, small-signal stability metrics, constructed from analytical state matrices under variable operating conditions during a fast ramping period, enable snapshot-based assessments of data center load fluctuations, provide enhanced observability into evolving grid conditions. By quantifying the stability impacts of large data center clusters, studies conducted in the modified IEEE benchmark $68-$bus model support improved operator situational awareness to capture risks in reliable integration of large data center loads.
Authors: Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini
The increasing integration of distributed energy resources (DERs) into power systems presents opportunities and challenges for ancillary services (AS) provision. Technical requirements of existing AS (i.e., duration, reliability, ramp rate, and lead time) have been designed for traditional generating units, making their provision by DER aggregates particularly challenging. This paper proposes a method to design the duration of reserve capacity AS products considering the operational constraints of DERs and the temporal dynamics of system imbalances. The optimal product duration is determined by maximizing product availability and aligning the supply profile with the system's balancing needs. We apply the methodology to a realistic Swiss low-voltage network with a diverse DER portfolio. The results reveal that (i) shorter product durations maximize average availability and (ii) long product durations improve the alignment with system balancing needs. This paper offers valuable insights for system operators to design AS products tailored for DER participation.
Authors: Hanyang He, John Harlim, Daning Huang, Yan Li
Model predictive control (MPC)-based energy management systems (EMS) are essential for ensuring optimal, secure, and stable operation in microgrids with high penetrations of distributed energy resources. However, due to the high computational cost for the decision-making, the conventional MPC-based EMS typically adopts a simplified integrated-bus power balance model. While this simplification is effective for small networks, large-scale systems require a more detailed branch flow model to account for the increased impact of grid power losses and security constraints. This work proposes an efficient and reliable MPC-based EMS that incorporates power-loss effects and grid-security constraints. %, while adaptively shaping the battery power profile in response to online renewable inputs, achieving reduced operational costs. It enhances system reliability, reduces operational costs, and shows strong potential for online implementation due to its reduced computational effort. Specifically, a second-order cone program (SOCP) branch flow relaxation is integrated into the constraint set, yielding a convex formulation that guarantees globally optimal solutions with high computational efficiency. Owing to the radial topology of the microgrid, this relaxation is practically tight, ensuring equivalence to the original problem. Building on this foundation, an online demand response (DR) module is designed to further reduce the operation cost through peak shaving. To the best of our knowledge, no prior MPC-EMS framework has simultaneously modeled losses and security constraints while coordinating flexible loads within a unified architecture. The developed framework enables secure operation with effective peak shaving and reduced total cost. The effectiveness of the proposed method is validated on 10-bus, 18-bus, and 33-bus systems.
Authors: Anoy Saha, Mona Ghassemi
The electrification of aircraft is reshaping the foundations of aerospace design by positioning electrical systems at the center of propulsion, control, and onboard functionality. This chapter provides an overview of electrical system architectures for electric and hybrid electric aircraft, highlighting both established principles and emerging design strategies. The discussion begins with the motivations for electrification, including reducing environmental impact, improving operational efficiency, and replacing complex pneumatic and hydraulic subsystems with lighter and more reliable electrical alternatives. Aircraft electrical architectures are classified into four major categories: conventional, more electric, all electric, and hybrid electric. A range of system topologies is examined, including direct current (DC), alternating current (AC), hybrid, and distributed configurations. Each is considered in terms of its effectiveness in delivering power, enabling redundancy, supporting fault isolation, and managing thermal performance. Real world examples are presented to demonstrate practical applications, with case studies drawn from the Boeing 787 Dreamliner, the Eviation Alice commuter aircraft, and NASA X57 Maxwell demonstrator. These examples illustrate the ongoing transition from incremental subsystem electrification toward fully integrated architectures that promise higher efficiency and greater sustainability.
Authors: Hanyang He, John Harlim, Daning Huang, Yan Li
Model predictive control (MPC)-based energy management systems (EMS) are essential for ensuring optimal, secure, and stable operation in microgrids with high penetrations of distributed energy resources. However, due to the high computational cost for the decision-making, the conventional MPC-based EMS typically adopts a simplified integrated-bus power balance model. While this simplification is effective for small networks, large-scale systems require a more detailed branch flow model to account for the increased impact of grid power losses and security constraints. This work proposes an efficient and reliable MPC-based EMS that incorporates power-loss effects and grid-security constraints. %, while adaptively shaping the battery power profile in response to online renewable inputs, achieving reduced operational costs. It enhances system reliability, reduces operational costs, and shows strong potential for online implementation due to its reduced computational effort. Specifically, a second-order cone program (SOCP) branch flow relaxation is integrated into the constraint set, yielding a convex formulation that guarantees globally optimal solutions with high computational efficiency. Owing to the radial topology of the microgrid, this relaxation is practically tight, ensuring equivalence to the original problem. Building on this foundation, an online demand response (DR) module is designed to further reduce the operation cost through peak shaving. To the best of our knowledge, no prior MPC-EMS framework has simultaneously modeled losses and security constraints while coordinating flexible loads within a unified architecture. The developed framework enables secure operation with effective peak shaving and reduced total cost. The effectiveness of the proposed method is validated on 10-bus, 18-bus, and 33-bus systems.
Authors: Hao Tu, Yebin Wang, Shaoshuai Mou, Huazhen Fang
Electric vertical take-off and landing (eVTOL) aircraft have emerged as a promising solution to transform urban transportation. They present a few technical challenges for battery management, a prominent one of which is the prediction of the power capability of their lithium-ion battery systems. The challenge originates from the high C-rate discharging conditions required during eVTOL flights as well as the complexity of lithium-ion batteries' electro-thermal dynamics. This paper, for the first time, formulates a power limit prediction problem for eVTOL which explicitly considers long prediction horizons and the possible occurrence of emergency landings. We then harness machine learning to solve this problem in two intertwined ways. First, we adopt a dynamic model that integrates physics with machine learning to predict a lithium-ion battery's voltage and temperature behaviors with high accuracy. Second, while performing search for the maximum power, we leverage machine learning to predict the remaining discharge time and use the prediction to accelerate the search with fast computation. Our validation results show the effectiveness of the proposed study for eVTOL operations.
Authors: Chen Chao, Zixiao Ma, Ziang Zhang
System restoration is critical for power system resilience, nonetheless, its growing reliance on artificial intelligence (AI)-based load forecasting introduces significant cybersecurity risks. Inaccurate forecasts can lead to infeasible planning, voltage and frequency violations, and unsuccessful recovery of de-energized segments, yet the resilience of restoration processes to such attacks remains largely unexplored. This paper addresses this gap by quantifying how adversarially manipulated forecasts impact restoration feasibility and grid security. We develop a gradient-based sparse adversarial attack that strategically perturbs the most influential spatiotemporal inputs, exposing vulnerabilities in forecasting models while maintaining stealth. We further create a restoration-aware validation framework that embeds these compromised forecasts into a sequential restoration model and evaluates operational feasibility using an unbalanced three-phase optimal power flow formulation. Simulation results show that the proposed approach is more efficient and stealthier than baseline attacks. It reveals system-level failures, such as voltage and power ramping violations that prevent the restoration of critical loads. These findings provide actionable insights for designing cybersecurity-aware restoration planning frameworks.
Authors: Mohammadjavad Abbaspour, Mukund R. Shukla, Praveen K. Saxena, Shivam Saxena
Indoor farming enables year-round food production but its reliance on artificial lighting significantly increases energy consumption, peak load charges, and energy costs for growers. Recent studies indicate that plants are able to tolerate interruptions in light, enabling the design of 24-hour lighting schedules (or "recipes") with strategic light modulation in alignment with day-ahead pricing. Thus, we propose an optimal lighting control strategy for indoor farming that modulates light intensity and photoperiod to reduce energy costs. The control strategy is implemented within a model predictive control framework and augmented with transformer-based neural networks to forecast 24-hour ahead solar radiation and electricity prices to improve energy cost reduction. The control strategy is informed by real-world experimentation on lettuce crops to discover minimum light exposure and appropriate dark-light intervals, which are mathematically formulated as constraints to maintain plant health. Simulations for a one-hectare greenhouse, based on real electricity market data from Ontario, demonstrate an annual cost reduction of $318,400 (20.9%), a peak load decrease of 1.6 MW (33.32%), and total energy savings of 1890 MWh (20.2%) against a baseline recipe. These findings highlight the potential of intelligent lighting control to improve the sustainability and economic feasibility of indoor farming.
Authors: Lorenzo Zapparoli, Blazhe Gjorgiev, Giovanni Sansavini
The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provide accurate cost estimations. Here, we propose a new method to assess VPPs' potential to deliver power reserve capacity products under forecasting uncertainty. First, the maximum feasible reserve quantity is determined using a novel formulation of subset simulation for efficient uncertainty quantification. Second, the supply curve is characterized by considering explicit and opportunity costs. The method is applied to a VPP based on a representative Swiss low-voltage network with a diversified DER portfolio. We find that VPPs can reliably offer reserve products and that opportunity costs drive product pricing. Additionally, we show that the product's requirements strongly impact the reserve capacity provision capability. This approach aims to support VPP managers in developing market strategies and policymakers in designing DER-focused AS products.
Authors: Javier Garcia-Aguilar, Aurelio Garcia-Cerrada, Juan L. Zamora, Emilio Bueno, Elena Saiz, Almudena Muñoz-Babiano, Mohammad E. Zarei
The displacement of synchronous generators by converter-interfaced renewable energy sources obliges wind farms to provide inertia, damping, and voltage support, above all in increasingly weak grid conditions. This paper presents a co-ordinated frequency-domain methodology for tuning all control layers of doubly-fed induction generators (DFIGs) within a wind farm operated as a Virtual Synchronous Machine (VSM). Starting from a full small-signal linearisation that preserves loop-to-loop and machine-to-machine couplings, the procedure reshapes every local open loop to explicit phase-margin targets through a single, prioritised iteration. The resulting controllers provide a step response and stability margins close to those programmed at the design stage, in spite of the cross coupling between control loops. Since controller synthesis relies exclusively on classical loop-shaping tools available in commercial simulation suites, it is readily applicable to industrial-scale projects.
Authors: Noah Rhodes
Data visualization is important for developing an understanding of a complex system. this http URL is a data visualization tool for power grids, one of the most complex systems in the world. The design of this http URL is intended to facilitate exploration of power grid data while performing research and to facilitate communication of research findings to an audience. Several tools created to support this software also facilitate analysis of power grid data by transforming the data into graph topology or data-frame data formats that are more compatible for some applications. The high level of flexibility in this http URL enables researchers who are developing and analyzing methods for solving novel power grid problems to better understand and communicate the complexities of their research.
Authors: Canberk Ekmekci, Mujdat Cetin
Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural networks have enabled the development of uncertainty-aware image reconstruction methods. Current generative model-based methods seek to quantify the inherent (aleatoric) uncertainty on the underlying image for given measurements by learning to sample from the posterior distribution of the underlying image. On the other hand, Bayesian neural network-based approaches aim to quantify the model (epistemic) uncertainty on the parameters of a deep neural network-based reconstruction method by approximating the posterior distribution of those parameters. Unfortunately, an ongoing need for an inversion method that can jointly quantify complex aleatoric uncertainty and epistemic uncertainty patterns still persists. In this paper, we present a scalable framework that can quantify both aleatoric and epistemic uncertainties. The proposed framework accepts an existing generative model-based posterior sampling method as an input and introduces an epistemic uncertainty quantification capability through Bayesian neural networks with latent variables and deep ensembling. Furthermore, by leveraging the conformal prediction methodology, the proposed framework can be easily calibrated to ensure rigorous uncertainty quantification. We evaluated the proposed framework on magnetic resonance imaging, computed tomography, and image inpainting problems and showed that the epistemic and aleatoric uncertainty estimates produced by the proposed framework display the characteristic features of true epistemic and aleatoric uncertainties. Furthermore, our results demonstrated that the use of conformal prediction on top of the proposed framework enables marginal coverage guarantees consistent with frequentist principles.
Authors: Joonyong Park, Kenichi Nakamura
This paper introduces EmoSSLSphere, a novel framework for multilingual emotional text-to-speech (TTS) synthesis that combines spherical emotion vectors with discrete token features derived from self-supervised learning (SSL). By encoding emotions in a continuous spherical coordinate space and leveraging SSL-based representations for semantic and acoustic modeling, EmoSSLSphere enables fine-grained emotional control, effective cross-lingual emotion transfer, and robust preservation of speaker identity. We evaluate EmoSSLSphere on English and Japanese corpora, demonstrating significant improvements in speech intelligibility, spectral fidelity, prosodic consistency, and overall synthesis quality. Subjective evaluations further confirm that our method outperforms baseline models in terms of naturalness and emotional expressiveness, underscoring its potential as a scalable solution for multilingual emotional TTS.
Authors: Marco Donald Migliore
This paper investigates the relationship between the Number of Degrees of Freedom ($N_{\rm DoF}$) of the field in deterministic and stochastic source models within Electromagnetic Information Theory (EIT). Our findings demonstrate a fundamental connection between these two approaches. Specifically, we show that a deterministic model and a stochastic model with a spatially incoherent and homogeneous source yield not only the same $N_{\rm DoF}$ but also identical eigenvalues and basis functions for field representation. This key equivalence not only explains the effectiveness of deterministic approaches in EIT but also corroborates the use of classical electromagnetic methods within this new discipline.
Authors: Zhihao Lin, Shuo Liu, Zhen Tian, Dezong Zhao, Jianglin Lan, Chongfeng Wei
Navigating unsignalized roundabouts in mixed-autonomy traffic presents significant challenges due to dense vehicle interactions, lane-changing complexities, and behavioral uncertainties of human-driven vehicles (HDVs). This paper proposes a safety-critical decision-making framework for connected and automated vehicles (CAVs) navigating dual-lane roundabouts alongside HDVs. We formulate the problem as a multi-agent Markov Decision Process and develop a hierarchical safety assessment mechanism that evaluates three critical interaction types: CAV-to-CAV (C2C), CAV-to-HDV (C2H), and CAV-to-Boundary (C2B). A key contribution is our lane-specific uncertainty model for HDVs, which captures distinct behavioral patterns between inner and outer lanes, with outer-lane vehicles exhibiting $2.3\times$ higher uncertainty due to less constrained movements. We integrate this safety framework with a multi-agent Monte Carlo Tree Search (MCTS) algorithm that employs safety-aware pruning to eliminate high-risk trajectories while maintaining computational efficiency. The reward function incorporates Shapley value-based credit assignment to balance individual performance with group coordination. Extensive simulation results validate the effectiveness of the proposed approach under both fully autonomous (100% AVs) and mixed traffic (50% AVs + 50% HDVs) conditions. Compared to benchmark methods, our framework consistently reduces trajectory deviations across all AVs and significantly lowers the rate of Post-Encroachment Time (PET) violations, achieving only 1.0% in the fully autonomous scenario and 3.2% in the mixed traffic setting.
Authors: Xiangying Li, Jiankuan Li, Yong Tang
This paper proposes a transformer-based lossless crossover method, termed Resonant Transformer Router (RTR), which achieves frequency separation while ensuring perfect phase alignment between low-frequency (LF) and high-frequency (HF) channels at the crossover frequency. The core property of RTR is that its frequency responses satisfy a linear complementary relation HLF(f)+HHF(f)=1. so that the original signal can be perfectly reconstructed by linear summation of the two channels. Theoretical derivation and circuit simulations demonstrate that RTR provides superior energy efficiency, phase consistency, and robustness against component tolerances. Compared with conventional LC crossovers and digital FIR/IIR filters, RTR offers a low-loss, low-latency hardware-assisted filtering solution suitable for high-fidelity audio and communication front-ends. The core theory behind this paper's work, lossless crossover, is based on a Chinese patent [CN116318117A] developed from the previous research of one of the authors, Jiankuan Li. We provide a comprehensive experimental validation of this theory and propose a new extension.
Authors: Yida Zhang, Qiuyan Liu, Hongtao Luo, Yuqi Xia, Qiang Wang, Fuchang Li, Xiaofeng Tao, Yuanwei Liu
Stacked intelligent metasurface (SIM) and dual-polarized SIM (DPSIM) enabled wave-domain signal processing have emerged as promising research directions for offloading baseband digital processing tasks and efficiently simplifying transceiver design. However, existing architectures are limited to employing SIM (DPSIM) for a single communication function, such as precoding or combining. To further enhance the overall performance of SIM (DPSIM)-assisted systems and achieve end-to-end (E2E) joint optimization from the transmitted bitstream to the received bitstream, we propose an SIM (DPSIM)-assisted E2E orthogonal frequency division multiplexing (OFDM) system, in which traditional communication tasks such as channel coding, modulation, precoding, combining, demodulation, and channel decoding are performed synchronously within the electromagnetic (EM) forward propagation. Furthermore, inspired by the idea of abstracting real metasurfaces as hidden layers of a neural network, we propose the EM neural network (EMNN) to enable the control of the E2E OFDM communication system. In addition, transfer learning is introduced into the model training, and a training and deployment framework for the EMNN is designed. Simulation results demonstrate that both SIM-assisted E2E OFDM systems and DPSIM-assisted E2E OFDM systems can achieve robust bitstream transmission under complex channel conditions. Our study highlights the application potential of EMNN and SIM (DPSIM)-assisted E2E OFDM systems in the design of next-generation transceivers.
Authors: Shiyong Chen
In this paper, a functional weighted minimum mean-squared error (WMMSE) algorithm is proposed for downlink beamforming in multiuser continuous aperture array (CAPA) systems where both the base station (BS) and users employ CAPAs. The beamforming optimization problem in such systems is inherently functional. To address this challenge, we first employ an orthonormal basis expansion to transform the functional problem into a discrete formulation that shares the same structure as the conventional discrete multi-user multiple-input multiple-output (MU-MIMO) problem. By deriving the first-order optimality conditions of the discrete formulation and mapping them back to the functional domain, we obtain the update equations of the proposed functional WMMSE algorithm. Simulation results show that the proposed method outperforms baselines in both spectral efficiency (SE) and computational complexity.
Authors: Shijie Wang, Haichao Gui, Rui Zhong
This paper addresses two interrelated problems of the nonlinear filtering mechanism and fast attitude filtering with the matrix Fisher distribution (MFD) on the special orthogonal group. By analyzing the distribution evolution along Bayes' rule, we reveal two essential properties that enhance the performance of Bayesian attitude filters with MFDs, particularly in challenging conditions, from a theoretical viewpoint. Benefiting from the new understanding of the filtering mechanism associated with MFDs, two closed-form filters with MFDs is then proposed. These filters avoid the burdensome computations in previous MFD-based filters by introducing linearized error systems with right-invariant errors but retaining the two advantageous properties. Moreover, we leverage the two properties and closed-form filtering iteration to prove the almost-global exponential stability of the proposed filter with right-invariant error for the single-axis rotation, which, to our knowledge, is not achieved by existing directional statistics-based filters. Numerical simulations demonstrate that the proposed filters are significantly more accurate than the classic invariant Kalman filter. Besides, they are also as accurate as recent MFD-based Bayesian filters in challenging circumstances with large initial error and measurement uncertainty but consumes far less computation time (about 1/5 to 1/100 of previous MFD-based attitude filters).
Authors: Jingqi Li, Gechen Qu, Jason J. Choi, Somayeh Sojoudi, Claire Tomlin
Multi-agent reinforcement learning (MARL) optimizes strategic interactions in non-cooperative dynamic games, where agents have misaligned objectives. However, data-driven methods such as multi-agent policy gradients (MA-PG) often suffer from instability and limit-cycle behaviors. Prior stabilization techniques typically rely on entropy-based exploration, which slows learning and increases variance. We propose a model-based approach that incorporates approximate priors into the reward function as regularization. In linear quadratic (LQ) games, we prove that such priors stabilize policy gradients and guarantee local exponential convergence to an approximate Nash equilibrium. We then extend this idea to infinite-horizon nonlinear games by introducing Multi-agent Guided Policy Search (MA-GPS), which constructs short-horizon local LQ approximations from trajectories of current policies to guide training. Experiments on nonlinear vehicle platooning and a six-player strategic basketball formation show that MA-GPS achieves faster convergence and more stable learning than existing MARL methods.
Authors: Chi Ho Leung, Philip E. Paré
We study the identification of continuous-time vector fields from irregularly sampled trajectories. We introduce Spectral Flow Learning (SFL), which learns in a windowed flow space using a lag-linear label operator that aggregates lagged Koopman actions. We provide finite-sample high-probability (FS-HP) guarantees for the class of variable-step linear multistep methods (vLLM). The FS-HP rates are constructed using spectral regularization with qualification-controlled filters for flow predictors under standard source and filter assumptions. A multistep observability inequality links flow error to vector-field error and yields two-term bounds that combine a statistical rate with an explicit discretization bias from vLMM theory. This preliminary preprint states the results and sketches proofs, with full proofs and extensions deferred to a journal version.
Authors: Ying-Ren Chien, Po-Heng Chou, You-Jie Peng, Chun-Yuan Huang, Hen-Wai Tsao, Yu Tsao
To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering circuits of a commercial NB-PLC modem to build a realistic dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured datasets, we conduct both quantitative and qualitative analyses. The training datasets include: 1) a piecewise spectral cyclostationary Gaussian model (PSCGM); 2) a frequency-shift (FRESH) filter; and 3) practical measurements from NB-PLC systems. Simulation results demonstrate that the generated noise samples from the proposed NGGAN are highly close to the real noise samples. The principal component analysis (PCA) scatter plots and Fréchet inception distance (FID) analysis have shown that NGGAN outperforms other GAN-based models by generating noise samples with superior fidelity and higher diversity.
Authors: Ishaan Mahajan, Khai Nguyen, Sam Schoedel, Elakhya Nedumaran, Moises Mata, Brian Plancher, Zachary Manchester
Model-predictive control (MPC) is a powerful framework for controlling dynamic systems under constraints, but it remains challenging to deploy on resource-constrained platforms, especially for problems involving conic constraints. To address this, we extend recent work developing fast, structure-exploiting, cached ADMM solvers for embedded applications, to provide support for second-order cones, as well as C++ code generation from Python, MATLAB, and Julia for easy deployment. Microcontroller benchmarks show that our solver provides up to a two-order-of-magnitude speedup, ranging from 10.6x to 142.7x, over state-of-the-art embedded solvers on QP and SOCP problems, and enables us to fit order-of-magnitude larger problems in memory. We validate our solver's deployed performance through simulation and hardware experiments, including conically-constrained trajectory tracking on a 27g Crazyflie quadrotor. To get started with Conic-TinyMPC, visit our documentation, examples, and the open-source codebase at this https URL.
Authors: Kun Woo Cho, Prasanthi Maddala, Ivan Seskar, Kyle Jamieson
5G mmWave roadside networks promise high bandwidth but face significant challenges in maintaining reliable connections for users moving at high speed. Frequent handovers, complex beam alignment, and signal blockage from car bodies lead to service interruptions and degraded performance. We present Wall-Street, a vehicle-mounted smart surface that enhances 5G mmWave connectivity for in-vehicle users. Wall-Street improves mobility management by (1) steering outdoor mmWave signals into the vehicle for shared coverage, enabling a single, collective handover decision for all users; (2) enabling neighbor-cell search without interrupting data transfer, allowing for seamless handovers; and (3) connecting users to a new cell before disconnecting from the old cell for reliable handovers. We have implemented and integrated Wall-Street in the COSMOS testbed and evaluated its real-time performance with multiple gNBs and users inside a surface-mounted vehicle, driving on a nearby road. In multi-UE scenarios, Wall-Street improves TCP throughput by up to 78% and reduces RTT by up to 34% over the standard 5G Standalone handover protocol.
Authors: Seyed Erfan Fatemieh, Mohammad Reza Reshadinezhad
Researchers and designers are facing problems with memory and power walls, considering the pervasiveness of Von-Neumann architecture in the design of processors and the problems caused by reducing the dimensions of deep sub-micron transistors. Memristive Approximate Computing (AC) and In-Memory Processing (IMP) can be promising solutions to these problems. We have tried to solve the power and memory wall problems by presenting the implementation algorithm of four memristive approximate full adders applying the Material Implication (IMPLY) method. The proposed circuits reduce the number of computational steps by up to 40% compared to the state-of-the-art. The energy consumption of the proposed circuits improves over the previous exact ones by 49%-75% and over the approximate full adders by up to 41%. Multiple error evaluation criteria evaluate the computational accuracy of the proposed approximate full adders in three scenarios in the 8-bit approximate adder structure. The proposed approximate full adders are evaluated in three image processing applications in three scenarios. The results of application-level simulation indicate that the four proposed circuits can be applied in all three scenarios, considering the acceptable image quality metrics of the output images.
Authors: Chao Shen, Yuan Li, Wenkang Zhan, Shujie Pan, Fuxin Lin, Kaiyao Xin, Hui Cong, Chi Xu, Xiaotian Cheng, Ruixiang Liu, Zhibo Ni, Chaoyuan Jin, Bo Xu, Siming Chen, Zhongming Wei, Chunlai Xue, Zhanguo Wang, Chao Zhao
Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters.
Authors: Philipp del Hougne
We recently introduced the "Virtual VNA" concept which estimates the $N \times N$ scattering matrix characterizing an arbitrarily complex linear reciprocal system with $N$ monomodal lumped ports by inputting and outputting waves only via $N_\mathrm{A}
Authors: Abdulhady Abas Abdullah, Aram Mahmood Ahmed, Tarik Rashid, Hadi Veisi
Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments. The primary challenge in this field is the effective separation and recognition of speech from background noise, crucial for applications ranging from voice-activated assistants to automated transcription services. The quality of speech recognition directly impacts user experience and accessibility in technology-driven communication. This review paper explores advanced clustering techniques, particularly focusing on the Kernel Fuzzy C-Means (KFCM) method, to address these challenges. Our findings indicate that KFCM, compared to traditional methods like K-Means (KM) and Fuzzy C-Means (FCM), provides superior performance in handling non-linear and non-stationary noise conditions in speech signals. The most notable outcome of this review is the adaptability of KFCM to various noisy environments, making it a robust choice for speech enhancement applications. Additionally, the paper identifies gaps in current methodologies, such as the need for more dynamic clustering algorithms that can adapt in real time to changing noise conditions without compromising speech recognition quality. Key contributions include a detailed comparative analysis of current clustering algorithms and suggestions for further integrating hybrid models that combine KFCM with neural networks to enhance speech recognition accuracy. Through this review, we advocate for a shift towards more sophisticated, adaptive clustering techniques that can significantly improve speech enhancement and pave the way for more resilient speech processing systems.
Authors: Biplov Paneru, Bishwash Paneru, Bipul Thapa, Khem Narayan Poudyal
This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair navigation. Five different models were trained on a pre-filtered dataset that was divided into fixed-length windows using a sliding window technique. Each window contained statistical measurements, FFT coefficients for different frequency bands, and a label identifying the activity carried out during that window that was taken from an open-source Kaggle repository. The XGBoost model outperformed the other models, CatBoost, GRU, SVC, and XGBoost, with an accuracy of 60%. The CatBoost model with a major difference between training and testing accuracy shows overfitting, and similarly, the best-performing model, with SVC, was implemented in a tkinter GUI. The wheelchair movement could be simulated in various directions, and a Raspberry Pi-powered wheelchair system for brain-computer interface is proposed here.
Authors: Ruiqi Xu, Zihan Zhu, Ben Ahlbrand, Srinath Sridhar, Daniel Ritchie
Recent advances in generative modeling have driven significant progress in text-guided texture synthesis. However, current methods focus on synthesizing texture for single static 3D object, and struggle to handle entire families of shapes, such as those produced by procedural programs. Applying existing methods naively to each procedural shape is too slow to support exploring different parameter configurations at interactive rates, and also results in inconsistent textures across the procedural shapes. To this end, we introduce ProcTex, the first text-to-texture system designed for part-based procedural models. ProcTex enables consistent and real-time text-guided texture synthesis for families of shapes, which integrates seamlessly with the interactive design flow of procedural modeling. To ensure consistency, our core approach is to synthesize texture for a template shape from the procedural model, followed by a texture transfer stage to apply the texture to other procedural shapes via solving dense correspondence. To ensure interactiveness, we propose a novel correspondence network and show that dense correspondence can be effectively learned by a neural network for procedural models. We also develop several techniques, including a retexturing pipeline to support structural variation from procedural parameters, and part-level UV texture map generation for local appearance editing. Extensive experiments on a diverse set of procedural models validate ProcTex's ability to produce high-quality, visually consistent textures while supporting interactive applications.
Authors: Thomas Lew
We derive first-order Pontryagin optimality conditions for stochastic optimal control with deterministic controls for systems modeled by rough differential equations (RDE) driven by Gaussian rough paths. This Pontryagin Maximum Principle (PMP) applies to systems following stochastic differential equations (SDE) driven by Brownian motion, yet it does not rely on forward-backward SDEs and involves the same Hamiltonian as the deterministic PMP. The proof consists of first deriving various integrable error bounds for solutions to nonlinear and linear RDEs by leveraging recent results on Gaussian rough paths. The PMP then follows using standard techniques based on needle-like variations. As an application, we propose the first indirect shooting method for nonlinear stochastic optimal control and show that it converges 10x faster than a direct method on a stabilization task.
Authors: Snehal Raj, Brian Coyle
Scaling full finetuning of large foundation models strains GPU memory and training time. Parameter Efficient Fine-Tuning (PEFT) methods address this issue via adapter modules which update only a small subset of model parameters. In this work, we introduce Quantum-Inspired Compound Adapters (QuIC Adapters), a PEFT approach inspired from Hamming-weight preserving quantum circuits that can effectively finetune a model using less than 0.02\% memory footprint of the base model. QuIC adapters preserve pretrained representations by enforcing orthogonality in weight parameters, and have native deployment mechanisms on quantum computers. We test QuIC adapters by finetuning large language models like LLaMA and vision transformers on language, math, reasoning and vision benchmarks. In its first-order configuration, QuIC recovers the performance of existing orthogonal methods, while higher-order configurations enable substantial parameter compression (over 40x smaller than LoRA) for a modest performance trade-off, unlocking applications in highly resource-constrained environments. Through ablation studies, we determine that combining multiple Hamming-weight orders with orthogonality and matrix compounding are essential for performant finetuning. Our findings suggest that QuIC adapters offers a promising direction for efficient finetuning of foundation models in resource-constrained environments.
Authors: Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard
Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics. Unlike prior methods, our algorithm avoids reliance on two-point gradient estimates while maintaining the same order of sample complexity. Furthermore, it eliminates the restrictive requirement of starting with a stable initial policy, broadening its applicability. Beyond these improvements, we introduce a refined analysis of error propagation through the contraction of the Riccati operator under the Riemannian distance. This refinement leads to a better sample complexity and ensures improved convergence guarantees.
Authors: Torbjørn Smith, Olav Egeland
A nonparametric adaptive controller is proposed for crane control where the payload tracks a desired trajectory with feedback from the payload position. The controller is based on a novel version of partial feedback linearization where the unactuated crane load dynamics are controlled with the position of the actuated crane dynamics instead of the acceleration. This is made possible by taking advantage of the gravity terms in a new Cartesian model that we propose for the load dynamics. This Cartesian model structure makes it possible to implement a nonparametric adaptive controller which cancels disturbances on the crane load by approximating the effects of unknown disturbance forces and structurally unknown dynamics in a reproducing kernel Hilbert space (RKHS). It is shown that the nonparametric adaptive controller leads to uniformly ultimately bounded errors in the presence of unknown forces and unmodeled dynamics. In addition, it is shown that the proposed partial feedback linearization based on the Cartesian model has certain advantages in payload tracking control also in the non-adaptive case. The performance of the nonparametric adaptive controller is validated in simulation and experiments with good results.
Authors: Reva Dhillon, Agni Ravi Deepa, Hrishav Das, Subham Basak, Satadal Ghosh
Efficient path-following is crucial in most of the applications of autonomous vehicles (UxV). Among various guidance strategies presented in literature, the look-ahead distance ($L_1$)-based nonlinear guidance has received significant attention due to its ease in implementation and ability to maintain a low cross-track error while following simpler reference paths and generating bounded lateral acceleration commands. However, the constant value of $L_1$ becomes problematic when the UxV is far away from the reference path and also produces higher cross-track error while following complex reference paths having high variation in radius of curvature. To address these challenges, the notion of look-ahead distance is leveraged in a novel way to develop a two-phase guidance strategy. Initially, when the UxV is far from the reference path, an optimized $L_1$ selection strategy is developed to guide the UxV towards the vicinity of the start point of the reference path, while maintaining minimal lateral acceleration command. Once the vehicle reaches a close neighborhood of the reference path, a novel notion of corrector point is incorporated in the constant $L_1$-based guidance scheme to generate the guidance command that effectively reduces the root mean square of the cross-track error and lateral acceleration requirement thereafter. Simulation results validate satisfactory performance of this proposed corrector point and look-ahead point pair-based guidance strategy, along with the developed mid-course guidance scheme. Also, its superiority over the conventional constant $L_1$ guidance scheme is established by simulation studies over different initial condition scenarios.
Authors: Emir Cem Gezer, Mael Korentin Ivan Moreau, Anders Sandneseng Høgden, Dong Trong Nguyen, Roger Skjetne, Asgeir Sørensen
The algorithms developed for Maritime Autonomous Surface Ships (MASS) are often challenging to test on actual vessels due to high operational costs and safety considerations. Simulations offer a cost-effective alternative and eliminate risks, but they may not accurately represent real-world dynamics for the given tasks. Utilizing small-scale model ships and robotic vessels in conjunction with a laboratory basin provides an accessible testing environment for the early stages of validation processes. However, designing and developing a model vessel for a single test can be costly and cumbersome, and researchers often lack access to such infrastructure. To address these challenges and enable streamlined testing, we have developed an in-house testbed that facilitates the development, testing, verification, and validation of MASS algorithms in a digital-physical laboratory. This infrastructure includes a set of small-scale model vessels, a simulation environment for each vessel, a comprehensive testbed environment, and a digital twin in Unity. With this, we aim to establish a full design and verification pipeline that starts with high-fidelity simulation models of each model vessel, to the model-scale testing in the laboratory basin, allowing possibilities for moving towards semi-fullscale validation with R/V milliAmpere1 and full-scale validation with R/V Gunnerus. In this work, we present our progress on the development of this testbed environment and its components, demonstrating its effectiveness in enabling ship guidance, navigation, and control (GNC), including autonomy.
Authors: Joon-Seung Choi, Dong-Min Byun, Hyung-Seok Oh, Seong-Whan Lee
Controlling singing style is crucial for achieving an expressive and natural singing voice. Among the various style factors, vibrato plays a key role in conveying emotions and enhancing musical depth. However, modeling vibrato remains challenging due to its dynamic nature, making it difficult to control in singing voice conversion. To address this, we propose VibESVC, a controllable singing voice conversion model that explicitly extracts and manipulates vibrato using discrete wavelet transform. Unlike previous methods that model vibrato implicitly, our approach decomposes the F0 contour into frequency components, enabling precise transfer. This allows vibrato control for enhanced flexibility. Experimental results show that VibE-SVC effectively transforms singing styles while preserving speaker similarity. Both subjective and objective evaluations confirm high-quality conversion.
Authors: Iddo Yosha, Gallil Maimon, Yossi Adi
Sentence stress refers to emphasis on words within a spoken utterance to highlight or contrast an idea. It is often used to imply an underlying intention not explicitly stated. Recent speech-aware language models (SLMs) have enabled direct audio processing, allowing models to access the full richness of speech to perform audio reasoning tasks such as spoken question answering. Despite the crucial role of sentence stress in shaping meaning and intent, it remains largely overlooked in evaluation and development of SLMs. We address this gap by introducing StressTest, a benchmark designed to evaluate models' ability to distinguish between meanings of speech based on the stress pattern. We evaluate leading SLMs, and find that despite their overall capabilities, they perform poorly on such tasks. Hence, we propose a novel data generation pipeline, and create Stress-17k, a training set that simulates change of meaning implied by stress variation. Results suggest, that our finetuned model, StresSLM, generalizes well to real recordings and notably outperforms existing SLMs on sentence stress reasoning and detection. Models, code, data, samples - this http URL.
Authors: Jens Lucht, Paul Meyer, Leon Merten Lohse, Tim Salditt
We present a Python toolbox for holographic and tomographic X-ray imaging. It comprises a collection of phase retrieval algorithms for the deeply holographic and direct contrast imaging regimes, including non-linear approaches and extended choices of regularization, constraint sets, and optimizers, all implemented with a unified and intuitive interface. Moreover, it features auxiliary functions for (tomographic) alignment, image processing, and simulation of imaging experiments. The capability of the toolbox is illustrated by the example of a catalytic nanoparticle, imaged in the deeply holographic regime at the 'GINIX' instrument of the P10 beamline at the PETRA III storage ring (DESY, Hamburg). Due to its modular design, the toolbox can be used for algorithmic development and benchmarking in a lean and flexible manner, or be interfaced and integrated in the reconstruction pipeline of other synchrotron or XFEL instruments for phase imaging based on propagation.
Authors: Yuqin Dai, Wanlu Zhu, Ronghui Li, Xiu Li, Zhenyu Zhang, Jun Li, Jian Yang
Music-driven dance generation has garnered significant attention due to its wide range of industrial applications, particularly in the creation of group choreography. During the group dance generation process, however, most existing methods still face three primary issues: multi-dancer collisions, single-dancer foot sliding and abrupt swapping in the generation of long group dance. In this paper, we propose TCDiff++, a music-driven end-to-end framework designed to generate harmonious group dance. Specifically, to mitigate multi-dancer collisions, we utilize a dancer positioning embedding to encode temporal and identity information. Additionally, we incorporate a distance-consistency loss to ensure that inter-dancer distances remain within plausible ranges. To address the issue of single-dancer foot sliding, we introduce a swap mode embedding to indicate dancer swapping patterns and design a Footwork Adaptor to refine raw motion, thereby minimizing foot sliding. For long group dance generation, we present a long group diffusion sampling strategy that reduces abrupt position shifts by injecting positional information into the noisy input. Furthermore, we integrate a Sequence Decoder layer to enhance the model's ability to selectively process long sequences. Extensive experiments demonstrate that our TCDiff++ achieves state-of-the-art performance, particularly in long-duration scenarios, ensuring high-quality and coherent group dance generation.
Authors: Huynh Dang Nguyen, Trong-Thang Pham, Ngan Le, Van Nguyen
The electrocardiogram (ECG) is an essential and effective tool for diagnosing heart diseases. However, its effectiveness can be compromised by noise or unavailability of one or more leads of the standard 12-lead recordings, resulting in diagnostic errors or uncertainty. To address these challenges, we propose TolerantECG, a foundation model for ECG signals that is robust to noise and capable of functioning with arbitrary subsets of the standard 12-lead ECG. TolerantECG training combines contrastive and self-supervised learning frameworks to jointly learn ECG signal representations alongside their corresponding knowledge-retrieval-based text report descriptions and corrupted or lead-missing signals. Comprehensive benchmarking results demonstrate that TolerantECG consistently ranks as the best or second-best performer across various ECG signal conditions and class levels in the PTB-XL dataset, and achieves the highest performance on the MIT-BIH Arrhythmia Database.
Authors: Mohammad Alikhani, Reza Kazemi
In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyberattack data, as most industrial systems operate under normal conditions. This data imbalance, combined with the high cost of annotation, hinders the effective training of machine learning models. Moreover, the rapid detection of attacks is essential, especially in critical infrastructure, to prevent large-scale disruptions. To address these challenges, we propose a real-time intrusion detection system based on a semi-supervised contrastive learning framework using the Kolmogorov-Arnold Network (KAN). Our method leverages abundant unlabeled data to effectively distinguish between normal and attack behaviors. We validate our approach on three benchmark datasets, UNSW-NB15, BoT-IoT, and Gas Pipeline, using only 2.20%, 1.28%, and 8% of labeled samples, respectively, to simulate real-world conditions. Experimental results show that our method outperforms existing contrastive learning-based approaches. We further compare KAN with a traditional multilayer perceptron (MLP), demonstrating KAN's superior performance in both detection accuracy and robustness under limited supervision. KAN's ability to model complex relationships, along with its learnable activation functions, is also explored and visualized, offering interpretability and the potential for rule extraction. The method supports multi-class classification and proves effective in safety, critical environments where reliability is paramount.
Authors: Jan Melechovsky, Ambuj Mehrish, Abhinaba Roy, Dorien Herremans
Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster's enhanced outputs over the original degraded audio, highlighting the effectiveness of our unified approach.
Authors: Keunwoo Choi, Seungheon Doh, Juhan Nam
We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline. In the proposed pipeline, multiple large language model (LLM) agents are created under various roles with specialized prompts and access to different parts of information, and the chat data is acquired by logging the conversation between the Listener LLM and the Recsys LLM. To cover various conversation scenarios, for each conversation, the Listener LLM is conditioned on a finetuned conversation goal. Finally, all the LLMs are multimodal with audio and images, allowing a simulation of multimodal recommendation and conversation. In the LLM-as-a-judge and subjective evaluation experiments, TalkPlayData 2 achieved the proposed goal in various aspects related to training a generative recommendation model for music. TalkPlayData 2 and its generation code are open-sourced at this https URL.
Authors: Keyu An, Yanni Chen, Chong Deng, Changfeng Gao, Zhifu Gao, Bo Gong, Xiangang Li, Yabin Li, Xiang Lv, Yunjie Ji, Yiheng Jiang, Bin Ma, Haoneng Luo, Chongjia Ni, Zexu Pan, Yiping Peng, Zhendong Peng, Peiyao Wang, Hao Wang, Wen Wang, Wupeng Wang, Biao Tian, Zhentao Tan, Nan Yang, Bin Yuan, Jieping Ye, Jixing Yu, Qinglin Zhang, Kun Zou, Han Zhao, Shengkui Zhao, Jingren Zhou
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep integration with large language models (LLMs). However, LLMs are prone to hallucination, which can significantly degrade user experience in real-world ASR applications. In this paper, we present Fun-ASR, a large-scale, LLM-based ASR system that synergistically combines massive data, large model capacity, LLM integration, and reinforcement learning to achieve state-of-the-art performance across diverse and complex speech recognition scenarios. Moreover, Fun-ASR is specifically optimized for practical deployment, with enhancements in streaming capability, noise robustness, code-switching, hotword customization, and satisfying other real-world application requirements. Experimental results show that while most LLM-based ASR systems achieve strong performance on open-source benchmarks, they often underperform on real industry evaluation sets. Thanks to production-oriented optimizations, Fun-ASR achieves state-of-the-art performance on real application datasets, demonstrating its effectiveness and robustness in practical settings.
Authors: Tornike Karchkhadze, Kuan-Lin Chen, Mojtaba Heydari, Robert Henzel, Alessandro Toso, Mehrez Souden, Joshua Atkins
We present StereoFoley, a video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. While recent generative video-to-audio models achieve strong semantic and temporal fidelity, they largely remain limited to mono or fail to deliver object-aware stereo imaging, constrained by the lack of professionally mixed, spatially accurate video-to-audio datasets. First, we develop and train a base model that generates stereo audio from video, achieving state-of-the-art in both semantic accuracy and synchronization. Next, to overcome dataset limitations, we introduce a synthetic data generation pipeline that combines video analysis, object tracking, and audio synthesis with dynamic panning and distance-based loudness controls, enabling spatially accurate object-aware sound. Finally, we fine-tune the base model on this synthetic dataset, yielding clear object-audio correspondence. Since no established metrics exist, we introduce stereo object-awareness measures and validate it through a human listening study, showing strong correlation with perception. This work establishes the first end-to-end framework for stereo object-aware video-to-audio generation, addressing a critical gap and setting a new benchmark in the field.
Authors: Xuanhao Zhang, Chang Li
Diffusion models have achieved remarkable progress in image and audio generation, largely due to Classifier-Free Guidance. However, the choice of guidance scale remains underexplored: a fixed scale often fails to generalize across prompts of varying complexity, leading to oversaturation or weak alignment. We address this gap by introducing a prompt-aware framework that predicts scale-dependent quality and selects the optimal guidance at inference. Specifically, we construct a large synthetic dataset by generating samples under multiple scales and scoring them with reliable evaluation metrics. A lightweight predictor, conditioned on semantic embeddings and linguistic complexity, estimates multi-metric quality curves and determines the best scale via a utility function with regularization. Experiments on MSCOCO~2014 and AudioCaps show consistent improvements over vanilla CFG, enhancing fidelity, alignment, and perceptual preference. This work demonstrates that prompt-aware scale selection provides an effective, training-free enhancement for pretrained diffusion backbones.
Authors: Pietro Bruschi
In-Orbit Servicing and Active Debris Removal require advanced robotic capabilities for capturing and detumbling uncooperative targets. This work presents a hierarchical control framework for autonomous robotic capture of tumbling objects in space. A simulation environment is developed, incorporating sloshing dynamics of the chaser, a rarely studied effect in space robotics. The proposed controller combines an inner Lyapunov-based robust control loop for multi-body dynamics with an outer loop addressing an extended inverse kinematics problem. Simulation results show improved robustness and adaptability compared to existing control schemes.
Authors: Dennis Gramlich, Shuhao Yan, Carsten W. Scherer, Christian Ebenbauer%
This article shows that distributionally robust controller synthesis as investigated in \cite{taskesen2024distributionally} can be formulated as a convex linear matrix inequality (LMI) synthesis problem. To this end, we rely on well-established convexification techniques from robust control. The LMI synthesis problem we propose has the advantage that it can be solved efficiently using off-the-shelf semi-definite programming (SDP) solvers. In addition, our formulation exposes the studied distributionally robust controller synthesis problem as an instance of robust $H_2$ synthesis.
Authors: Gio Paik, Yongbeom Kim, Soungmin Lee, Sangmin Ahn, Chanwoo Kim
Despite advances in multilingual automatic speech recognition (ASR), code-switching (CS), the mixing of languages within an utterance common in daily speech, remains a severely underexplored challenge. In this paper, we introduce HiKE: the Hierarchical Korean-English code-switching benchmark, the first globally accessible evaluation framework for Korean-English CS, aiming to provide a means for the precise evaluation of multilingual ASR models and to foster research in the field. The proposed framework not only consists of high-quality, natural CS data across various topics, but also provides meticulous loanword labels and a hierarchical CS-level labeling scheme (word, phrase, and sentence) that together enable a systematic evaluation of a model's ability to handle each distinct level of code-switching. Through evaluations of diverse multilingual ASR models and fine-tuning experiments, this paper demonstrates that although most multilingual ASR models initially exhibit inadequate CS-ASR performance, this capability can be enabled through fine-tuning with synthetic CS data. HiKE is available at this https URL
Authors: Chi Ho Leung, Philip E. Paré
We propose the multistep port-Hamiltonian Gaussian process (MS-PHS GP) to learn physically consistent continuous-time dynamics and a posterior over the Hamiltonian from noisy, irregularly-sampled trajectories. By placing a GP prior on the Hamiltonian surface $H$ and encoding variable-step multistep integrator constraints as finite linear functionals, MS-PHS GP enables closed-form conditioning of both the vector field and the Hamiltonian surface without latent states, while enforcing energy balance and passivity by design. We state a finite-sample vector-field bound that separates the estimation and variable-step discretization terms. Lastly, we demonstrate improved vector-field recovery and well-calibrated Hamiltonian uncertainty on mass-spring, Van der Pol, and Duffing benchmarks.
Authors: Seungheon Doh, Keunwoo Choi, Juhan Nam
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited, leaving other simpler yet crucial components such as metadata or attribute filtering underutilized in the system. We propose an LLM-based music recommendation system with tool calling to serve as a unified retrieval-reranking pipeline. Our system positions an LLM as an end-to-end recommendation system that interprets user intent, plans tool invocations, and orchestrates specialized components: boolean filters (SQL), sparse retrieval (BM25), dense retrieval (embedding similarity), and generative retrieval (semantic IDs). Through tool planning, the system predicts which types of tools to use, their execution order, and the arguments needed to find music matching user preferences, supporting diverse modalities while seamlessly integrating multiple database filtering methods. We demonstrate that this unified tool-calling framework achieves competitive performance across diverse recommendation scenarios by selectively employing appropriate retrieval methods based on user queries, envisioning a new paradigm for conversational music recommendation systems.
Authors: Georg Schildbach
State-of-the-art approaches of Robust Model Predictive Control (MPC) are restricted to linear systems of relatively small scale, i.e., with no more than about 5 states. The main reason is the computational burden of determining a robust positively invariant (RPI) set, whose complexity suffers from the curse of dimensionality. The recently proposed approach of Deadbeat Robust Model Predictive Control (DRMPC) is the first that does not rely on an RPI set. Yet it comes with the full set of essential system theoretic guarantees. DRMPC is hence a viable option, in particular, for large-scale systems. This paper introduces a detailed design procedure for DRMPC. It is shown that the optimal control problem generated for DRMPC has exactly the same computational complexity as Nominal MPC. A numerical study validates its applicability to randomly generated large-scale linear systems of various dimensions.
Authors: Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Yen-Ting Liu
Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and makes it difficult to manage product quality. In this paper, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and a deep learning (DL)-based model. The two most common defect categories of DIP are examined: (1) surface defects, and (2) pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable-sized dataset for training and testing. Four varieties of the YOLO model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50\%, detection time of 285 ms, and is far superior to threshold-based approaches. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.
Authors: Yubo Zhang, Jeremy Johnston, Xiaodong Wang
We develop an end-to-end deep learning framework for downlink beamforming in large-scale sparse MIMO channels. The core is a deep EDN architecture with three modules: (i) an encoder NN, deployed at each user end, that compresses estimated downlink channels into low-dimensional latent vectors. The latent vector from each user is compressed and then fed back to the BS. (ii) a beamformer decoder NN at the BS that maps recovered latent vectors to beamformers, and (iii) a channel decoder NN at the BS that reconstructs downlink channels from recovered latent vectors to further refine the beamformers. The training of EDN leverages two key strategies: (a) semi-amortized learning, where the beamformer decoder NN contains an analytical gradient ascent during both training and inference stages, and (b) knowledge distillation, where the loss function consists of a supervised term and an unsupervised term, and starting from supervised training with MMSE beamformers, over the epochs, the model training gradually shifts toward unsupervised using the sum-rate objective. The proposed EDN beamforming framework is extended to both far-field and near-field hybrid beamforming scenarios. Extensive simulations validate its effectiveness under diverse network and channel conditions.
Authors: Wenlong Shi, Hongyi Li, Cong Bai, Zhaoyu Wang
Integrating renewable energy sources into the grid not only reduces global carbon emissions, but also facilitates distribution system (DS) blackstart restoration. This process leverages renewable energy, inverters, situational awareness and distribution automation to initiate blackstart at the DS level, obtaining a fast response and bottom-up restoration. In this Review, we survey the latest technological advances for DS blackstart restoration using renewable energy. We first present mathematical models for distributed energy resources (DERs), network topology, and load dynamics. We then discuss how the situational awareness can help improve restoration performance through real-time monitoring and forecasting. Next, the DS blackstart restoration problem, including objectives, constraints, and existing methodologies for decision-making are provided. Lastly, we outline remaining challenges, and highlight the opportunities and future research directions.
Authors: Wenlong Shi, Junyuan Zheng, Zhaoyu Wang
Restoration in power distribution systems (PDSs) is well studied, however, most existing research focuses on network partition and microgrid formation, where load transfer is limited to adjacent feeders. This focus is not practical, as when adjacent feeders lack sufficient capacity, utilities may request support from more distant feeders in practice. Such a hirarchical restoration is complex, especially when involving changing system conditions due to cold load pickup and delayed reconnection of behind-the-meter DERs. To fill this research gap, a situationally aware multi-tier load restoration framework is proposed. Specifically, models are proposed to describe the multi-tier load restoration, including the multi-tier load transfer and substation transformer and feeder protection models. By introducing binary actional switching variables and load block transfer variables, the models effectively captures the dynamics of switches and multi-tier transfer process. To integrate situational awareness of evolving system conditions, the problem is formulated as a mixed-integer linear program (MILP) and then embedded within a rolling horizon optimization. Particularly, a set of safeguarded constraints are developed based on segment-level restoration reward bounds to mitigate the myopia of traditional rolling horizon optimization. The proposed safeguarded rolling strategy guarantees that each time step is lower bounded by a $(1-\varepsilon)$-fraction of its optimal restoration potential, thereby balancing short-term switching decisions with long-term restoration goals. Finally, cases studies on the modified IEEE 123-node test feeder validate the proposed multi-tier restoration framework.
Authors: Tejaswini Sanjay Katale, Lu Gao, Yunpeng Zhang, Alaa Senouci
Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber-physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bi-level formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bi-level optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack-control interaction is solved via Karush-Kuhn-Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction of service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber-physical infrastructure.
Authors: Bassel Diban, Giovanni Mazzanti
This paper goes one step forward in the life estimation of HVDC cable insulation under load cycles by introducing for the first time a microscopic model of charge conduction and transport i.e., Bipolar Charge Transport BCT model for electric field calculation inside the insulation thickness. The paper firstly includes the development and the validation of BCT model with that found in literature. Then, the parameters of the developed BCT model are optimized using Pulsed Electro-Acoustic PEA space charge measurements. Followed by the integration of the developed, validated and optimized model into the electric field calculation for life estimation of a 500 kV DC-XLPE insulated cable subjected to Type Test load cycles according to Cigre Techical Brochure 852. The developed microscopic model is compared to the macroscopic models already found in the literature. The microscopic model shows a comparable electric field inversion similarly to macroscopic models. However, the behavior of the microscopic model is noticed to be different under heating and cooling load cycles. In hot cable, the maximum electric field stabilizes at different amplitude and position inside the insulation thickness in both models. This investigation has been carried out in the framework of the HEU-NEWGEN research project.
Authors: Kaidi Huang, Lin Cheng, Yue Zhou, Fashun Shi, Yufei Xi, Yingrui Zhuang, Ning Qi
Peer-to-peer energy trading offers a promising solution for enhancing renewable energy utilization and economic benefits within interconnected microgrids. However, existing real-time P2P markets face two key challenges: high computational complexity in trading mechanisms, and suboptimal participant decision-making under diverse uncertainties. Existing prediction-based decision-making methods rely heavily on accurate forecasts, which are typically unavailable for microgrids, while prediction-free methods suffer from myopic behaviors. To address these challenges, this paper proposes an improved double auction mechanism combined with an adaptive step-size search algorithm to reduce computational burden, and a data-driven dual-reference online optimization (DDOO) framework to enhance participant decision-making. The improved mechanism simplifies bidding procedures, significantly reducing computational burden and ensuring rapid convergence to the market equilibrium. Additionally, the prediction-free DDOO framework mitigates myopic decision-making by introducing two informative reference signals. Case studies on a 20-microgrid system demonstrate the effectiveness and scalability of the proposed mechanism and approach. The improved mechanism significantly decreases the computational time while increasing local energy self-sufficiency periods from 0.01% to 29.86%, reducing reverse power flow periods from 24.51% to 3.96%, and lowering average operating costs by 19.20%. Compared with conventional approaches such as Lyapunov optimization and model predictive control, the DDOO framework achieves a 10%-13% reduction in operating costs with an optimality gap of only 5.76%.
Authors: Ghulam Mohy-ud-din, Yunqi Wang, Rahmat Heidari, Frederik Geth
Addressing the uncertainty introduced by increasing renewable integration is crucial for secure power system operation, yet capturing it while preserving the full nonlinear physics of the grid remains a significant challenge. This paper presents a stochastic security constrained optimal power flow model with chance constraints supporting nonlinear AC power flow equations and non Gaussian uncertainties. We use general polynomial chaos expansion to model arbitrary uncertainties of finite variance, enabling accurate moment computations and robust prediction of system states across diverse operating scenarios. The chance constraints probabilistically limit inequality violations, providing a more flexible representation of controllable variables and the consequent power system operation. Case studies validate the proposed models effectiveness in satisfying operational constraints and capturing uncertainty with high fidelity. Compared to the deterministic formulation, it also uncovers a wider set of unsecure contingencies, highlighting improved uncertainty capture and operational insight.
Authors: Yanhua Tan, Beixiong Zheng, Yi Fang, Derrick Wing Kwan Ng, Jie Xu, Rui Zhang
Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. Among them, rotatable antenna (RA) technology has recently drawn significant attention in wireless systems owing to its unique ability to exploit additional spatial degrees-of-freedom (DoFs) by dynamically adjusting the three-dimensional (3D) boresight direction of each antenna. In this letter, we propose a new RA-assisted cognitive radio (CR) system designed to achieve efficient spectrum sharing while mitigating interference between primary and secondary communication links. Specifically, we formulate an optimization problem for the joint design of the transmit beamforming and the boresight directions of RAs at the secondary transmitter (ST), aimed at maximizing the received signal-to-interference-plus-noise ratio (SINR) at the secondary receiver (SR), while satisfying both interference constraint at the primary receiver (PR) and the maximum transmit power constraint at the ST. Although the formulated problem is challenging to solve due to its non-convexity and coupled variables, we develop an efficient algorithm by leveraging alternating optimization (AO) and successive convex approximation (SCA) techniques to acquire high-quality solutions. Numerical results demonstrate that the proposed RA-assisted system substantially outperforms conventional benchmark schemes in spectrum-sharing CR systems, validating RA's capability to simultaneously enhance the communication quality at the SR and mitigate interference at the PR.
Authors: Zhe Zhang, Gaochang Wu, Jing Zhang, Xiatian Zhu, Dacheng Tao, Tianyou Chai
Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain. The majority of existing UDA-SS works typically consider images whilst recent attempts have extended further to tackle videos by modeling the temporal dimension. Although the two lines of research share the major challenges -- overcoming the underlying domain distribution shift, their studies are largely independent, resulting in fragmented insights, a lack of holistic understanding, and missed opportunities for cross-pollination of ideas. This fragmentation prevents the unification of methods, leading to redundant efforts and suboptimal knowledge transfer across image and video domains. Under this observation, we advocate unifying the study of UDA-SS across video and image scenarios, enabling a more comprehensive understanding, synergistic advancements, and efficient knowledge sharing. To that end, we explore the unified UDA-SS from a general data augmentation perspective, serving as a unifying conceptual framework, enabling improved generalization, and potential for cross-pollination of ideas, ultimately contributing to the overall progress and practical impact of this field of research. Specifically, we propose a Quad-directional Mixup (QuadMix) method, characterized by tackling distinct point attributes and feature inconsistencies through four-directional paths for intra- and inter-domain mixing in a feature space. To deal with temporal shifts with videos, we incorporate optical flow-guided feature aggregation across spatial and temporal dimensions for fine-grained domain alignment. Extensive experiments show that our method outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks. Our source code and models will be released at this https URL.
Authors: Rafael Cisneros, Leyan Fang, Wei He, Romeo Ortega
In this paper we propose a variation of the widely popular Interconnection-and-Damping-Assigment Passivity-Based Control (IDA-PBC) based on Poincare's Lemma to design output feedback globally stabilizing controllers for two dimensional systems. The procedure is constructive and, in comparison with the classical IDA-PBC, whose application is often stymied by the need to solve the (infamous) matching partial differential equation (PDE), in this new method the PDE is replaced by an ordinary differential equation, whose solution is far simpler. The procedure is then applied for the design of voltage-feedback controllers for the three most typical DC-to-DC power converter topologies: the Buck, Boost and Buck-Boost. It is assumed that these converters feed an uncertain load, which is characterized by a static relation between its voltage and current. In the case when the load consists of the parallel connection of a resistive term and a constant power load we propose an adaptive version of the design, adding an identification scheme for the load parameters. This allows the controller to regulate the converter output when the load varies-that is a typical scenario in these applications. Extensive numerical simulations and experimental results validate the approach.
Authors: Burak Dindar, Can Berk Saner, Hüseyin Kemal Çakmak, Veit Hagenmeyer
As the role of distribution system (DS) flexibility in transmission system operator (TSO) network management becomes increasingly vital, data privacy concerns hinder seamless interoperability. The notion of the feasible operating region (FOR), defined in the PQ domain, has emerged as a promising privacy-preserving approach. However, effectively leveraging FOR in TSO operations remains challenging due to three key factors: its accurate determination in large-scale, meshed DS networks; its tractable analytical representation; and its economic valuation. In the present paper, we propose a novel AC optimal power flow (OPF)-based method to construct a three-dimensional PQV-FOR, explicitly accounting for voltage variability and diverse flexibility-providing unit (FPU) characteristics. The construction process employs a two-stage sampling strategy that combines bounding box projection and Fibonacci direction techniques to efficiently capture the FOR. We then introduce an implicit polynomial fitting approach to analytically represent the FOR. Furthermore, we derive a quadratic cost function over the PQV domain to monetize the FOR. Thus, the proposed framework enables single-round TSO-DSO coordination: the DSO provides an analytical FOR and cost model; the TSO determines operating point at the point of common coupling (PCC) within the FOR-based AC-OPF; and the DSO computes FPU dispatch by solving its local OPF, without computationally intensive disaggregation or iterative coordination. Case studies on meshed DS with up to 533 buses, integrated into TS, demonstrates the method's efficiency compared to standard AC-OPF. On average, the proposed approach yields negligible cost deviations of at most 0.058% across test cases, while reducing computation times by up to 58.11%.
Authors: Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Bergés
Advanced control strategies like Model Predictive Control (MPC) offer significant energy savings for HVAC systems but often require substantial engineering effort, limiting scalability. Reinforcement Learning (RL) promises greater automation and adaptability, yet its practical application in real-world residential settings remains largely undemonstrated, facing challenges related to safety, interpretability, and sample efficiency. To investigate these practical issues, we performed a direct comparison of an MPC and a model-based RL controller, with each controller deployed for a one-month period in an occupied house with a heat pump system in West Lafayette, Indiana. This investigation aimed to explore scalability of the chosen RL and MPC implementations while ensuring safety and comparability. The advanced controllers were evaluated against each other and against the existing controller. RL achieved substantial energy savings (22\% relative to the existing controller), slightly exceeding MPC's savings (20\%), albeit with modestly higher occupant discomfort. However, when energy savings were normalized for the level of comfort provided, MPC demonstrated superior performance. This study's empirical results show that while RL reduces engineering overhead, it introduces practical trade-offs in model accuracy and operational robustness. The key lessons learned concern the difficulties of safe controller initialization, navigating the mismatch between control actions and their practical implementation, and maintaining the integrity of online learning in a live environment. These insights pinpoint the essential research directions needed to advance RL from a promising concept to a truly scalable HVAC control solution.
Authors: Eman Badr, Abdullah Al Maruf
Complex, interconnected cyber-physical systems (CPS) are increasingly prevalent in domains such as power systems. Cyber-resilient architectures have been proposed to recover compromised cyber components of CPS. Recent works have studied tuning the recovery times of such architectures to guarantee safety in single-system settings. Extending these designs to interconnected CPS is more challenging, since solutions must account for attacks on multiple subsystems that can occur in any order and potentially infinite possible temporal overlap. This paper aims to address the aforementioned challenge by developing a scalable framework to assign resilient architectures and to inform the tuning of their recovery times. Our approach introduces a scalar index that quantifies the impact of each subsystem on safety under compromised input. These indices aggregate linearly across subsystems, enabling scalable analysis under arbitrary attack orderings and temporal overlaps. We establish a linear inequality relating each subsystem's index and recovery time that guarantees safety and guides resilient architecture assignment. We also propose a segmentation-based approach to strengthen the previously derived conditions. We then present algorithms to compute the proposed indices and to find a cost-optimal architecture assignment with a safety guarantee. We validate the framework through a case study on temperature regulation in interconnected rooms under different attack scenarios.
Authors: Rohan Vitthal Thorat, Juhi Singh, Rajdip Nayek
Structural vibrations induced by external excitations pose significant risks, including safety hazards for occupants, structural damage, and increased maintenance costs. While conventional model-based control strategies, such as Linear Quadratic Regulator (LQR), effectively mitigate vibrations, their reliance on accurate system models necessitates tedious system identification. This tedious system identification process can be avoided by using a model-free Reinforcement learning (RL) method. RL controllers derive their policies solely from observed structural behaviour, eliminating the requirement for an explicit structural model. For an RL controller to be truly model-free, its training must occur on the actual physical system rather than in simulation. However, during this training phase, the RL controller lacks prior knowledge and it exerts control force on the structure randomly, which can potentially harm the structure. To mitigate this risk, we propose guiding the RL controller using a Linear Quadratic Regulator (LQR) controller. While LQR control typically relies on an accurate structural model for optimal performance, our observations indicate that even an LQR controller based on an entirely incorrect model outperforms the uncontrolled scenario. Motivated by this finding, we introduce a hybrid control framework that integrates both LQR and RL controllers. In this approach, the LQR policy is derived from a randomly selected model and its parameters. As this LQR policy does not require knowledge of the true or an approximate structural model the overall framework remains model-free. This hybrid approach eliminates dependency on explicit system models while minimizing exploration risks inherent in naive RL implementations. As per our knowledge, this is the first study to address the critical training safety challenge of RL-based vibration control and provide a validated solution.
Authors: Sebastian Kebrich, Felix Engelhardt, David Franzmann, Christina Büsing, Jochen Linßen, Heidi Heinrichs
Future greenhouse gas neutral energy systems will be dominated by renewable energy technologies whose energy output and utilisation is subject to uncertain weather conditions. This work proposes an algorithm for capacity expansion planning if only uncertain data is available for a year's operative parameters. When faced with multiple possible operating years, the quality of a solution derived on a single operating year's data is evaluated for all years, and the optimisation problem is iteratively modified whenever supply gaps are detected. These modifications lead to solutions with sufficient back-up capacity to overcome periods of cold dark lulls, and sufficient total annual energy supply across all years. A computational study on an energy system model of Germany for 40 different operating years shows that the iterative algorithm finds solutions that guarantee security of supply for all considered years increasing the total annual cost by 1.6-2.9% compared to a lower bound. Results also underline the importance of assessing the feasibility of energy system models using atypical time-series, combining dark lull and cold period effects.
Authors: Madhushan Ramalingam, Yaish Riaz, Priyanthi Rajamanoharan, Piyumi Dasanayaka
Diabetic retinopathy (DR) is a major cause of visual impairment, and effective treatment options depend heavily on timely and accurate diagnosis. Deep learning models have demonstrated great success identifying DR from retinal images. However, relying only on predictions made by models, without any indication of model confidence, creates uncertainty and poses significant risk in clinical settings. This paper investigates an alternative in uncertainty-aware deep learning models, including a rejection mechanism to reject low-confidence predictions, contextualized by deferred decision-making in clinical practice. The results show there is a trade-off between prediction coverage and coverage reliability. The Variational Bayesian model adopted a more conservative strategy when predicting DR, subsequently rejecting the uncertain predictions. The model is evaluated by means of important performance metrics such as Accuracy on accepted predictions, the proportion of accepted cases (coverage), the rejection-ratio, and Expected Calibration Error (ECE). The findings also demonstrate a clear trade-off between accuracy and caution, establishing that the use of uncertainty estimation and selective rejection improves the model's reliability in safety-critical diagnostic use cases.
Authors: Lyuzhu Pan, Hongcai Zhang
Electric autonomous mobility-on-demand (EAMoD) systems are emerging all over the world. However, their potential swarm charging in depots may deteriorate operation of the power system, further in turn affecting EAMoD system's optimal operation. To prevent this latent risk, we develop a real-time coordination framework for the EAMoD system and the power system. First, the temporal-spatial characteristics of EAMoD fleets are fully described based on a Markov decision process model, including serving trips, repositioning, and charging. Second, charger accessibility of EAMoD depot charging is well modeled as real-world configuration, wherein fast and slow charge piles are both included. Third, the power system regulation model provides real-time charging regulation constraints for EAMoD systems to prevent potential overload and undervoltage. To address the poor solution quality attributed to the complex decision space of the EAMoD system, this paper proposes a piecewise linear-based approximate dynamic programming algorithm combined with model predictive control. Numerical experiments in the Manhattan and a 14-node power distribution network validate the effectiveness of the proposed algorithm and underscore the necessity of system coordination.
Authors: Lyuzhu Pan, Hongcai Zhang
In the last decade, charging service providers are emerging along with the prevalence of electric vehicles. These providers need to strategically optimize their charging prices to improve the profits considering operation conditions of the coupled power-transportation network. However, the optimal pricing problem generally involves the user equilibrium model, which leads to a mathematical program with equilibrium constraints. As a result, the pricing problem is non-convex and computationally intractable especially for large-scale network. To address this challenge, we propose a generalized sensitivity analysis approach for optimal pricing of electric vehicle charging on coupled power-transportation network. Specifically, we adopt a sensitivity analysis to capture the best response of charging demand to charging price in the gradient form. Consequently, charging service providers can make pricing decisions based on the gradient information instead of the conventional KKT conditions of the user equilibrium model. We then propose a tailored gradient descent algorithm to solve the whole pricing problem. The mathematical proof of validity is given and the time complexity of the proposed algorithm is theoretically polynomial. Numerical experiments on different scales of networks verify the computational efficiency of the proposed algorithm, indicating its potential in evaluating the impact of the optimal pricing on the operational performance of large-scale coupled power-transportation network.
Authors: Pengyu Ren, Wei Sun, Yifan Wang, Gareth Harrison
The rapid expansion of data center infrastructure is reshaping power system dynamics by significantly increasing electricity demand while also offering potential for fast and controllable flexibility. To ensure reliable operation under such conditions, the frequency secured unit commitment problem must be solved with enhanced modeling of demand side frequency response. In this work, we propose a data-driven linearization framework based on decision tree based constraint learning to embed nonlinear nadir frequency constraints into mixed-integer linear programming. This approach enables tractable optimization of generation schedules and fast frequency response from data centers. Through case studies on both a benchmark system and a 2030 future scenario with higher DC penetration, we demonstrate that increasing the proportion of flexible DC load consistently improves system cost efficiency and supports renewable integration. However, this benefit exhibits diminishing marginal returns, motivating the introduction of the Marginal Flexibility Value metric to quantify the economic value of additional flexibility. The results highlight that as DCs become a larger share of system load, their active participation in frequency response will be increasingly indispensable for maintaining both economic and secure system operations.
Authors: Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea Pérez-Toro, Christian Bergler, Andreas Maier, Johann Jäger, Siming Bayer
The increasing integration of distributed energy resources (DERs), particularly renewables, poses significant challenges for power system protection, with fault classification (FC) and fault localization (FL) being among the most critical tasks. Conventional protection schemes, based on fixed thresholds, cannot reliably identify and localize short circuits with the increasing complexity of the grid under dynamic conditions. Machine learning (ML) offers a promising alternative; however, systematic benchmarks across models and settings remain limited. This work presents, for the first time, a comparative benchmarking study of classical ML models for FC and FL in power system protection based on EMT data. Using voltage and current waveforms segmented into sliding windows of 10 ms to 50 ms, we evaluate models under realistic real-time constraints. Performance is assessed in terms of accuracy, robustness to window size, and runtime efficiency. The best-performing FC model achieved an F1 score of 0.992$\pm$0.001, while the top FL model reached an R2 of 0.806$\pm$0.008 with a mean processing time of 0.563 ms.
Authors: Thibaud Cambronne, Samuel Bobick, Wente Zeng, Scott Moura
Demand charge often constitutes a significant portion of electricity costs for commercial electric vehicle (EV) charging station operators. This paper explores control methods to reduce peak power consumption at workplace EV charging stations in a joint price and power optimization framework. We optimize a menu of price options to incentivize users to select controllable charging service. Using this framework, we propose a model predictive control approach to reduce both demand charge and overall operator costs. Through a Monte Carlo simulation, we find that our algorithm outperforms a state-of-the-art benchmark optimization strategy and can significantly reduce station operator costs.
Authors: Ayush Patnaik, Adam B Zufall, Stephen K Robinson, Xinfan Lin
Lithium plating during fast charging is a critical degradation mechanism that accelerates capacity fade and can trigger catastrophic safety failures. Recent work has identified a distinctive dQ/dV peak above 4.0 V as a reliable signature of plating onset; however, conventional methods for computing dQ/dV rely on finite differencing with filtering, which amplifies sensor noise and introduces bias in peak location. In this paper, we propose a Gaussian Process (GP) framework for lithium plating detection by directly modeling the charge-voltage relationship Q(V) as a stochastic process with calibrated uncertainty. Leveraging the property that derivatives of GPs remain GPs, we infer dQ/dV analytically and probabilistically from the posterior, enabling robust detection without ad hoc smoothing. The framework provides three key benefits: (i) noise-aware inference with hyperparameters learned from data, (ii) closed-form derivatives with credible intervals for uncertainty quantification, and (iii) scalability to online variants suitable for embedded BMS. Experimental validation on Li-ion coin cells across a range of C-rates (0.2C-1C) and temperatures (0-40°C) demonstrates that the GP-based method reliably detects plating peaks under low-temperature, high-rate charging, while correctly reporting no peaks in baseline cases. The concurrence of GP-identified differential peaks, reduced charge throughput, and capacity fade measured via reference performance tests confirms the method's accuracy and robustness, establishing a practical pathway for real-time lithium plating detection.
Authors: Viren S. Ram, Tullio de Rubeis, Dario Ambrosini, Rajshekhar Gannavarpu
Diffractive optical element based background oriented schlieren (BOS) is a popular technique for quantitative flow visualization. This technique relies on encoding spatial density variations of the test medium in the form of an optical fringe pattern; and hence, its accuracy is directly influenced by the quality of fringe pattern demodulation. We introduce a robust deep learning assisted subspace method which enables reliable fringe pattern demodulation even in the presence of severe noise and uneven fringe distortions in recorded BOS fringe patterns. The method's effectiveness to handle fringe pattern artifacts is demonstrated via rigorous numerical simulations. Furthermore, the method's practical applicability is experimentally validated using real-world BOS images obtained from a liquid diffusion process.
Authors: Yanan Du, Sai Xu, Jagmohan Chauhan
This paper develops a graph-based hybrid beamforming framework for multiple-input multiple-output (MIMO) cell-free integrated sensing and communication (ISAC) networks. Specifically, we construct a novel MIMO cell-free ISAC network model. In this model, multiple dual-function base station (BS) transmitters employ distributed hybrid beamforming to enable simultaneous communication and sensing, while maintaining physical separation between the transmitters and the radar receiver. Building on this model, we formulate a multi-objective optimization problem under a power constraint to jointly improve communication and sensing performance. To solve it, the problem is first reformulated as a single-objective optimization problem. Then, a graph-based method composed of multiple graph neural networks (GNNs) is developed to realize hybrid beamforming with either perfect or imperfect channel state information. Once trained, the neural network model can be deployed distributively across BSs, enabling fast and efficient inference. To further reduce inference latency, a custom field-programmable gate array (FPGA)-based accelerator is developed. Numerical simulations validate the communication and sensing capabilities of the proposed optimization approach, while experimental evaluations demonstrate remarkable performance gains of FPGA-based acceleration in GNN inference.
Authors: Tony Kinchen, Ting Bai, Nishanth Venkatesh S., Andreas A. Malikopoulos
Urban traffic anomalies such as collisions and disruptions threaten the safety, efficiency, and sustainability of transportation systems. We present a simulation-based framework for modeling, detecting, and predicting such anomalies in urban networks. Using the SUMO platform, we generate reproducible rear-end and intersection crash scenarios with matched baselines, enabling controlled experimentation and comparative evaluation. We record vehicle-level travel time, speed, and emissions for edge and network-level analysis. On this dataset, we develop a hybrid forecasting architecture that combines bidirectional long short-term memory networks with a diffusion convolutional recurrent neural network to capture temporal dynamics and spatial dependencies. Our simulation studies on the Broadway corridor in New York City demonstrate the framework's ability to reproduce consistent incident conditions, quantify their effects, and provide accurate multi-horizon traffic forecasts. Our results highlight the value of combining controlled anomaly generation with deep predictive models to support reproducible evaluation and sustainable traffic management.
Authors: Valdemar Farré, David Vega, Juan Estrada, Juan A. Vásquez Peralvo, Symeon Chatzinotas
The proliferation of cell-free Massive MIMO represents a transformative shift in wireless network architecture, addressing critical limitations of conventional distributed Massive MIMO systems. This paper presents an intelligent radio network planning framework that bridges legacy 5G infrastructures with future B5G/6G networks through cell-free architectures. By leveraging operational insights from existing 5G deployments, we systematically address coverage optimization, and capacity enhancement. Our scalable framework enables seamless evolution from legacy designs to next-generation cell-free systems. Through extensive simulations in dense urban environments, we demonstrate substantial improvements: 45% spectral efficiency gains, 30% interference reduction, and significantly enhanced uniform coverage. The proposed framework provides network operators with a practical roadmap for transitioning from traditional cellular architectures to demanding B5G/6G requirements while maximizing existing infrastructure investments.
Authors: Hao Luo, Shuaifeng Jiang, Saeed R. Khosravirad, Ahmed Alkhateeb
Deep learning (DL) techniques have demonstrated strong performance in compressing and reconstructing channel state information (CSI) while reducing feedback overhead in massive MIMO systems. A key challenge, however, is their reliance on extensive site-specific training data, whose real-world collection incurs significant overhead and limits scalability across deployment sites. To address this, we propose leveraging site-specific digital twins to assist the training of DL-based CSI compression models. The digital twin integrates an electromagnetic (EM) 3D model of the environment, a hardware model, and ray tracing to produce site-specific synthetic CSI data, allowing DL models to be trained without the need for extensive real-world measurements. We further develop a fidelity analysis framework that decomposes digital twin quality into four key aspects: 3D geometry, material properties, ray tracing, and hardware modeling. We explore how these factors influence the reliability of the data and model performance. To enhance the adaptability to real-world environments, we propose a refinement strategy that incorporates a limited amount of real-world data to fine-tune the DL model pre-trained on the digital twin dataset. Evaluation results show that models trained on site-specific digital twins outperform those trained on generic datasets, with the proposed refinement method effectively enhancing performance using limited real-world data. The simulations also highlight the importance of digital twin fidelity, especially in 3D geometry, ray tracing, and hardware modeling, for improving CSI reconstruction quality. This analysis framework offers valuable insights into the critical fidelity aspects, and facilitates more efficient digital twin development and deployment strategies for various wireless communication tasks.
Authors: Chandan Kumar Sheemar, Jorge Querol, Wali Ullah Khan, Prabhu Thiruvasagam, Sourabh Solanki, Idir Edjekouane, Alejandro Gonzalez-Garrido, Mohammed Al-Ansi, Carla E. Garcia, Symeon Chatzinotas
Satellite systems are expected to be a cornerstone of sixth-generation (6G) networks, providing ubiquitous coverage and supporting a wide range of services across communications, sensing, and positioning, navigation, and timing (PNT). Meeting these demands with current function-specific payload architectures is challenging in terms of cost, spectral use, and sustainability. This survey introduces the framework of multi-functional satellite systems (MFSS), which integrate two or more of these core services into a single payload, enabling resource sharing and functional synergy. A unified taxonomy is proposed, covering joint communications and sensing (JCAS), joint communications and PNT (JCAP), joint sensing and PNT (JSAP), and fully integrated joint communications, sensing, and PNT (JCSAP) systems. The paper reviews the state-of-the-art in each domain, examines existing payload architectures, and outlines cooperative, integrated, and joint design strategies. Key challenges, including waveform co-design, synchronization, interference mitigation, and resource management, are discussed, along with potential solutions and future research directions. By unifying diverse satellite capabilities within a single platform, MFSS can achieve higher spectral efficiency, reduced launch mass and cost, improved energy use, and enhanced service versatility, contributing to the development of sustainable and intelligent non-terrestrial networks (NTNs) for the 6G and beyond space era.
Authors: Nishant Kumar, Shravan Kumar Singh, Nikhil Chander
Deploying vertical bifacial PV modules can play a significant role in agrivoltaics, fencing walls, noise barriers, building integrated photovoltaics (BIPV), solar PV for electric vehicles, and many other applications. This research work presents the performance comparison of vertical bifacial photovoltaic (VBPV) modules facing East-West (E-W) and South-North (S-N) directions. Also, the VBPV modules are compared with vertical and tilted south-facing monofacial PV modules. Six PV modules (monofacial and bifacial) were installed at the rooftop of IIT Bhilai academic building, Raipur (21.16° N, 81.65° E), India, and studied for a year from May 2022 to April 2023. The results show that the E-W facing VBPV module gives two production peaks, one in the morning and another in the evening, as compared to the single notable rise at midday observed for a monofacial module. From a series of experiments, 19 days of data were collected over the one-year period from May 2022 to April 2023, with specific inclusion of important days like solstices and equinoxes. In addition, the energy generation results are compared with PVsyst simulations, while also addressing the limitations of the PVsyst simulation of vertical PV modules. E-W bifacial generation is higher than S-N bifacial and south-facing monofacial modules from February to April. The VBPV modules in E-W and S-N orientations present a promising opportunity for expanding the agrivoltaics sector in tropical and sub-tropical countries, like India. This has huge implications for addressing the sustainable development goals by simultaneously contributing to sustainable land management, green energy generation, energy security and water conservation in the vast geo-climatic expanse of tropics.
Authors: Yonatan Gizachew Achamyeleh, Yang Xiang, Yun-Ping Hsiao, Yasamin Moghaddas, Mohammad Abdullah Al Faruque
The growing complexity of global supply chains has made hardware Trojans a significant threat in sensor-based power electronics. Traditional Trojan designs depend on digital triggers or fixed threshold conditions that can be detected during standard testing. In contrast, we introduce Environmental Rate Manipulation (ERM), a novel Trojan triggering mechanism that activates by monitoring the rate of change in environmental parameters rather than their absolute values. This approach allows the Trojan to remain inactive under normal conditions and evade redundancy and sensor-fusion defenses. We implement a compact 14~$\mu$m$^2$ circuit that measures capacitor charging rates in standard sensor front-ends and disrupts inverter pulse-width modulation PWM signals when a rapid change is induced. Experiments on a commercial Texas Instruments solar inverter demonstrate that ERM can trigger catastrophic driver chip failure. Furthermore, ETAP simulations indicate that a single compromised 100~kW inverter may initiate cascading grid instabilities. The attack's significance extends beyond individual sensors to entire classes of environmental sensing systems common in power electronics, demonstrating fundamental challenges for hardware security.
Authors: Xin Chen, Xiaoyang Wang, Ana Colacelli, Matt Lee, Le Xie
The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, this review aims to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation.
Authors: Rangrang Zheng, Greg Schivley, Matthias Fripp, Michael J. Roberts
Expanding interregional transmission is widely viewed as essential for integrating clean energy into decarbonized power systems. Using the open-source Switch capacity expansion model with detailed representation of existing U.S. generation and transmission infrastructure, solar, wind, and storage resources, and hourly operations, we evaluate the role of transmission across least-cost, socially optimal, and zero-emissions scenarios for 2050. An optimal nationwide plan would more than triple interregional transmission capacity, yet this reduces the cost of a zero-emissions system by only 7% relative to relying on existing transmission, as storage, solar and wind siting, and nuclear generation serve as close substitutes. Regional cost and rent effects vary, with transmission generally favoring wind and hydrogen resources over solar and batteries. Sensitivity analysis shows diminishing returns: one-fifth of the benefits of full expansion can be achieved with one-twelfth of the added capacity, while cost reductions for batteries and hydrogen provide comparable or greater system savings than transmission. Reconductoring -- quadrupling line capacity at half the cost of new builds achieves nearly all the benefits of unconstrained expansion. These results suggest that while substantial transmission expansion is economically justified, a diverse set of flexibility resources can substitute for large-scale grid build-out, and the relative value of transmission is highly contingent on technological and cost developments.
Authors: Yuting Hu, Jinjun Xiong
A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be considered. In this paper, we propose a PF rebuild model based on graph attention networks (GAT) by constructing a new graph based on the real and imaginary parts of voltage at each bus. By comparing with two state-of-the-art PF rebuild models for different standard IEEE power system cases and their modified topology variants, we demonstrate the feasibility of our method. Experimental results show that our proposed model achieves better accuracy for a changing network and can generalize to different networks with less accuracy discount.
Authors: Yi Hu, Zheyuan Cheng
Reliable detection and classification of power system events are critical for maintaining grid stability and situational awareness. Existing approaches often depend on limited labeled datasets, which restricts their ability to generalize to rare or unseen disturbances. This paper proposes a novel framework that integrates generative modeling, sliding-window temporal processing, and decision fusion to achieve robust event detection and classification using synchrophasor data. A variational autoencoder-generative adversarial network is employed to model normal operating conditions, where both reconstruction error and discriminator error are extracted as anomaly indicators. Two complementary decision strategies are developed: a threshold-based rule for computational efficiency and a convex hull-based method for robustness under complex error distributions. These features are organized into spatiotemporal detection and classification matrices through a sliding-window mechanism, and an identification and decision fusion stage integrates the outputs across PMUs. This design enables the framework to identify known events while systematically classifying previously unseen disturbances into a new category, addressing a key limitation of supervised classifiers. Experimental results demonstrate state-of-the-art accuracy, surpassing machine learning, deep learning, and envelope-based baselines. The ability to recognize unknown events further highlights the adaptability and practical value of the proposed approach for wide-area event analysis in modern power systems.
Authors: Ghulam Mohy-ud-din, Yunqi Wang, Rahmat Heidari, Frederik Geth
Addressing the uncertainty introduced by increasing renewable integration is crucial for secure power system operation, yet capturing it while preserving the full nonlinear physics of the grid remains a significant challenge. This paper presents a stochastic security constrained optimal power flow model with chance constraints supporting nonlinear AC power flow equations and non Gaussian uncertainties. We use general polynomial chaos expansion to model arbitrary uncertainties of finite variance, enabling accurate moment computations and robust prediction of system states across diverse operating scenarios. The chance constraints probabilistically limit inequality violations, providing a more flexible representation of controllable variables and the consequent power system operation. Case studies validate the proposed models effectiveness in satisfying operational constraints and capturing uncertainty with high fidelity. Compared to the deterministic formulation, it also uncovers a wider set of unsecure contingencies, highlighting improved uncertainty capture and operational insight.
Authors: Yunpeng Xiao, Hui Guo, Wenqi Wu, Xiuli Wang, Xifan Wang
The capacity market provides economic guidance for generation investment and ensures the adequacy of generation capability for power systems. With the rapidly increasing proportion of renewable energy, the adequacy of flexibility and resilience becomes more crucial for the secure operation of power systems. In this context, this paper incorporates the flexibility and resilience demand into the capacity market by formulating the capacity demand curves for ramping capability, inertia and recovery capabilities besides the generation capability. The guidance on generation investment of the capacity market is also taken into account by solving the generation investment equilibrium among generation companies with a Nash Cournot model employing an equivalent quadratic programming formulation. The overall problem is established as a trilevel game and an iterative algorithm is devised to formulate the capacity demand curves in the upper level based on Genco's investment acquired from the middle and lower levels. The case study further demonstrates that to incorporate flexibility and resilience demand into the capacity market could stimulate proper generation investment and ensure the adequacy of flexibility and resilience in power systems.
Authors: Harshal D. Kaushik, Jingbo Wang, Roshni Anna Jacob, Jie Zhang
For electrifying the transportation sector, deploying a strategically planned and efficient charging infrastructure is essential. This paper presents a two-phase approach for electric vehicle (EV) charger deployment that integrates spatial point-of-interest analysis and maximum coverage optimization over an integrated spatial power grid. Spatial-focused studies in the literature often overlook electrical grid constraints, while grid-focused work frequently considers statistically modeled EV charging demand. To address these gaps, a new framework is proposed that combines spatial network planning with electrical grid considerations. This study approaches EV charger planning from a perspective of the distribution grid, starting with an estimation of EV charging demand and the identification of optimal candidate locations. It ensures that the capacity limits of newly established chargers are maintained within the limits of the power grid. This framework is applied in a test case for the Dallas area, integrating the existing EV charger network with an 8500-bus distribution system for comprehensive planning.
Authors: Afsaneh Mollasalehi, Armin Farhadi
Rising global energy demand from population growth raises concerns about the sustainability of fossil fuels. Consequently, the energy sector has increasingly transitioned to renewable energy sources like solar and wind, which are naturally abundant. However, the periodic and unpredictable nature of these resources pose significant challenges for power system reliability. Accurate forecasting is essential to ensure grid stability and optimize energy management. But due to the high variability in weather conditions which directly affected wind and solar energy, achieving precise predictions remains difficult. Advancements in Artificial Intelligence (AI), particularly in Machine Learning (ML) and Deep Learning (DL), offer promising solutions to improve forecasting accuracy. The study highlights three widely used algorithms for solar and wind energy prediction: Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). These models are capable of learning complex patterns from historical and environmental data, enabling more accurate forecasts and contributing to the enhanced efficiency and reliability of renewable energy systems. This review aims to provide an overview on RF, XGBoost, and LSTM by conducting a comparative analysis across three essential criteria: research prevalence, model complexity, and computational execution time.
Authors: Maya Domeshek, Christoph Graf, Burçin Ünel
Coordinated planning of generation, storage, and transmission more accurately captures the interactions among these three capacity types necessary to meet electricity demand, at least in theory. However, in practice, U.S. system operators typically follow a sequential planning approach: They first determine future generation and storage additions based on an assumed unconstrained (`copper plate') system. Next, they perform dispatch simulations of this projected generation and storage capacity mix on the existing transmission grid to identify transmission constraint violations. These violations indicate the need for transmission upgrades. We describe a multistage, multi-locational planning model that co-optimizes generation, storage, and transmission investments. The model respects reliability constraints as well as state energy and climate policies. We test the two planning approaches using a current stakeholder-informed 20-zone model of the PJM region, developed for the current FERC Order No. 1920 compliance filing process. In our most conservative model specification, we find that the co-optimized approach estimates 67% lower transmission upgrade needs than the sequential model, leading to total system costs that are .6% lower and similar reliability and climate outcomes. Our sensitivities show larger transmission and cost savings and reliability and climate benefits from co-optimized planning.
Authors: Sebastian Otzen, Hannes M. H. Wolf, Christian A. Hans
The increasing decentralization of power systems driven by a large number of renewable energy sources poses challenges in power flow optimization. Partially unknown power line properties can render model-based approaches unsuitable. With increasing deployment of sensors, data-driven methods rise as a promising alternative. They offer the flexibility to adapt to varying grid structures and unknown line properties. In this paper, we propose a novel data-driven representation of nonlinear power flow equations for radial grids based on Willems' Fundamental Lemma. The approach allows for direct integration of input/output data into power flow optimisation, enabling cost minimization and constraint enforcement without requiring explicit knowledge of the electrical properties or the topology of the grid. Moreover, we formulate a convex relaxation to ensure compatibility with state-of-the-art solvers. In a numerical case study, we demonstrate that the novel approach performs similar to state-of-the-art methods, without the need for an explicit system identification step.
Authors: Yang Cui, Peter Pan, Lei He, Sheng Zhao
With the rapid advancement of speech generative models, unauthorized voice cloning poses significant privacy and security risks. Speech watermarking offers a viable solution for tracing sources and preventing misuse. Current watermarking technologies fall mainly into two categories: DSP-based methods and deep learning-based methods. DSP-based methods are efficient but vulnerable to attacks, whereas deep learning-based methods offer robust protection at the expense of significantly higher computational cost. To improve the computational efficiency and enhance the robustness, we propose PKDMark, a lightweight deep learning-based speech watermarking method that leverages progressive knowledge distillation (PKD). Our approach proceeds in two stages: (1) training a high-performance teacher model using an invertible neural network-based architecture, and (2) transferring the teacher's capabilities to a compact student model through progressive knowledge distillation. This process reduces computational costs by 93.6% while maintaining high level of robust performance and imperceptibility. Experimental results demonstrate that our distilled model achieves an average detection F1 score of 99.6% with a PESQ of 4.30 in advanced distortions, enabling efficient speech watermarking for real-time speech synthesis applications.
Authors: Po-Heng Chou
Based on the License-Assisted Access (LAA) small cell architecture, the LAA coexisting with Wi-Fi heterogeneous networks provides LTE mobile users with high bandwidth efficiency as the unlicensed channels are shared among LAA and Wi-Fi. However, LAA and Wi-Fi interfere with each other when both systems use the same unlicensed channel in heterogeneous networks. In such a network, unlicensed band allocation for LAA and Wi-Fi is an important issue that may affect the quality of service (QoS) of both systems significantly. In this paper, we propose an analytical model and conduct simulation experiments to study four allocations for the unlicensed band: unlicensed full allocation (UFA), unlicensed time-division allocation (UTA), and UFA/UTA with buffering mechanism (UFAB and UTAB) for the LAA data packets. We evaluate the performance of these unlicensed band allocation schemes in terms of the acceptance rate of both LAA and Wi-Fi packet data in the LAA buffer queue. Our study provides guidelines for designing the channel occupation phase and the buffer size of the LAA small cell.
Authors: Po-Heng Chou
In this letter, we propose an analytical model and conduct simulation experiments to study listen-before-talk-based unlicensed band allocation with the buffering mechanism for the License-Assisted Access (LAA) packets in the heterogeneous networks. In such a network, unlicensed band allocation for LAA and Wi-Fi is an important issue, which may affect the quality of service for both systems significantly. We evaluate the performance of these unlicensed band allocations in terms of the acceptance rate of both LAA and Wi-Fi packets. This letter provides the guidelines for designing the channel occupation phase and buffer threshold of the LAA systems.
Authors: Dennis Gramlich, Shuhao Yan, Carsten W. Scherer, Christian Ebenbauer%
This article shows that distributionally robust controller synthesis as investigated in \cite{taskesen2024distributionally} can be formulated as a convex linear matrix inequality (LMI) synthesis problem. To this end, we rely on well-established convexification techniques from robust control. The LMI synthesis problem we propose has the advantage that it can be solved efficiently using off-the-shelf semi-definite programming (SDP) solvers. In addition, our formulation exposes the studied distributionally robust controller synthesis problem as an instance of robust $H_2$ synthesis.
Authors: Hongchuan Jia, Fayu Wan (NUIST), Vladimir Mordachev, Jérôme Rossignol (ICB), Glauco Fontagalland, Nour Murad (PIMENT), Blaise Ravelo (NUIST)
An experimental study on magnetic near-field (NF) scanning of printed circuit board (PCB) emission radiation is developed in this paper. The design and installation of the electromagnetic (EM) NF scanner is introduced. The test bed of magnetic NF emission in the microwave frequency range is described. The methodology of the microstrip magnetic NF probe is discussed. The probe calibration process was performed following the IEC 61967-1 NF scanning standard. The NF scanner functioning is tested with passive microstrip circuit square loop probe and device under test (DUT) PCB radiation in the test plan positioned at 1-mm above the ground plane. Based on the standard test with I-shape 50-$\Omega$ transmission line (TL), the calibration process of radiated magnetic field was validated by comparison between HFSS__ simulation and experimentation in very wideband frequency from 0.1-GHz to 3-GHz. Then, a nonstandard TL based DUT was experimented. Accordingly, the cartographies of scanned magnetic NF at two different test frequencies, 2 GHz and 3 GHz, are discussed. The NF scanner is under development for targeting the EMC radiated emission of PCB dedicated to operate in 6G wireless communication.
Authors: Hang Yin, Li Qiao, Yu Ma, Shuo Sun, Kan Li, Zhen Gao, Dusit Niyato
Despite significant advancements in traditional syntactic communications based on Shannon's theory, these methods struggle to meet the requirements of 6G immersive communications, especially under challenging transmission conditions. With the development of generative artificial intelligence (GenAI), progress has been made in reconstructing videos using high-level semantic information. In this paper, we propose a scalable generative video semantic communication framework that extracts and transmits semantic information to achieve high-quality video reconstruction. Specifically, at the transmitter, description and other condition signals (e.g., first frame, sketches, etc.) are extracted from the source video, functioning as text and structural semantics, respectively. At the receiver, the diffusion-based GenAI large models are utilized to fuse the semantics of the multiple modalities for reconstructing the video. Simulation results demonstrate that, at an ultra-low channel bandwidth ratio (CBR), our scheme effectively captures semantic information to reconstruct videos aligned with human perception under different signal-to-noise ratios. Notably, the proposed ``First Frame+Desc." scheme consistently achieves CLIP score exceeding 0.92 at CBR = 0.0057 for SNR > 0 dB. This demonstrates its robust performance even under low SNR conditions.
Authors: Nishant Mehrotra, Sandesh Rao Mattu, Saif Khan Mohammed, Ronny Hadani, Robert Calderbank
Zak-OTFS is modulation scheme where signals are formed in the delay-Doppler (DD) domain, converted to the time domain (DD) for transmission and reception, then returned to the DD domain for processing. We describe how to use the same architecture for radar sensing. The intended delay resolution is $\frac{1}{B}$ where $B$ is the radar bandwidth, and the intended Doppler resolution is $\frac{1}{T}$ where $T$ is the transmission time. We form a radar waveform in the DD domain, illuminate the scattering environment, match filter the return, then correlate with delay and Doppler shifts of the transmitted waveform. This produces an image of the scattering environment, and the radar ambiguity function expresses the blurriness of this image. The possible delay and Doppler shifts generate the continuous Heisenberg-Weyl group which has been widely studied in the theory of radar. We describe how to approach the problem of waveform design, not from the perspective of this continuous group, but from the perspective of a discrete group of delay and Doppler shifts, where the discretization is determined by the intended delay and Doppler resolution of the radar. We describe how to approach the problem of shaping the ambiguity surface through symplectic transformations that normalize our discrete Heisenberg-Weyl group. The complexity of traditional continuous radar signal processing is $\mathcal{O}\big(B^2T^2\big)$. We describe how to reduce this complexity to $\mathcal{O}\big(BT\log T\big)$ by choosing the radar waveform to be a common eigenvector of a maximal commutative subgroup of our discrete Heisenberg-Weyl group. The theory of symplectic transformations also enables defining libraries of optimal radar waveforms with small peak-to-average power ratios.
Authors: Yizhe Zhang, Qiang Chen, Tao Zhou
The emergence of powerful, general-purpose omnimodels capable of processing diverse data modalities has raised a critical question: can these ``jack-of-all-trades'' systems perform on par with highly specialized models in knowledge-intensive domains? This work investigates this question within the high-stakes field of medical image segmentation. We conduct a comparative study analyzing the zero-shot performance of a state-of-the-art omnimodel (Gemini, the ``Nano Banana'' model) against domain-specific deep learning models on three distinct tasks: polyp (endoscopy), retinal vessel (fundus), and breast tumor segmentation (ultrasound). Our study focuses on performance at the extremes by curating subsets of the ``easiest'' and ``hardest'' cases based on the specialist models' accuracy. Our findings reveal a nuanced and task-dependent landscape. For polyp and breast tumor segmentation, specialist models excel on easy samples, but the omnimodel demonstrates greater robustness on hard samples where specialists fail catastrophically. Conversely, for the fine-grained task of retinal vessel segmentation, the specialist model maintains superior performance across both easy and hard cases. Intriguingly, qualitative analysis suggests omnimodels may possess higher sensitivity, identifying subtle anatomical features missed by human annotators. Our results indicate that while current omnimodels are not yet a universal replacement for specialists, their unique strengths suggest a potential complementary role with specialist models, particularly in enhancing robustness on challenging edge cases.
Authors: Pierluigi Poggiolini, Francesco Poletti
The recent progress in low-loss hollow-core fibers allows to speculate on the possibility of building a transatlantic submarine cable that can achieve the goal of 1 Pb/s per direction, leveraging bidirectional transmission, and at the same time drastically increase span length, theoretically to 200km. In this version, we add the analysis of the impact of Rayleigh backscattering.
Authors: Ali Baheri, David Millard, Alireza Vahid
Distributed systems require fusing heterogeneous local probability distributions into a global summary over sparse and unreliable communication networks. Traditional consensus algorithms, which average distributions in Euclidean space, ignore their inherent geometric structure, leading to misleading results. Wasserstein barycenters offer a geometry-aware alternative by minimizing optimal transport costs, but their entropic approximations via the Sinkhorn algorithm typically require centralized coordination. This paper proposes a fully decentralized Sinkhorn algorithm that reformulates the centralized geometric mean as an arithmetic average in the log-domain, enabling approximation through local gossip protocols. Agents exchange log-messages with neighbors, interleaving consensus phases with local updates to mimic centralized iterations without a coordinator. To optimize bandwidth, we integrate event-triggered transmissions and b-bit quantization, providing tunable trade-offs between accuracy and communication while accommodating asynchrony and packet loss. Under mild assumptions, we prove convergence to a neighborhood of the centralized entropic barycenter, with bias linearly dependent on consensus tolerance, trigger threshold, and quantization error. Complexity scales near-linearly with network size. Simulations confirm near-centralized accuracy with significantly fewer messages, across various topologies and conditions.
Authors: Bingsong Bai, Qihang Lu, Wenbing Yang, Zihan Sun, Yueran Hou, Peilei Jia, Songbai Pu, Ruibo Fu, Yingming Gao, Ya Li, Jun Gao
Paralinguistic sounds, like laughter and sighs, are crucial for synthesizing more realistic and engaging speech. However, existing methods typically depend on proprietary datasets, while publicly available resources often suffer from incomplete speech, inaccurate or missing timestamps, and limited real-world relevance. To address these problems, we propose an automated framework for generating large-scale paralinguistic data and apply it to construct the SynParaSpeech dataset. The dataset comprises 6 paralinguistic categories with 118.75 hours of data and precise timestamps, all derived from natural conversational speech. Our contributions lie in introducing the first automated method for constructing large-scale paralinguistic datasets and releasing the SynParaSpeech corpus, which advances speech generation through more natural paralinguistic synthesis and enhances speech understanding by improving paralinguistic event detection. The dataset and audio samples are available at this https URL.
Authors: Xiaohua Zhou, Tianyu Fang, Yijie Mao, Bruno Clerckx
Beyond-diagonal reconfigurable intelligent surface (BD-RIS), which enables advanced wave control through interconnection of RIS elements, are gaining growing recognition as a promising technology for 6G and beyond. However, the enhanced flexibility of BD-RIS in controlling the phase and amplitude of reflected signals comes at the cost of high circuit complexity. In this paper, we propose two novel BD-RIS architectures, namely, the stem-connected RIS and cluster-connected RIS, to explore trade-off between circuit complexity and performance. Specifically, the proposed stem-connected RIS is capable of achieving the same performance as fully-connected RIS while significantly reducing circuit complexity. The proposed cluster-connected RIS offers a unified framework that generalizes existing BD-RIS architectures--including single-connected, fully-connected, group-connected, tree-connected (arrowhead), and forest-connected (arrowhead) RISs--as special cases. This framework enables a much more flexible trade-offs between circuit complexity and system performance than existing ones. Based on the proposed BD-RIS architectures, we introduce a novel and generalized structure-oriented symmetric unitary projection method for designing the scattering matrix across all BD-RIS configurations. This method is effectively applied to solve the sum channel gain maximization problem and other utility-based optimization problems. Numerical results demonstrate that the proposed stem-connected RIS is the simplest architecture that achieves optimal BD-RIS performance, while the cluster-connected RIS further enlarges the performance-complexity trade-off range. Furthermore, the proposed projection-based algorithms demonstrate high efficiency.
Authors: Yuxiao Yi, Qingyao Zhuang, Zhi-Qin John Xu
Pediatric brain tumor segmentation presents unique challenges due to the rarity and heterogeneity of these malignancies, yet remains critical for clinical diagnosis and treatment planning. We propose an ensemble approach integrating nnU-Net, Swin UNETR, and HFF-Net for the BraTS-PED 2025 challenge. Our method incorporates three key extensions: adjustable initialization scales for optimal nnU-Net complexity control, transfer learning from BraTS 2021 pre-trained models to enhance Swin UNETR's generalization on pediatric dataset, and frequency domain decomposition for HFF-Net to separate low-frequency tissue contours from high-frequency texture details. Our final ensemble combines nnU-Net ($\gamma=0.7$), fine-tuned Swin UNETR, and HFF-Net, achieving Dice scores of 72.3% (ET), 95.6% (NET), 68.9% (CC), 89.5% (ED), 92.3% (TC), and 92.3% (WT), respectively.
Authors: Qianqian Li, Hua Li, Shiya Hao, Lintao Li, Xiaoming Dai
This study evaluates the performance of an active reconfigurable intelligent surface (ARIS)-assisted non-orthogonal multiple access (NOMA) system employing low-precision analog-to-digital converters (ADCs). Analytical approximations for the outage probability (OP) are derived, considering residual hardware impairments (RHIs) and imperfect successive interference cancellation (ipSIC). Additionally, we analyze the asymptotic OP, system throughput, and diversity order at high signal-to-noise ratios (SNRs). Simulation results demonstrate that the proposed quantized ARIS-NOMA system outperforms its passive counterpart (PRIS-NOMA), achieving lower OP and higher throughput with reduced transmit power requirements and fewer reflecting elements. Moreover, the outage performance of both quantized ARIS-NOMA and PRIS-NOMA systems demonstrates significant improvement as the number of reflecting elements increases. The negative impacts of low-precision ADCs can be effectively mitigated by optimizing transmit power and scaling the number of reflecting elements.
Authors: Marko Fidanovski, Iván Alexander Morales Sandoval, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Emil Björnson
Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) are emerging as a transformative technology in wireless communications, enabling enhanced performance and quality of service (QoS) of wireless systems in harsh urban environments due to their relatively low cost and advanced signal processing capabilities. Generally, BD-RIS systems are employed to improve robustness, increase achievable rates, and enhance energy efficiency of wireless systems in both direct and indirect ways. The direct way is to produce a favorable propagation environment via the design of optimized scattering matrices, while the indirect way is to reap additional improvements via the design of multiple-input multiple-output (MIMO) beamformers that further exploit the latter "engineered" medium. In this article, the problem of sum-rate maximization via BD-RIS is examined, with a focus on feasibility, namely low-complexity physical implementation, by enforcing reciprocity in the BD-RIS design. We begin by outlining the system model and formulating an optimization problem that aims to enhance the system's sum-rate by designing a symmetric scattering matrix. In particular, the approach leverages a manifold optimization framework, where a penalty term is added to the objective function to ensure that the symmetry constraint is upheld, with reciprocity further enforced by projecting the obtained solution onto a set of feasible scattering matrices. Simulation results demonstrate the effectiveness of the proposed method in outperforming current state-of-the-art (SotA) approaches in terms of sum-rate maximization.
Authors: M.O. Aibinu, A. Shoukat, F.M. Mahomed
The logistic growth model is a classical framework for describing constrained growth phenomena, widely applied in areas such as population dynamics, epidemiology, and resource management. This study presents a generalized extension using Atangana-Baleanu in Caputo sense (ABC)-type fractional derivatives. Proportional time delay is also included, allowing the model to capture memory-dependent and nonlocal dynamics not addressed in classical formulations. Free parameters provide flexibility for modeling complex growth in industrial, medical, and social systems. The Hybrid Sumudu Variational (HSV) method is employed to efficiently obtain semi-analytical solutions. Results highlight the combined effects of fractional order and delay on system behavior. This approach demonstrates the novelty of integrating ABC-type derivatives, proportional delay, and HSV-based solutions for real-world applications.
Authors: Mariana Álvarez, Alexander Alegría, Andrés Rivera, Sebastián Pedersen
This paper presents and discusses a mathematical model inspired by control theory to derive optimal public policies for minimizing costs associated with the reduction and control of criminal activity in a population. Specifically, we analyze the optimal control problem \begin{equation*} \min G(u_1, u_2, u_3) = \int_{0}^{t_{\text{F}}} \left( I(t) - R(t) + \frac{B_1}{2} u_1^2(t) + \frac{B_2}{2} u_2^2(t) + \frac{B_3}{2} u_3^2(t) \right) \, dt. \end{equation*} where $I=I(t)$ and $R=R(t)$ satisfies the system of equations \begin{equation*} \left\{ \begin{aligned} \dot{S} &= \Lambda - (1-u_1)SI - \mu S + ((1+u_3)\gamma_2)I + \rho \Omega R,\\ \dot{I} &= (1-u_1)SI - (\mu + \delta_1)I - ((1+u_2)\gamma_1)I - ((1+u_3)\gamma_2)I + (1-\Omega)\rho R,\\ \dot{R} &= ((1+u_2)\gamma_1)I - (\mu + \delta_2 + \rho)R. \end{aligned} \right. \end{equation*} Our approach assumes that the social and economic effects of criminal behavior can be modeled by a dynamic SIR-type system, which serves as a constraint on a cost functional associated with the strategies implemented by government and law enforcement authorities to reduce criminal behavior. Using optimal control theory, the proposed controls, i.e., preventive policies (such as community and social cohesion programs), are expected to have a significant and positive impact on crime reduction, generating opportunities for the most disadvantaged sectors of Cali society and contributing to long-term security. Given that resources to address this problem are limited, this research aims to determine an optimal combination of public interventions and policies that minimize criminality at the lowest possible economic cost, using an SIR model, tools from variational calculus, and optimal control theory.
Authors: Dazhuan Xu, Han Zhang, Nan Wang
In this paper, we employ the thoughts and methodologies of Shannon's information theory to solve the problem of the optimal radar parameter estimation. Based on a general radar system model, the \textit{a posteriori} probability density function of targets' parameters is derived. Range information (RI) and entropy error (EE) are defined to evaluate the performance. It is proved that acquiring 1 bit of the range information is equivalent to reducing estimation deviation by half. The closed-form approximation for the EE is deduced in all signal-to-noise ratio (SNR) regions, which demonstrates that the EE degenerates to the mean square error (MSE) when the SNR is tending to infinity. Parameter estimation theorem is then proved, which claims that the theoretical RI is achievable. The converse claims that there exists no unbiased estimator whose empirical RI is larger than the theoretical RI. Simulation result demonstrates that the theoretical EE is tighter than the commonly used Cramér-Rao bound and the ZivZakai bound.
Authors: Alberto Maria Nobili, Yuzhen Qin, Carlo Alberto Avizzano, Danielle S. Bassett, Fabio Pasqualetti
Many natural and man-made network systems need to maintain certain patterns, such as working at equilibria or limit cycles, to function properly. Thus, the ability to stabilize such patterns is crucial. Most of the existing studies on stabilization assume that network systems states can be measured online so that feedback control strategies can be used. However, in many real-world scenarios, systems states, e.g., neuronal activity in the brain, are often difficult to measure. In this paper, we take this situation into account and study the stabilization problem of linear network systems with an open-loop control strategy (vibrational control). We derive a graph-theoretic sufficient condition for structural vibrational stabilizability, under which network systems can always be stabilized. We further provide an approach to select the locations in the network for control placement and design corresponding vibrational inputs to stabilize systems that satisfy this condition. Finally, we provide some numerical results that demonstrate the validity of our theoretical findings.
Authors: Kushagra Gupta, Xinjie Liu, Ross Allen, Ufuk Topcu, David Fridovich-Keil
Zero-sum games arise in a wide variety of problems, including robust optimization and adversarial learning. However, algorithms deployed for finding a local Nash equilibrium in these games often converge to non-Nash stationary points. This highlights a key challenge: for any algorithm, the stability properties of its underlying dynamical system can cause non-Nash points to be potential attractors. To overcome this challenge, algorithms must account for subtleties involving the curvatures of players' costs. To this end, we leverage dynamical system theory and develop a second-order algorithm for finding a local Nash equilibrium in the smooth, possibly nonconvex-nonconcave, zero-sum game setting. First, we prove that this novel method guarantees convergence to only local Nash equilibria with an asymptotic local \textit{linear} convergence rate. We then interpret a version of this method as a modified Gauss-Newton algorithm with local \textit{superlinear} convergence to the neighborhood of a point that satisfies first-order local Nash equilibrium conditions. In comparison, current related state-of-the-art methods with similar guarantees do not offer convergence rates in the nonconvex-nonconcave setting. Furthermore, we show that this approach naturally generalizes to settings with convex and potentially coupled constraints while retaining earlier guarantees of convergence to only local (generalized) Nash equilibria. Code for our experiments can be found at this https URL.
Authors: Jakub Poćwiardowski, Mateusz Modrzejewski, Marek S. Tatara
This work introduces the M6(GPT)3 composer system, capable of generating complete, multi-minute musical compositions with complex structures in any time signature, in the MIDI domain from input descriptions in natural language. The system utilizes an autoregressive transformer language model to map natural language prompts to composition parameters in JSON format. The defined structure includes time signature, scales, chord progressions, and valence-arousal values, from which accompaniment, melody, bass, motif, and percussion tracks are created. We propose a genetic algorithm for the generation of melodic elements. The algorithm incorporates mutations with musical significance and a fitness function based on normal distribution and predefined musical feature values. The values adaptively evolve, influenced by emotional parameters and distinct playing styles. The system for generating percussion in any time signature utilises probabilistic methods, including Markov chains. Through both human and objective evaluations, we demonstrate that our music generation approach outperforms baselines on specific, musically meaningful metrics, offering a viable alternative to purely neural network-based systems.
Authors: Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, A K M Mahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional attributes of speech into discrete tokens remains challenging. This process demands acoustic, semantic, and contextual information for precise speech representations. Existing speech representations generally fall into two categories: acoustic tokens from audio codecs and semantic tokens from speech self-supervised learning models. Although recent efforts have unified acoustic and semantic tokens for improved performance, they overlook the crucial role of contextual representation in comprehensive speech modeling. Our empirical investigations reveal that the absence of contextual representations results in elevated Word Error Rate (WER) and Word Information Lost (WIL) scores in speech transcriptions. To address these limitations, we propose two novel distillation approaches: (1) a language model (LM)-guided distillation method that incorporates contextual information, and (2) a combined LM and self-supervised speech model (SM)-guided distillation technique that effectively distills multimodal representations (acoustic, semantic, and contextual) into a comprehensive speech tokenizer, termed DM-Codec. The DM-Codec architecture adopts a streamlined encoder-decoder framework with a Residual Vector Quantizer (RVQ) and incorporates the LM and SM during the training process. Experiments show DM-Codec significantly outperforms state-of-the-art speech tokenization models, reducing WER by up to 13.46%, WIL by 9.82%, and improving speech quality by 5.84% and intelligibility by 1.85% on the LibriSpeech benchmark dataset. Code, samples, and checkpoints are available at this https URL.
Authors: Guanwen Xie, Jingzehua Xu, Ziqi Zhang, Xiangwang Hou, Dongfang Ma, Shuai Zhang, Yong Ren, Dusit Niyato
It is significant to employ multiple autonomous underwater vehicles (AUVs) to execute the underwater target tracking task collaboratively. However, it's pretty challenging to meet various prerequisites utilizing traditional control methods. Therefore, we propose an effective two-stage learning from demonstrations training framework, FISHER, to highlight the adaptability of reinforcement learning (RL) methods in the multi-AUV underwater target tracking task, while addressing its limitations such as extensive requirements for environmental interactions and the challenges in designing reward functions. The first stage utilizes imitation learning (IL) to realize policy improvement and generate offline datasets. To be specific, we introduce multi-agent discriminator-actor-critic based on improvements of the generative adversarial IL algorithm and multi-agent IL optimization objective derived from the Nash equilibrium condition. Then in the second stage, we develop multi-agent independent generalized decision transformer, which analyzes the latent representation to match the future states of high-quality samples rather than reward function, attaining further enhanced policies capable of handling various scenarios. Besides, we propose a simulation to simulation demonstration generation procedure to facilitate the generation of expert demonstrations in underwater environments, which capitalizes on traditional control methods and can easily accomplish the domain transfer to obtain demonstrations. Extensive simulation experiments from multiple scenarios showcase that FISHER possesses strong stability, multi-task performance and capability of generalization.
Authors: Mahdi Saberi, Chi Zhang, Mehmet Akçakaya
Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are susceptible to small adversarial input perturbations, or attacks, resulting in major distortions in the output images. Various strategies have been proposed to reduce the effects of these attacks, but they require retraining and may lower reconstruction quality for non-perturbed/clean inputs. In this work, we propose a novel approach for mitigating adversarial attacks on MRI reconstruction models without any retraining. Based on the idea of cyclic measurement consistency, we devise a novel mitigation objective that is minimized in a small ball around the attack input. Results show that our method substantially reduces the impact of adversarial perturbations across different datasets, attack types/strengths and PD-DL networks, and qualitatively and quantitatively outperforms conventional mitigation methods that involve retraining. We also introduce a practically relevant scenario for small adversarial perturbations that models impulse noise in raw data, which relates to \emph{herringbone artifacts}, and show the applicability of our approach in this setting. Finally, we show our mitigation approach remains effective in two \emph{realistic} extension scenarios: a blind setup, where the attack strength or algorithm is not known to the user; and an adaptive attack setup, where the attacker has full knowledge of the defense strategy.
Authors: Siddharth Chandak
Two-time-scale stochastic approximation algorithms are iterative methods used in applications such as optimization, reinforcement learning, and control. Finite-time analysis of these algorithms has primarily focused on fixed point iterations where both time-scales have contractive mappings. In this work, we broaden the scope of such analyses by considering settings where the slower time-scale has a non-expansive mapping. For such algorithms, the slower time-scale can be viewed as a stochastic inexact Krasnoselskii-Mann iteration. We also study a variant where the faster time-scale has a projection step which leads to non-expansiveness in the slower time-scale. We show that the last-iterate mean square residual error for such algorithms decays at a rate $O(1/k^{1/4-\epsilon})$, where $\epsilon>0$ is arbitrarily small. We further establish almost sure convergence of iterates to the set of fixed points. We demonstrate the applicability of our framework by applying our results to minimax optimization, linear stochastic approximation, and Lagrangian optimization.
Authors: Yihe Wang, Nan Huang, Nadia Mammone, Marco Cecchi, Xiang Zhang
Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face two major challenges: 1) the lack of large-scale EEG-AD datasets for robust representation learning, and 2) the absence of a dedicated deep learning pipeline for subject-level detection, which is more clinically meaningful than the commonly used sample-level detection. To address these gaps, we have curated the world's largest EEG-AD corpus to date, comprising 2,255 subjects. Leveraging this unique data corpus, we propose LEAD, the first large-scale foundation model for EEG analysis in dementia. Our approach provides an innovative framework for subject-level AD detection, including: 1) a comprehensive preprocessing pipeline such as artifact removal, resampling, and filtering, and a newly proposed multi-scale segmentation strategy, 2) a subject-regularized spatio-temporal transformer trained with a novel subject-level cross-entropy loss and an indices group-shuffling algorithm, and 3) AD-guided contrastive pre-training. We pre-train on 12 datasets (3 AD-related and 9 non-AD) and fine-tune/test on 4 AD datasets. Compared with 10 baselines, LEAD consistently obtains superior subject-level detection performance under the challenging subject-independent cross-validation protocol. On the benchmark ADFTD dataset, our model achieves an impressive subject-level Sensitivity of 90.91% under the leave-one-subject-out (LOSO) setting. These results strongly validate the effectiveness of our method for real-world EEG-based AD detection. Source code: this https URL
Authors: Simon Donker van Heel, Rutger-Jan Lange, Bram van Os, Dick van Dijk
Can stochastic gradient methods track a moving target? We address the problem of tracking multivariate time-varying parameters under noisy observations and potential model misspecification. Specifically, we examine implicit and explicit score-driven (ISD and ESD) filters, which update parameter predictions using the gradient of the logarithmic postulated observation density (commonly referred to as the score). For both filter types, we derive novel sufficient conditions that ensure the exponential stability of the filtered parameter path and the existence of a finite mean squared error (MSE) bound relative to the pseudo-true parameter path. Our (non-)asymptotic MSE bounds rely on mild moment conditions on the data-generating process, while our stability results are agnostic about the true process. For the ISD filter, concavity of the postulated log density combined with simple parameter restrictions is sufficient to guarantee stability. In contrast, the ESD filter additionally requires the score to be Lipschitz continuous and the learning rate to be sufficiently small. We validate our theoretical findings through simulation studies, showing that ISD filters outperform ESD filters in terms of accuracy and stability.
Authors: Xinran Liu, Xu Dong, Shenbin Qian, Diptesh Kanojia, Wenwu Wang, Zhenhua Feng
Music-driven dance generation is a challenging task as it requires strict adherence to genre-specific choreography while ensuring physically realistic and precisely synchronized dance sequences with the music's beats and rhythm. Although significant progress has been made in music-conditioned dance generation, most existing methods struggle to convey specific stylistic attributes in generated dance. To bridge this gap, we propose a diffusion-based framework for genre-specific 3D full-body dance generation, conditioned on both music and descriptive text. To effectively incorporate genre information, we develop a text-based control mechanism that maps input prompts, either explicit genre labels or free-form descriptive text, into genre-specific control signals, enabling precise and controllable text-guided generation of genre-consistent dance motions. Furthermore, to enhance the alignment between music and textual conditions, we leverage the features of a music foundation model, facilitating coherent and semantically aligned dance synthesis. Last, to balance the objectives of extracting text-genre information and maintaining high-quality generation results, we propose a novel multi-task optimization strategy. This effectively balances competing factors such as physical realism, spatial accuracy, and text classification, significantly improving the overall quality of the generated sequences. Extensive experimental results obtained on the FineDance and AIST++ datasets demonstrate the superiority of GCDance over the existing state-of-the-art approaches.
Authors: Harish K. Dureppagari, R. Michael Buehrer, Harpreet S. Dhillon
In this paper, we propose a two-stage weighted projection method (TS-WPM) for time-difference-of-arrival (TDOA)-based localization, providing provable improvements in positioning accuracy, particularly under high geometric dilution of precision (GDOP) and low signal-to-noise ratio (SNR) conditions. TS-WPM employs a two-stage iterative refinement approach that dynamically updates both range and position estimates, effectively mitigating residual errors while maintaining computational efficiency. Additionally, we extend TS-WPM to support cooperative localization by leveraging two-way time-of-arrival (TW-TOA) measurements, which enhances positioning accuracy in scenarios with limited anchor availability. To analyze TS-WPM, we derive its error covariance matrix and mean squared error (MSE), establishing conditions for its optimality and robustness. To facilitate rigorous evaluation, we develop a 3rd Generation Partnership Project (3GPP)-compliant analytical framework, incorporating 5G New Radio (NR) physical layer aspects as well as large-scale and small-scale fading. As part of this, we derive a generalized Cramér-Rao lower bound (CRLB) for multipath propagation and introduce a novel non-line-of-sight (NLOS) bias model that accounts for propagation conditions and SNR variations. Our evaluations demonstrate that TS-WPM achieves near-CRLB performance and consistently outperforms state-of-the-art weighted nonlinear least squares (WNLS) in high GDOP and low SNR scenarios. Moreover, cooperative localization with TS-WPM significantly enhances accuracy, especially when an insufficient number of anchors (such as 2) are visible. Finally, we analyze the computational complexity of TS-WPM, showing its balanced trade-off between accuracy and efficiency, making it a scalable solution for real-time localization in next-generation networks.
Authors: Benjamin Wong, Ryan H. Lee, Tyler M. Paine, Santosh Devasia, Ashis G. Banerjee
Ergodic exploration has spawned a lot of interest in mobile robotics due to its ability to design time trajectories that match desired spatial coverage statistics. However, current ergodic approaches are for continuous spaces, which require detailed sensory information at each point and can lead to fractal-like trajectories that cannot be tracked easily. This paper presents a new ergodic approach for graph-based discretization of continuous spaces. It also introduces a new time-discounted ergodicity metric, wherein early visitations of information-rich nodes are weighted more than late visitations. A Markov chain synthesized using a convex program is shown to converge more rapidly to time-discounted ergodicity than the traditional fastest mixing Markov chain. The resultant ergodic traversal method is used within a hierarchical framework for active inspection of confined spaces with the goal of detecting anomalies robustly using SLAM-driven Bayesian hypothesis testing. Experiments on a ground robot show the advantages of this framework over three continuous space ergodic planners as well as greedy and random exploration methods for left-behind foreign object debris detection in a ballast tank.
Authors: Siddharth Chandak, Shaan Ul Haque, Nicholas Bambos
Two-time-scale Stochastic Approximation (SA) is an iterative algorithm with applications in reinforcement learning and optimization. Prior finite time analysis of such algorithms has focused on fixed point iterations with mappings contractive under Euclidean norm. Motivated by applications in reinforcement learning, we give the first mean square bound on non linear two-time-scale SA where the iterations have arbitrary norm contractive mappings and Markovian noise. We show that the mean square error decays at a rate of $O(1/n^{2/3})$ in the general case, and at a rate of $O(1/n)$ in a special case where the slower timescale is noiseless. Our analysis uses the generalized Moreau envelope to handle the arbitrary norm contractions and solutions of Poisson equation to deal with the Markovian noise. By analyzing the SSP Q-Learning algorithm, we give the first $O(1/n)$ bound for an algorithm for asynchronous control of MDPs under the average reward criterion. We also obtain a rate of $O(1/n)$ for Q-Learning with Polyak-averaging and provide an algorithm for learning Generalized Nash Equilibrium (GNE) for strongly monotone games which converges at a rate of $O(1/n^{2/3})$.
Authors: Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano, Luca Laurenti, Alessandro Abate
Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such systems. However, in safety-critical contexts, the use of neural approximations requires formal bounds on their closeness to the underlying system. To address this fundamental challenge, we propose a novel, adaptive, and parallelizable verification method based on certified first-order models. Our approach provides formal error bounds on the neural approximations of dynamical systems, allowing them to be safely employed as surrogates by interpreting the error bound as bounded disturbances acting on the approximated dynamics. We demonstrate the effectiveness and scalability of our method on a range of established benchmarks from the literature, showing that it significantly outperforms the state-of-the-art. Furthermore, we show that our framework can successfully address additional scenarios previously intractable for existing methods - neural network compression and an autoencoder-based deep learning architecture for learning Koopman operators for the purpose of trajectory prediction.
Authors: Han Yin, Yang Xiao, Rohan Kumar Das, Jisheng Bai, Haohe Liu, Wenwu Wang, Mark D Plumbley
Audio generation systems now create very realistic soundscapes that can enhance media production, but also pose potential risks. Several studies have examined deepfakes in speech or singing voice. However, environmental sounds have different characteristics, which may make methods for detecting speech and singing deepfakes less effective for real-world sounds. In addition, existing datasets for environmental sound deepfake detection are limited in scale and audio types. To address this gap, we introduce EnvSDD, the first large-scale curated dataset designed for this task, consisting of 45.25 hours of real and 316.74 hours of fake audio. The test set includes diverse conditions to evaluate the generalizability, such as unseen generation models and unseen datasets. We also propose an audio deepfake detection system, based on a pre-trained audio foundation model. Results on EnvSDD show that our proposed system outperforms the state-of-the-art systems from speech and singing domains.
Authors: Goksenin Yuksel, Marcel van Gerven, Kiki van der Heijden
Although audio foundations models have seen great progress on a wide variety of tasks, their application in real-world acoustic environments with reverberation and noise has been less successful. Moreover, as audio foundation models are typically trained on dry, single-channel audio clips, the inherent spatial nature of real-world sound scenes is overlooked and tasks involving sound localization ruled out. To address these limitations, we propose GRAM: a General-purpose Real-world Audio Model utilizing a multi-channel masked auto-encoder approach to efficiently learn spatial audio representations from high-quality simulated real-world scenes. To evaluate the performance of GRAM and other audio foundation models in real-world sound scenes, we release Nat-HEAR: A naturalistic version of the HEAR benchmark suite comprising a simulated real-world version, as well as two new sound localization tasks. We show that the performance of GRAM surpasses all state-of-the-art self-supervised audio foundation models and speech models on both HEAR and Nat-HEAR, while using only a fraction of the training data. GRAM also showcases state-of-the-art localization performance, surpassing even supervised sound localization approaches, and can be flexibly applied either to a two-channel, binaural sound format or a four-channel, Ambisonics format. Validating GRAM's performance on real-world sound recordings demonstrates robust transfer to real-world scenes. Taken together, GRAM presents a significant advancement towards robust, spatial audio foundation models for real-world applications.
Authors: Wenyu Zhang, Yingxu He, Geyu Lin, Zhuohan Liu, Shuo Sun, Bin Wang, Xunlong Zou, Jeremy H. M. Wong, Qiongqiong Wang, Hardik B. Sailor, Nancy F. Chen, Ai Ti Aw
Audio Large Language Models (AudioLLMs) have achieved strong results in semantic tasks like speech recognition and translation, but remain limited in modeling paralinguistic cues such as emotion. Existing approaches often treat emotion understanding as a classification problem, offering little insight into the underlying rationale behind predictions. In this work, we explore emotion reasoning, a strategy that leverages the generative capabilities of AudioLLMs to enhance emotion recognition by producing semantically aligned, evidence-grounded explanations. To support this in multitask AudioLLMs, we introduce a unified framework combining reasoning-augmented data supervision, dual-encoder architecture, and task-alternating training. This approach enables AudioLLMs to effectively learn different tasks while incorporating emotional reasoning. Experiments on IEMOCAP and MELD show that our approach not only improves emotion prediction accuracy but also enhances the coherence and evidential grounding of the generated responses. Experiments on two out-of-domain datasets demonstrate the generalization capabilities of the resulting model.
Authors: Pooneh Mousavi, Gallil Maimon, Adel Moumen, Darius Petermann, Jiatong Shi, Haibin Wu, Haici Yang, Anastasia Kuznetsova, Artem Ploujnikov, Ricard Marxer, Bhuvana Ramabhadran, Benjamin Elizalde, Loren Lugosch, Jinyu Li, Cem Subakan, Phil Woodland, Minje Kim, Hung-yi Lee, Shinji Watanabe, Yossi Adi, Mirco Ravanelli
Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics while enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks. They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: this https URL.
Authors: Zeyang Song, Shimin Zhang, Yuhong Chou, Jibin Wu, Haizhou Li
Spiking Neural Networks (SNNs), inspired by biological neural mechanisms, represent a promising neuromorphic computing paradigm that offers energy-efficient alternatives to traditional Artificial Neural Networks (ANNs). Despite proven effectiveness, SNN architectures have struggled to achieve competitive performance on large-scale speech processing tasks. Two key challenges hinder progress: (1) the high computational overhead during training caused by multi-timestep spike firing, and (2) the absence of large-scale SNN architectures tailored to speech processing tasks. To overcome the issues, we introduce Input-aware Multi-Level Spikeformer, i.e. IML-Spikeformer, a spiking Transformer architecture specifically designed for large-scale speech processing. Central to our design is the Input-aware Multi-Level Spike (IMLS) mechanism, which simulates multi-timestep spike firing within a single timestep using an adaptive, input-aware thresholding scheme. IML-Spikeformer further integrates a Re-parameterized Spiking Self-Attention (RepSSA) module with a Hierarchical Decay Mask (HDM), forming the HD-RepSSA module. This module enhances the precision of attention maps and enables modeling of multi-scale temporal dependencies in speech signals. Experiments demonstrate that IML-Spikeformer achieves word error rates of 6.0\% on AiShell-1 and 3.4\% on Librispeech-960, comparable to conventional ANN transformers while reducing theoretical inference energy consumption by 4.64$\times$ and 4.32$\times$ respectively. IML-Spikeformer marks an advance of scalable SNN architectures for large-scale speech processing in both task performance and energy efficiency. Our source code and model checkpoints are publicly available at this http URL.
Authors: Riccardo Bussola, Michele Focchi, Giulio Turrisi, Claudio Semini, Luigi Palopoli
Jumping poses a significant challenge for quadruped robots, despite being crucial for many operational scenarios. While optimisation methods exist for controlling such motions, they are often time-consuming and demand extensive knowledge of robot and terrain parameters, making them less robust in real-world scenarios. Reinforcement learning (RL) is emerging as a viable alternative, yet conventional end-to-end approaches lack efficiency in terms of sample complexity, requiring extensive training in simulations, and predictability of the final motion, which makes it difficult to certify the safety of the final motion. To overcome these limitations, this paper introduces a novel guided reinforcement learning approach that leverages physical intuition for efficient and explainable jumping, by combining Bézier curves with a Uniformly Accelerated Rectilinear Motion (UARM) model. Extensive simulation and experimental results clearly demonstrate the advantages of our approach over existing alternatives.
Authors: Pablo Picazo-Martinez, Carlos Barroso-Fernández, Alejandro Calvillo-Fernandez, Milan Groshev, Carlos J. Bernardos, Antonio de la Oliva, Alain Mourad
This paper presents a novel multiband passive sensing system that leverages IEEE 802.11bf Wi-Fi signals for environmental sensing, focusing on both sub-7 GHz and millimeter-wave (mmWave) bands. By combining Channel State Information (CSI) from multiple bands, the system enhances accuracy and reliability in detecting human presence, movement, and activities in indoor environments. Utilizing a novel model, called MILAGRO, the system demonstrates robust performance across different scenarios, including monitoring human presence in workspaces and tracking movement in corridors. Experimental results show high accuracy (95-100%), with improved performance by integrating multiband data. The system also addresses key security concerns associated with passive sensing, proposing measures to mitigate potential risks. This work advances the use of Wi-Fi for passive sensing by reducing reliance on active sensing infrastructure and extending the capabilities of low-cost, non-intrusive environmental monitoring.
Authors: Brennen A. Hill, Mant Koh En Wei, Thangavel Jishnuanandh
Robust coordination is critical for effective decision-making in multi-agent systems, especially under partial observability. A central question in Multi-Agent Reinforcement Learning (MARL) is whether to engineer communication protocols or learn them end-to-end. We investigate this dichotomy using embodied world models. We propose and compare two communication strategies for a cooperative task-allocation problem. The first, Learned Direct Communication (LDC), learns a protocol end-to-end, with agents generating messages and actions concurrently. The second, Intention Communication, uses an engineered inductive bias: a compact, learned world model, the Imagined Trajectory Generation Module (ITGM), to simulate future states. Agents then communicate a summary of this plan. We evaluate these approaches on goal-directed interaction in a grid world, a canonical abstraction for embodied AI problems. Our experiments reveal that while emergent communication is viable in simple settings, the engineered, world model-based approach shows superior performance, sample efficiency, and scalability as complexity increases. These findings advocate for integrating structured, predictive models into MARL agents to enable active, goal-driven coordination.
Authors: Bahar Taşkesen, Dan A. Iancu, Çağıl Koçyiğit, Daniel Kuhn
We study a generalization of the classical discrete-time, Linear-Quadratic-Gaussian (LQG) control problem where the noise distributions affecting the states and observations are unknown and chosen adversarially from divergence-based ambiguity sets centered around a known nominal distribution. For a finite horizon model with Gaussian nominal noise and a structural assumption on the divergence that is satisfied by many examples -- including 2-Wasserstein distance, Kullback-Leibler divergence, moment-based divergences, entropy-regularized optimal transport, or Fisher (score-matching) divergence -- we prove that a control policy that is affine in the observations is optimal and the adversary's corresponding worst-case optimal distribution is Gaussian. When the nominal means are zero (as in the classical LQG model), we show that the adversary should optimally set the distribution's mean to zero and the optimal control policy becomes linear. Moreover, the adversary should optimally ``inflate" the noise by choosing covariance matrices that dominate the nominal covariance in Loewner order. Exploiting these structural properties, we develop a Frank-Wolfe algorithm whose inner step solves standard LQG subproblems via Kalman filtering and dynamic programming and show that the implementation consistently outperforms semidefinite-programming reformulations of the problem. All structural and algorithmic results extend to an infinite-horizon, average-cost formulation, yielding stationary linear policies and a time-invariant Gaussian distribution for the adversary. Lastly, we show that when the divergence is 2-Wasserstein, the entire framework remains valid when the nominal distributions are elliptical rather than Gaussian.
Authors: Yucong Zhang, Juan Liu, Ming Li
Pre-trained foundation models have demonstrated remarkable success in audio, vision and language, yet their potential for general machine signal modeling with arbitrary sampling rates-covering acoustic, vibration, and other industrial sensor data-remains under-explored. In this work, we propose a novel foundation model ECHO that integrates an advanced band-split architecture with frequency positional embeddings, enabling spectral localization across arbitrary sampling configurations. Moreover, the model incorporates sliding patches to support inputs of variable length without padding or cropping, producing a concise embedding that retains both temporal and spectral fidelity and naturally extends to streaming scenarios. We evaluate our method on various kinds of machine signal datasets, including previous DCASE task 2 challenges (2020-2025), and widely-used industrial signal corpora. Experimental results demonstrate consistent state-of-the-art performance in machine signal anomaly detection and fault classification, confirming the effectiveness and generalization capability of the proposed model. We open-sourced ECHO on this https URL.
Authors: Yifan Li, Javad Sohankar, Ji Luo, Jing Li, Yi Su
Quantifying and forecasting individual brain aging trajectories is critical for understanding neurodegenerative disease and the heterogeneity of aging, yet current approaches remain limited. Most models predict chronological age, an imperfect surrogate for biological aging, or generate synthetic MRIs that enhance data diversity but fail to capture subject-specific trajectories. Here, we present BrainPath, a 3D generative framework that learns longitudinal brain aging dynamics during training and, at inference, predicts anatomically faithful MRIs at arbitrary timepoints from a single baseline scan. BrainPath integrates an age calibration loss, a swap learning strategy, and an age perceptual loss to preserve subtle, biologically meaningful variations. Across held-out ADNI and an independent NACC dataset, BrainPath outperforms state-of-the-art reference models in structural similarity (SSIM), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and MRI age-difference accuracy, while capturing realistic and temporally consistent aging patterns. Beyond methodological innovation, BrainPath enables personalized mapping of brain aging, synthetic follow-up scan prediction, and trajectory-based analyses, providing a foundation for precision modeling of brain aging and supporting research into neurodegeneration and aging interventions.
Authors: Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei, Siyu Ren, Yuan Liu, Junhui Hou, Wenping Wang
Level-of-detail (LoD) representation is critical for efficiently modeling and transmitting various types of signals, such as images and 3D shapes. In this work, we propose a novel network architecture that enables LoD signal representation. Our approach builds on a modified Multi-Layer Perceptron (MLP), which inherently operates at a single scale and thus lacks native LoD support. Specifically, we introduce the Tailed Multi-Layer Perceptron (T-MLP), which extends the MLP by attaching an output branch, also called tail, to each hidden layer. Each tail refines the residual between the current prediction and the ground-truth signal, so that the accumulated outputs across layers correspond to the target signals at different LoDs, enabling multi-scale modeling with supervision from only a single-resolution signal. Extensive experiments demonstrate that our T-MLP outperforms existing neural LoD baselines across diverse signal representation tasks.
Authors: Linus Stuhlmann, Michael Alexander Saxer
This study evaluates the performance of three advanced speech encoder models, Wav2Vec 2.0, XLS-R, and Whisper, in speaker identification tasks. By fine-tuning these models and analyzing their layer-wise representations using SVCCA, k-means clustering, and t-SNE visualizations, we found that Wav2Vec 2.0 and XLS-R capture speaker-specific features effectively in their early layers, with fine-tuning improving stability and performance. Whisper showed better performance in deeper layers. Additionally, we determined the optimal number of transformer layers for each model when fine-tuned for speaker identification tasks.
Authors: Junjie Li, Kong Aik Lee, Duc-Tuan Truong, Tianchi Liu, Man-Wai Mak
There are various factors that can influence the performance of speaker recognition systems, such as emotion, language and other speaker-related or context-related variations. Since individual speech frames do not contribute equally to the utterance-level representation, it is essential to estimate the importance or reliability of each frame. The xi-vector model addresses this by assigning different weights to frames based on uncertainty estimation. However, its uncertainty estimation model is implicitly trained through classification loss alone and does not consider the temporal relationships between frames, which may lead to suboptimal supervision. In this paper, we propose an improved architecture, xi+. Compared to xi-vector, xi+ incorporates a temporal attention module to capture frame-level uncertainty in a context-aware manner. In addition, we introduce a novel loss function, Stochastic Variance Loss, which explicitly supervises the learning of uncertainty. Results demonstrate consistent performance improvements of about 10\% on the VoxCeleb1-O set and 11\% on the NIST SRE 2024 evaluation set.
Authors: Shiqi Xu, Lihao Zhang, Yuyang Du, Qun Yang, Soung Chang Liew
Recent progress in robotics has underscored the demand for real-time control in applications such as manufacturing, healthcare, and autonomous systems, where the timely delivery of mission-critical commands under heterogeneous robotic traffic is paramount for operational efficacy and safety. In these scenarios, mission-critical traffic follows a strict deadline-constrained communication pattern: commands must arrive within defined QoS deadlines, otherwise late arrivals can degrade performance or destabilize control this http URL this work, we demonstrate on a real-time SDR platform that CSMA, widely adopted in robotic communications,suffers severe degradation under high robot traffic loads, with contention-induced collisions and delays disrupting the on-time arrival of mission-critical packets. To address this problem, we propose an IEEE 802.11-compatible hybrid TDMA/CSMA protocol that combines TDMA's deterministic slot scheduling with CSMA's adaptability for heterogeneous robot this http URL protocol achieves collision-free, low-latency mission-critical command delivery and IEEE 802.11 compatibility through the synergistic integration of sub-microsecond PTP-based slot synchronization-essential for establishing precise timing for TDMA, a three-session superframe with dynamic TDMA allocation for structured and adaptable traffic management,and beacon-NAV protection to preemptively secure these critical communication sessions from interference. Emulation experiments on real-time SDR testbed and Robot Operating System (ROS) simulation show that the proposed protocol reduces missed-deadline errors by 93% compared to the CSMA baseline. In high-speed robot path-tracking ROS simulations, the protocol lowers Root Mean Square (RMS) trajectory error by up to 90% compared with a CSMA baseline, all while maintaining throughput for non-critical traffic within +-2%.
Authors: Kimiaki Shirahama, Miki Yanobu, Kaduki Yamashita, Miho Ohsaki
One of the key issues in Deep Neural Networks (DNNs) is the black-box nature of their internal feature extraction process. Targeting vision-related domains, this paper focuses on analysing the feature space of a DNN by proposing a decoder that can generate images whose features are guaranteed to closely match a user-specified feature. Owing to this guarantee that is missed in past studies, our decoder allows us to evidence which of various image attributes are encoded into the user-specified feature. Our decoder is implemented as a guided diffusion model that guides the reverse image generation of a pre-trained diffusion model to minimise the Euclidean distance between the feature of a clean image estimated at each step and the user-specified feature. One practical advantage of our decoder is that it can analyse feature spaces of different DNNs with no additional training and run on a single COTS GPU. The experimental results targeting CLIP's image encoder, ResNet-50 and vision transformer demonstrate that images generated by our decoder have features remarkably similar to the user-specified ones and reveal valuable insights into these DNNs' feature spaces.
Authors: Keunwoo Choi, Seungheon Doh, Juhan Nam
We present TalkPlayData 2, a synthetic dataset for multimodal conversational music recommendation generated by an agentic data pipeline. In the proposed pipeline, multiple large language model (LLM) agents are created under various roles with specialized prompts and access to different parts of information, and the chat data is acquired by logging the conversation between the Listener LLM and the Recsys LLM. To cover various conversation scenarios, for each conversation, the Listener LLM is conditioned on a finetuned conversation goal. Finally, all the LLMs are multimodal with audio and images, allowing a simulation of multimodal recommendation and conversation. In the LLM-as-a-judge and subjective evaluation experiments, TalkPlayData 2 achieved the proposed goal in various aspects related to training a generative recommendation model for music. TalkPlayData 2 and its generation code are open-sourced at this https URL.
Authors: Xin Xing, Irmak Karaca, Amir Akhavanrezayat, Samira Badrloo, Quan Dong Nguyen, Mahadevan Subramaniam
We propose MAE-SAM2, a novel foundation model for retinal vascular leakage segmentation on fluorescein angiography images. Due to the small size and dense distribution of the leakage areas, along with the limited availability of labeled clinical data, this presents a significant challenge for segmentation tasks. Our approach integrates a Self-Supervised learning (SSL) strategy, Masked Autoencoder (MAE), with SAM2. In our implementation, we explore different loss functions and conclude a task-specific combined loss. Extensive experiments and ablation studies demonstrate that MAE-SAM2 outperforms several state-of-the-art models, achieving the highest Dice score and Intersection-over-Union (IoU). Compared to the original SAM2, our model achieves a $5\%$ performance improvement, highlighting the promise of foundation models with self-supervised pretraining in clinical imaging tasks.
Authors: Dimitri Jacquemont, Carlo Bosio, Teaya Yang, Ruiqi Zhang, Ozgur Orun, Shuai Li, Reza Alam, Thomas M. Schutzius, Simo A. Makiharju, Mark W. Mueller
Photovoltaic (PV) panels are becoming increasingly widespread in the domain of renewable energy, and thus, small efficiency gains can have massive effects. Anti-reflective and self-cleaning coatings enhance panel performance but degrade over time, requiring periodic reapplication. Uncrewed Aerial Vehicles (UAVs) offer a flexible and autonomous way to apply protective coatings more often and at lower cost compared to traditional manual coating methods. In this letter, we propose a quadcopter-based system, equipped with a liquid dispersion mechanism, designed to automate such tasks. The localization stack only uses onboard sensors, relying on visual-inertial odometry and the relative position of the PV panel detected with respect to the quadcopter. The control relies on a model-based controller that accounts for the ground effect and the mass decrease of the quadcopter during liquid dispersion. We validate the autonomy capabilities of our system through extensive indoor and outdoor experiments.
Authors: Md Mubtasim Ahasan, Rafat Hasan Khan, Tasnim Mohiuddin, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Amin Ahsan Ali, Md Mofijul Islam, A K M Mahbubur Rahman
Speech tokenization enables discrete representation and facilitates speech language modeling. However, existing neural codecs capture low-level acoustic features, overlooking the semantic and contextual cues inherent to human speech. While recent efforts introduced semantic representations from self-supervised speech models or incorporated contextual representations from pre-trained language models, challenges remain in aligning and unifying the semantic and contextual representations. We introduce FuseCodec, which unifies acoustic, semantic, and contextual representations through strong cross-modal alignment and globally informed supervision. We propose three complementary techniques: (i) Latent Representation Fusion, integrating semantic and contextual features directly into the encoder latent space for robust and unified representation learning; (ii) Global Semantic-Contextual Supervision, supervising discrete tokens with globally pooled and broadcasted representations to enhance temporal consistency and cross-modal alignment; and (iii) Temporally Aligned Contextual Supervision, strengthening alignment by dynamically matching contextual and speech tokens within a local window for fine-grained token-level supervision. We further introduce FuseCodec-TTS, demonstrating our methodology's applicability to zero-shot speech synthesis. Empirically, FuseCodec achieves state-of-the-art performance in LibriSpeech, surpassing EnCodec, SpeechTokenizer, and DAC in transcription accuracy, perceptual quality, intelligibility, and speaker similarity. Results highlight the effectiveness of contextually and semantically guided tokenization for speech tokenization and downstream tasks. Code and pretrained models are available at this https URL.
Authors: Feilong Chen, Yijiang Liu, Yi Huang, Hao Wang, Miren Tian, Ya-Qi Yu, Minghui Liao, Jihao Wu
We propose MindVL, a multimodal large language model (MLLMs) trained on Ascend NPUs. The training of state-of-the-art MLLMs is often confined to a limited set of hardware platforms and relies heavily on massive, undisclosed data recipes, which hinders reproducibility and open research. To change the common perception that Ascend hardware is unsuitable for efficient full-stage MLLM training, we introduce MindSpeed-MLLM, a highly efficient training framework that supports stable and high-performance training of large-scale Dense and Mixture-of-Experts (MoE) models on Ascend hardware. Based on this, we provide a systematic and open description of the data production methods and mixing strategies for all training stages. Furthermore, we present MindVL, a data-efficient multimodal large language model trained end-to-end on Ascend NPUs. In addition, we find that averaging weights from checkpoints trained with different sequence lengths is particularly effective and yields further gains when combined with test-time resolution search. Our experiments demonstrate superior data efficiency: MindVL-8B matches the performance of Qwen2.5VL-7B using only 10\% of its training data, while our MoE model, MindVL-671B-A37B, matches Qwen2.5VL-72B using only 3\% of the Qwen2.5VL training data, and achieves comparable performance with other leading multimodal MoE models. Our work provides the community with a valuable hardware alternative, open data recipes, and effective performance-enhancing techniques.
Authors: Wataru Nakata, Yuki Saito, Yota Ueda, Hiroshi Saruwatari
Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, multilingual recordings. We introduce Sidon, a fast, open-source speech restoration model that converts noisy in-the-wild speech into studio-quality speech and scales to dozens of languages. Sidon consists of two models: w2v-BERT 2.0 finetuned feature predictor to cleanse features from noisy speech and vocoder trained to synthesize restored speech from the cleansed features. Sidon achieves restoration performance comparable to Miipher: Google's internal speech restoration model with the aim of dataset cleansing for speech synthesis. Sidon is also computationally efficient, running up to 500 times faster than real time on a single GPU. We further show that training a TTS model using a Sidon-cleansed automatic speech recognition corpus improves the quality of synthetic speech in a zero-shot setting. Code and model are released to facilitate reproducible dataset cleansing for the research community.
Authors: Paris A. Karakasis, Nicholas D. Sidiropoulos
We introduce a novel framework for clustering a collection of tall matrices based on their column spaces, a problem we term Subspace Clustering of Subspaces (SCoS). Unlike traditional subspace clustering methods that assume vectorized data, our formulation directly models each data sample as a matrix and clusters them according to their underlying subspaces. We establish conceptual links to Subspace Clustering and Generalized Canonical Correlation Analysis (GCCA), and clarify key differences that arise in this more general setting. Our approach is based on a Block Term Decomposition (BTD) of a third-order tensor constructed from the input matrices, enabling joint estimation of cluster memberships and partially shared subspaces. We provide the first identifiability results for this formulation and propose scalable optimization algorithms tailored to large datasets. Experiments on real-world hyperspectral imaging datasets demonstrate that our method achieves superior clustering accuracy and robustness, especially under high noise and interference, compared to existing subspace clustering techniques. These results highlight the potential of the proposed framework in challenging high-dimensional applications where structure exists beyond individual data vectors.
Authors: Junjie Xiao, Lu Wang, Xiong Du, Pedro Rodriguez, Zian Qin
Active power oscillations frequently arise in inverter-dominated power systems with multiple converters operating under Virtual Synchronous Generator control, posing risks to system stability and protection coordination. While various mitigation strategies have been proposed, many rely on prior knowledge of system parameters, offer limited damping performance, or involve complex models that lack physical interpretability, making them difficult to apply in practice. To address these challenges, this paper first introduces a physically intuitive RLC equivalent circuit model to explain the root causes of APOs in both stand-alone and grid-connected modes. By mapping inertia, damping, and feeder impedance to capacitive, resistive, and inductive elements, respectively, the model reveals how mismatches among converters lead to inter-unit oscillations characterized by LC resonance. Building on this insight, we propose two mode-specific mitigation strategies: in SA mode, a graph theory based impedance control ensures proportional reactive power sharing and effectively suppresses APOs; and in GC mode, adaptive inertia and damping control with feedforward filtering is designed to reshape transient power dynamics while preserving frequency stability. The proposed methods are validated through extensive simulations and real-time hardware-in-the-loop experiments, demonstrating their effectiveness in suppressing oscillations and enhancing the robustness of multi-converter power systems.
Authors: Mohamad Chehade, Hao Zhu
The rise of distributed energy resources (DERs) is reshaping modern distribution grids, introducing new challenges in attaining voltage stability under dynamic and decentralized operating conditions. This paper presents NEO-Grid, a unified learning-based framework for volt-var optimization (VVO) and volt-var control (VVC) that leverages neural network surrogates for power flow and deep equilibrium models (DEQs) for closed-loop control. Our method replaces traditional linear approximations with piecewise-linear ReLU networks trained to capture the nonlinear relationship between power injections and voltage magnitudes. For control, we model the recursive interaction between voltage and inverter response using DEQs, allowing direct fixed-point computation and efficient training via implicit differentiation. We evaluated NEO-Grid on the IEEE 33-bus system, demonstrating that it significantly improves voltage regulation performance compared to standard linear and heuristic baselines in both optimization and control settings. Our results establish NEO-Grid as a scalable, accurate, and interpretable solution for learning-based voltage regulation in distribution grids.
Authors: D. Roncagliolo (1), M. Gallo (1), D. Kaza (2), F. D'Agostino (1), A. Chiarelli (3), F. Silvestro (1) ((1) Univerisity of Genova, (2) Cetena S.p.A., (3) Fincantieri S.p.A)
The adoption of low-voltage direct current sections within grid architectures is emerging as a promising design option in the naval sector. This paper presents a preliminary comparative assessment of three different grid topologies, using an existing MVAC-LVAC shipboard power system as a reference: a conventional MVAC-LVAC radial distribution with an additional LVDC section, a full LVDC radial distribution and a zonal LVDC distribution. Each architecture includes typical elements such as synchronous generators, propulsion motors, energy storage system units, extra propulsive loads, and pulse power loads. The analysis exploits five key performance indicators: weight, volume, technology readiness level, average system interruption duration index, and pulsed power loads interruption index.
Authors: Luis van Sandbergen
The provision of renewable electricity is the foundation for a sustainable future. To achieve the goal of sustainable renewable energy, Battery Energy Storage Systems (BESS) could play a key role to counteract the intermittency of solar and wind generation power. In order to aid the system, the BESS can simply charge at low wholesale prices and discharge during high prices, which is also called energy arbitrage. However, the real-time execution of energy arbitrage is not straightforward for many companies due to the fundamentally different behavior of storages compared to conventional power plants. In this work, the optimized operation of standalone BESS in the cross-market energy arbitrage business is addressed by describing a generic framework for trading integrated BESS operation, the development of a suitable backtest engine and a specific optimization-based strategy formulation for cross-market optimized BESS operation. In addition, this strategy is tested in a case study with a sensitivity analysis to investigate the influence of forecast uncertainty. The results show that the proposed strategy allows an increment in revenues by taking advantage of the increasing market volatility. Furthermore, the sensitivity analysis shows the robustness of the proposed strategy, as only a moderate portion of revenues will be lost if real forecasts are adopted.
Authors: Rahul K. Gupta
Power distribution networks, especially in North America, are often unbalanced due to the mix of single-, two- and three-phase networks as well as due to the high penetration of single-phase devices at the distribution level such as electric vehicle (EV) chargers and single-phase solar plants. However, the network operator must adhere to the voltage unbalance levels within the limits specified by IEEE, IEC, and NEMA standards for the safety of the equipment as well as the efficiency of the network operation. Existing works have proposed active and reactive power control in the network to minimize imbalances. However, these optimization problems are highly nonlinear and nonconvex due to the inherent non-linearity of unbalanced metrics and power-flow equations. In this work, we propose a linearization approach of unbalance metrics such as voltage unbalance factors (VUF), phase voltage unbalance rate (PVUR), and line voltage unbalance rate (LVUR) using the first order Taylor's approximation. This linearization is then applied to the phase balancing control scheme; it is formulated as a feedback approach where the linearization is updated successively after the active/reactive control setpoint has been actuated and shows improvement in voltage imbalances. We demonstrate the application of the proposed scheme on a standard IEEE benchmark test case, demonstrating its effectiveness.
Authors: Luis van Sandbergen
The provision of renewable electricity is the foundation for a sustainable future. To achieve the goal of sustainable renewable energy, Battery Energy Storage Systems (BESS) could play a key role to counteract the intermittency of solar and wind generation power. In order to aid the system, the BESS can simply charge at low wholesale prices and discharge during high prices, which is also called energy arbitrage. However, the real-time execution of energy arbitrage is not straightforward for many companies due to the fundamentally different behavior of storages compared to conventional power plants. In this work, the optimized operation of standalone BESS in the cross-market energy arbitrage business is addressed by describing a generic framework for trading integrated BESS operation, the development of a suitable backtest engine and a specific optimization-based strategy formulation for cross-market optimized BESS operation. In addition, this strategy is tested in a case study with a sensitivity analysis to investigate the influence of forecast uncertainty. The results show that the proposed strategy allows an increment in revenues by taking advantage of the increasing market volatility. Furthermore, the sensitivity analysis shows the robustness of the proposed strategy, as only a moderate portion of revenues will be lost if real forecasts are adopted.
Authors: Markus Amann, Malte Probst, Raphael Wenzel, Thomas H. Weisswange, Miguel Ángel Sotelo
In interactions between automated vehicles (AVs) and crossing pedestrians, modeling implicit vehicle communication is crucial. In this work, we present a combined prediction and planning approach that allows to consider the influence of the planned vehicle behavior on a pedestrian and predict a pedestrian's reaction. We plan the behavior by solving two consecutive optimal control problems (OCPs) analytically, using variational calculus. We perform a validation step that assesses whether the planned vehicle behavior is adequate to trigger a certain pedestrian reaction, which accounts for the closed-loop characteristics of prediction and planning influencing each other. In this step, we model the influence of the planned vehicle behavior on the pedestrian using a probabilistic behavior acceptance model that returns an estimate for the crossing probability. The probabilistic modeling of the pedestrian reaction facilitates considering the pedestrian's costs, thereby improving cooperative behavior planning. We demonstrate the performance of the proposed approach in simulated vehicle-pedestrian interactions with varying initial settings and highlight the decision making capabilities of the planning approach.
Authors: Yubin Kim, Taehan Kim, Wonjune Kang, Eugene Park, Joonsik Yoon, Dongjae Lee, Xin Liu, Daniel McDuff, Hyeonhoon Lee, Cynthia Breazeal, Hae Won Park
Vocal health plays a crucial role in peoples' lives, significantly impacting their communicative abilities and interactions. However, despite the global prevalence of voice disorders, many lack access to convenient diagnosis and treatment. This paper introduces VocalAgent, an audio large language model (LLM) to address these challenges through vocal health diagnosis. We leverage Qwen-Audio-Chat fine-tuned on three datasets collected in-situ from hospital patients, and present a multifaceted evaluation framework encompassing a safety assessment to mitigate diagnostic biases, cross-lingual performance analysis, and modality ablation studies. VocalAgent demonstrates superior accuracy on voice disorder classification compared to state-of-the-art baselines. Its LLM-based method offers a scalable solution for broader adoption of health diagnostics, while underscoring the importance of ethical and technical validation.
Authors: Milan Marocchi, Matthew Fynn, Kayapanda Mandana, Yue Rong
Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep learning has recently been applied to classify abnormal heart sounds indicative of CVDs using synchronised phonocardiogram (PCG) and electrocardiogram (ECG) signals, as well as multichannel PCG (mPCG). However, state-of-the-art architectures remain underutilised due to the limited availability of synchronised and multichannel datasets. Augmented datasets and pre-trained models provide a pathway to overcome these limitations, enabling transformer-based architectures to be trained effectively. This work combines traditional signal processing with denoising diffusion models, WaveGrad and DiffWave, to create an augmented dataset to fine-tune a Wav2Vec 2.0-based classifier on multimodal and multichannel heart sound datasets. The approach achieves state-of-the-art performance. On the Computing in Cardiology (CinC) 2016 dataset of single channel PCG, accuracy, unweighted average recall (UAR), sensitivity, specificity and Matthew's correlation coefficient (MCC) reach 92.48%, 93.05%, 93.63%, 92.48%, 94.93% and 0.8283, respectively. Using the synchronised PCG and ECG signals of the training-a dataset from CinC, 93.14%, 92.21%, 94.35%, 90.10%, 95.12% and 0.8380 are achieved for accuracy, UAR, sensitivity, specificity and MCC, respectively. Using a wearable vest dataset consisting of mPCG data, the model achieves 77.13% accuracy, 74.25% UAR, 86.47% sensitivity, 62.04% specificity, and 0.5082 MCC. These results demonstrate the effectiveness of transformer-based models for CVD detection when supported by augmented datasets, highlighting their potential to advance multimodal and multichannel heart sound classification.
Authors: Zeyu Xie, Yaoyun Zhang, Xuenan Xu, Yongkang Yin, Chenxing Li, Mengyue Wu, Yuexian Zou
The rapid development of generative audio raises ethical and security concerns stemming from forged data, making deepfake sound detection an important safeguard against the malicious use of such technologies. Although prior studies have explored this task, existing methods largely focus on binary classification and fall short in explaining how manipulations occur, tracing where the sources originated, or generalizing to unseen sources-thereby limiting the explainability and reliability of detection. To address these limitations, we present FakeSound2, a benchmark designed to advance deepfake sound detection beyond binary accuracy. FakeSound2 evaluates models across three dimensions: localization, traceability, and generalization, covering 6 manipulation types and 12 diverse sources. Experimental results show that although current systems achieve high classification accuracy, they struggle to recognize forged pattern distributions and provide reliable explanations. By highlighting these gaps, FakeSound2 establishes a comprehensive benchmark that reveals key challenges and aims to foster robust, explainable, and generalizable approaches for trustworthy audio authentication.
Authors: Dahlia Saba, Dominic Groß
In this article, we investigate small-signal frequency and DC voltage stability of hybrid AC/DC power systems that combine AC and DC transmission, conventional machine- based generation, and converter-interfaced generation. The main contributions of this work are a compact frequency domain representation of hybrid AC/DC systems and associated stability conditions that can be divided into conditions on the individual bus dynamics and conditions on each DC network. The bus- level conditions apply to a wide range of technologies (e.g., synchronous generators, synchronous condensers, grid-forming renewables and energy storage). Moreover, the system-level conditions establish that hybrid AC/DC systems combining a wide range of devices are stable independently of the network topology provided that the frequency response of converters on each DC network is sufficiently coherent relative to the network coupling strength. Additionally, we develop and validate a novel reduced- order damper winding model for multi-machine systems.
Authors: Chiaki Kojima, Yuya Muto, Hikaru Akutsu, Rinnosuke Shima, Yoshihiko Susuki
In regions with heavy snowfall, the living environment is becoming a serious problem due to heavy snow accumulation. A road heating is an electrical device which promotes snow melting by burying a heating cable as a thermal source underground in such regions. When integrating the road heating into power distribution systems, we need to optimize the flow of electric power by appropriately integrating distributed power sources and conventional power distribution equipment. In this paper, we introduce a battery storage to the power distribution system including road heating, and extend the predictive switching control of the systems due to the authors' previous study to the case where battery storage is installed. As a main result, we propose a predictive switching control that utilizes photovoltaic (PV) power generation and surplus power stored in the battery storage effectively, and achieves the reduction of distribution loss, attenuation of voltage fluctuation, and efficient snow melting, simultaneously. We verify the effectiveness of the application of battery storage through numerical simulation using actual time series data of weather conditions and active power of the PV power generation and load.
Authors: Wenchao Liu, Xuhui Zhang, Jinke Ren, Weijie Yuan, Changsheng You, Shuangyang Li
Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is regarded as a key enabler for next-generation wireless systems. However, conventional fixed antenna arrays limit the ability of UAVs to fully exploit their inherent potential. To overcome this limitation, we propose a UAV-enabled ISAC framework equipped with fluid antenna (FA) arrays, where the mobility of antenna elements introduces additional spatial degrees of freedom to simultaneously enhance communication and sensing performance. A multi-objective optimization problem is formulated to maximize the communication rates of multiple users while minimizing the Cramér-Rao bound (CRB) for single-target angle estimation. Due to excessively frequent updates of FA positions may lead to response delays, a three-timescale optimization framework is developed to jointly design transmit beamforming, FA positions, and UAV trajectory based on their characteristics. To solve the non-convexity of the problem, an alternating optimization-based algorithm is developed to obtain a sub-optimal solution. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, validating the effectiveness of integrating the FA technology into the UAV-enabled ISAC systems.
Authors: Peng Liu, Zesong Fei, Meng Hua, Guangji Chen, Xinyi Wang, Ruiqi Liu
Pinching antennas (PAs), a new type of reconfigurable and flexible antenna structures, have recently attracted significant research interest due to their ability to create line-of-sight links and mitigate large-scale path loss. Owing to their potential benefits, integrating PAs into wireless powered mobile edge computing (MEC) systems is regarded as a viable solution to enhance both energy transfer and task offloading efficiency. Unlike prior studies that assume ideal continuous PA placement along waveguides, this paper investigates a practical discrete PA-assisted wireless powered MEC framework, where devices first harvest energy from PA-emitted radio-frequency signals and then adopt a partial offloading mode, allocating part of the harvested energy to local computing and the remainder to uplink offloading. The uplink phase considers both the time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA), each examined under three levels of PA activation flexibility. For each configuration, we formulate a joint optimization problem to maximize the total computational bits and conduct a theoretical performance comparison between the TDMA and NOMA schemes. To address the resulting mixed-integer nonlinear problems, we develop a two-layer algorithm that combines closed-form solutions based on Karush-Kuhn-Tucker (KKT) conditions with a cross-entropy-based learning method. Numerical results validate the superiority of the proposed design in terms of the harvested energy and computation performance, revealing that TDMA and NOMA achieve comparable performance under coarser PA activation levels, whereas finer activation granularity enables TDMA to achieve superior computation performance over NOMA.
Authors: Tianyi Liao, Wei Guo, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief
The fluid antenna system (FAS) is a disruptive tech-nology for future wireless communication networks. This paper considers the joint optimization of beamforming matrices and antenna positions for weighted sum rate (WSR) maximization in fluid antenna (FA)-assisted multiuser multiple-input multiple-output (MU-MIMO) networks, which presents significant chal-lenges due to the strong coupling between beamforming and FA positions, the non-concavity of the WSR objective function, and high computational complexity. To address these challenges, we first propose a novel block coordinate ascent (BCA)-based method that employs matrix fractional programming techniques to reformulate the original complex problem into a more tractable form. Then, we develop a parallel majorization maximization (MM) algorithm capable of optimizing all FA positions simul-taneously. To further reduce computational costs, we propose a decentralized implementation based on the decentralized base-band processing (DBP) architecture. Simulation results demon-strate that our proposed algorithm not only achieves significant WSR improvements over conventional MIMO networks but also outperforms the existing method. Moreover, the decentralized implementation substantially reduces computation time while maintaining similar performance compared with the centralized implementation.
Authors: Wenchao Liu, Xuhui Zhang, Chunjie Wang, Jinke Ren, Weijie Yuan, Changsheng You
Low-altitude wireless networks (LAWNs) are widely regarded as a cornerstone of the emerging low-altitude economy, yet they face significant challenges, including rapidly varying channels, diverse functional requirements, and dynamic interference environments. Fluid antenna (FA) systems introduce spatial reconfigurability that complements and extends conventional beamforming, enabling flexible exploitation of spatial diversity and adaptive response to channel variations. This paper proposes a novel architecture for FA-empowered LAWNs and presents a case study demonstrating substantial improvements in communication, sensing, and control performance compared to fixed-position antenna (FPA) systems. Key practical deployment considerations are examined, including mechanical design, position control, energy efficiency, and compliance with emerging industry standards. In addition, several future research directions are highlighted, including intelligent optimization, multi-function integration, and the exploration of novel low-altitude applications. By integrating theoretical analysis with practical deployment perspectives, this paper establishes FA systems as a key enabler for adaptive, efficient, and resilient network infrastructures in next-generation LAWNs.
Authors: M. Andrecut
In this paper we discuss several surprisingly simple methods for transforming the Raspberry Pi Pico (RP2) microcontroller into a radio transmitter, by using only cheap off the shelf electronic components, and open source software. While initially this transformation may look as a harmless curiosity, in some extreme cases it can also pose security risks, since it can be used to open a large number of local stealth radio communication channels.
Authors: Jingyi Wang, Zhongyuan Zhao, Qingtian Wang, Zexu Li, Yue Wang, Tony Q. S. Quek
Edge intelligence has emerged as a promising strategy to deliver low-latency and ubiquitous services for mobile devices. Recent advances in fine-tuning mechanisms of foundation models have enabled edge intelligence by integrating low-rank adaptation (LoRA) with federated learning. However, in wireless networks, the device heterogeneity and resource constraints on edge devices pose great threats to the performance of federated fine-tuning. To tackle these issues, we propose to optimize federated fine-tuning in heterogenous wireless networks via online learning. First, the framework of switching-based federated fine-tuning in wireless networks is provided. The edge devices switches to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of device heterogeneity and transmission unreliability. Second, a tractable upper bound on the inference risk gap is derived based on theoretical analysis. To improve the generalization capability, we formulate a non-convex mixed-integer programming problem with long-term constraints, and decouple it into model switching, transmit power control, and bandwidth allocation subproblems. An online optimization algorithm is developed to solve the problems with polynomial computational complexity. Finally, the simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.
Authors: Ammar Kamoona, Hui Song, Ali Moradi Amani, Mahdi Jalili, Xinghuo Yu, Peter McTaggart
Electric vehicle (EV) charging loads identification from behind smart meter recordings is an indispensable aspect that enables effective decision-making for energy distributors to reach an informed and intelligent decision about the power grid's reliability. When EV charging happens behind the meter (BTM), the charging occurs on the customer side of the meter, which measures the overall electricity consumption. In other words, the charging of the EV is considered part of the customer's load and not separately measured by the Distribution Network Operators (DNOs). DNOs require complete knowledge about the EV presence in their network. Identifying the EV charging demand is essential to better plan and manage the distribution grid. Unlike supervised methods, this paper addresses the problem of EV charging load identification in a non-nonintrusive manner from low-frequency smart meter using an unsupervised learning approach based on anomaly detection technique. Our approach does not require prior knowledge of EV charging profiles. It only requires real power consumption data of non-EV users, which are abundant in practice. We propose a deep temporal convolution encoding decoding (TAE) network. The TAE is applied to power consumption from smart BTM from Victorian households in Australia, and the TAE shows superior performance in identifying households with EVs.
Authors: Oscar A. Oviedo
This study presents the development and optimization of a deep learning model based on Long Short-Term Memory (LSTM) networks to predict short-term hourly electricity demand in Córdoba, Argentina. Integrating historical consumption data with exogenous variables (climatic factors, temporal cycles, and demographic statistics), the model achieved high predictive precision, with a mean absolute percentage error of 3.20\% and a determination coefficient of 0.95. The inclusion of periodic temporal encodings and weather variables proved crucial to capture seasonal patterns and extreme consumption events, enhancing the robustness and generalizability of the model. In addition to the design and hyperparameter optimization of the LSTM architecture, two complementary analyses were carried out: (i) an interpretability study using Random Forest regression to quantify the relative importance of exogenous drivers, and (ii) an evaluation of model performance in predicting the timing of daily demand maxima and minima, achieving exact-hour accuracy in more than two-thirds of the test days and within abs(1) hour in over 90\% of cases. Together, these results highlight both the predictive accuracy and operational relevance of the proposed framework, providing valuable insights for grid operators seeking optimized planning and control strategies under diverse demand scenarios.
Authors: Yifan Dong, Ge Chen, Junjie Qin
This paper proposes a federated framework for demand flexibility aggregation to support grid operations. Unlike existing geometric methods that rely on a static, pre-defined base set as the geometric template for aggregation, our framework establishes a true federated process by enabling the collaborative optimization of this base set without requiring the participants sharing sensitive data with the aggregator. Specifically, we first formulate the base set optimization problem as a bilevel program. Using optimal solution functions, we then reformulate the bilevel program into a single-level, unconstrained learning task. By exploiting the decomposable structure of the overall gradient, we further design a decentralized gradient-based algorithm to solve this learning task. The entire framework, encompassing base set optimization, aggregation, and disaggregation, operates by design without exchanging raw user data. Numerical results demonstrate that our proposed framework unlocks substantially more flexibility than the approaches with static base sets, thus providing a promising framework for efficient and privacy-enhanced approaches to coordinate demand flexibility at scale.
Authors: Shishir Lamichhane, Abodh Poudyal, Nicholas R. Jones, Bala Krishnamoorthy, Anamika Dubey
This paper proposes a Separable Projective Approximation Routine-Optimal Power Flow (SPAR-OPF) framework for solving two-stage stochastic optimization problems in power systems. The framework utilizes a separable piecewise linear approximation of the value function and learns the function based on sample sub-gradient information. We present two formulations to model the learned value function, and compare their effectiveness. Additionally, an efficient statistical method is introduced to assess the quality of the obtained solutions. The effectiveness of the proposed framework is validated using distributed generation siting and sizing problem in three-phase unbalanced power distribution systems as an example. Results show that the framework approximates the value function with over 98% accuracy and provides high-quality solutions with an optimality gap of less than 1%. The framework scales efficiently with system size, generating high-quality solutions in a short time when applied to a 9500-node distribution system with 1200 scenarios, while the extensive formulations and progressive hedging failed to solve the problem.
Authors: Siying Li, Lang Tong, Timothy D. Mount
We develop an energy management system (EMS) for artificial intelligence (AI) data centers with colocated renewable generation. Under a cost-minimizing framework, the EMS of renewable-colocated data center (RCDC) co-optimizes AI workload scheduling, on-site renewable utilization, and electricity market participation. Within both wholesale and retail market participation models, the economic benefit of the RCDC operation is maximized. Empirical evaluations using real-world traces of electricity prices, data center power consumption, and renewable generation demonstrate significant electricity cost reduction from renewable and AI data center colocations.
Authors: Shishir Lamichhane, Anamika Dubey
With the integration of renewable energy resources in power systems, managing operational flexibility and reliability while minimizing operational costs has become increasingly challenging. Battery energy storage system (BESS) offers a promising solution to address these issues. This paper presents a stochastic dynamic economic dispatch with storage (SDED-S) framework to assess the impact of BESS in managing uncertainty. The temporal correlation between wind and load uncertainties is captured, with scenarios generated using a method inspired by stratified and importance sampling. The proposed approach is demonstrated on a modified IEEE 39-bus system, where selected conventional generators are converted to wind power plants. Case studies show that strategic BESS deployment significantly improves system flexibility by reducing renewable curtailments and dispatch costs. Renewable energy curtailments decrease upon increasing BESS size and approach zero depending on wind penetration level. Higher wind penetrations result in greater curtailments without storage and yield larger cost savings when BESS is deployed, highlighting the growing need for flexibility as renewable energy penetrations increase.
Authors: Hadi Nemati, Pedro Sánchez-Martín, Álvaro Ortega, Lukas Sigrist, Luis Rouco
This paper proposes the integration of Concentrated Solar Power Plant (CSP) in the Renewable-only virtual power plant (RVPP) for bidding in the electricity day-ahead and secondary reserve markets, as well as trading thermal energy through a heat purchase agreement. A reformulated two-stage robust optimization approach is introduced to account for multiple uncertainties, including electricity prices, non-dispatchable renewable energy sources electrical production, CSP thermal production, and uncertainties in electrical and thermal demand consumption. The provision of energy and reserve by the thermal storage of CSP is modeled using an adjustable approach, which allocates a share of energy for up and down reserves based on the profitability of the RVPP. Simulations are conducted for several case studies to demonstrate the effectiveness and computational efficiency of the proposed approach under different RVPP operator decisions against uncertain parameters and various trading strategies for electricity and thermal energy. The simulation results show that integrating CSP into RVPP enhances RVPP flexibility for both electrical and thermal trading. Furthermore, the results indicate that the profitability of the RVPP increases when all trading options are considered, across different levels of conservatism adopted by the RVPP operator in response to uncertain parameters.
Authors: Yiqiao Xu, Quan Wan, Alessandra Parisio
To address the variability of renewable generation, initiatives have been launched globally to provide faster and more effective frequency responses. In the UK, the National Energy System Operator (NESO) has introduced a suite of three new dynamic services, where aggregation of assets is expected to play a key role. For an Aggregated Response Unit (ARU), the required level of frequency response varies with grid frequency, resulting in a frequency-varying equality constraint that assets should meet collectively. We show that the optimal coordination of an ARU constitutes a Frequency-Varying Optimization (FVO) problem, in which the optimal trajectory for each asset evolves dynamically. To facilitate online optimization, we reformulate the FVO problem into Tracking of the Optimal Trajectory (TOT) problems, with algorithms proposed for two scenarios: one where the asset dynamics are negligible, and another where they must be accounted for. Under reasonable conditions, the ARU converges to the optimal trajectory within a fixed time, and within the maximum delivery time requested by NESO. The proposed framework can be readily distributed to coordinate a large number of assets. Numerical results verify the effectiveness and scalability of the proposed control framework.
Authors: Ahmed S. Alahmed, Audun Botterud, Saurabh Amin, Ali T. Al-Awami
We develop a mathematical framework to jointly schedule water and electricity in a profit-maximizing renewable colocated water desalination plant that integrates both thermal and membrane based technologies. The price-taking desalination plant sells desalinated water to a water utility at a given price and engages in bidirectional electricity transactions with the grid, purchasing or selling power based on its net electricity demand. We show that the optimal scheduling policy depends on the plant's internal renewable generation and follows a simple threshold structure. Under the optimal policy, thermal based water output decreases monotonically with renewable output, while membrane based water output increases monotonically. We characterize the structure and intuition behind the threshold policy and examine key special properties.
Authors: Peilan Wang, Yu Xue, Weidong Mei, Jun Fang, Rui Zhang
Improving the fundamental performance trade-off in integrated sensing and communication (ISAC) systems has been deemed as one of the most significant challenges. To address it, we propose in this letter a novel ISAC system that leverages an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) and the UAV's maneuverability in six-dimensional (6D) space, i.e., three-dimensional (3D) location and 3D rotation, thus referred to as passive 6D movable antenna (6DMA). We aim to maximize the signal-to-noise ratio (SNR) for sensing a single target while ensuring a minimum SNR at a communication user equipment (UE), by jointly optimizing the transmit beamforming at the ISAC base station (BS), the 3D location and orientation as well as the reflection coefficients of the IRS. To solve this challenging non-convex optimization problem, we propose a two-stage approach. In the first stage, we aim to optimize the IRS's 3D location, 3D orientation, and reflection coefficients to enhance both the channel correlations and power gains for sensing and communication. Given their optimized parameters, the optimal transmit beamforming at the ISAC BS is derived in closed form. Simulation results demonstrate that the proposed passive 6DMA-enabled ISAC system significantly improves the sensing and communication trade-off by simultaneously enhancing channel correlations and power gains, and outperforms other baseline schemes.
Authors: Jun He, Andrew L. Liu, Yihsu Chen
Designing socially optimal policies in multi-agent environments is a fundamental challenge in both economics and artificial intelligence. This paper studies a general framework for learning Stackelberg equilibria in dynamic and uncertain environments, where a single leader interacts with a population of adaptive followers. Motivated by pressing real-world challenges such as equitable electricity tariff design for consumers with distributed energy resources (such as rooftop solar and energy storage), we formalize a class of Stackelberg Markov games and establish the existence and uniqueness of stationary Stackelberg equilibria under mild continuity and monotonicity conditions. We then extend the framework to incorporate a continuum of agents via mean-field approximation, yielding a tractable Stackelberg-Mean Field Equilibrium (S-MFE) formulation. To address the computational intractability of exact best-response dynamics, we introduce a softmax-based approximation and rigorously bound its error relative to the true Stackelberg equilibrium. Our approach enables scalable and stable learning through policy iteration without requiring full knowledge of follower objectives. We validate the framework on an energy market simulation, where a public utility or a state utility commission sets time-varying rates for a heterogeneous population of prosumers. Our results demonstrate that learned policies can simultaneously achieve economic efficiency, equity across income groups, and stability in energy systems. This work demonstrates how game-theoretic learning frameworks can support data-driven policy design in large-scale strategic environments, with applications to real-world systems like energy markets.
Authors: Seyyedali Hosseinalipour, Shimiao Li, Adedoyin Inaolaji, Filippo Malandra, Luis Herrera, Nicholas Mastronarde
The recent emergence of large language models (LLMs) such as GPT-3 has marked a significant paradigm shift in machine learning. Trained on massive corpora of data, these models demonstrate remarkable capabilities in language understanding, generation, summarization, and reasoning, transforming how intelligent systems process and interact with human language. Although LLMs may still seem like a recent breakthrough, the field is already witnessing the rise of a new and more general category: multi-modal, multi-task foundation models (M3T FMs). These models go beyond language and can process heterogeneous data types/modalities, such as time-series measurements, audio, imagery, tabular records, and unstructured logs, while supporting a broad range of downstream tasks spanning forecasting, classification, control, and retrieval. When combined with federated learning (FL), they give rise to M3T Federated Foundation Models (FedFMs): a highly recent and largely unexplored class of models that enable scalable, privacy-preserving model training/fine-tuning across distributed data sources. In this paper, we take one of the first steps toward introducing these models to the power systems research community by offering a bidirectional perspective: (i) M3T FedFMs for smart grids and (ii) smart grids for FedFMs. In the former, we explore how M3T FedFMs can enhance key grid functions, such as load/demand forecasting and fault detection, by learning from distributed, heterogeneous data available at the grid edge in a privacy-preserving manner. In the latter, we investigate how the constraints and structure of smart grids, spanning energy, communication, and regulatory dimensions, shape the design, training, and deployment of M3T FedFMs.
Authors: Salim Oyinlola, Peter Olabisi Oluseyi
The growing demand for reliable electricity in universities necessitates intelligent energy management. This study proposes a machine learning-based load shedding framework for the University of Lagos, designed to optimize distribution and reduce waste. The methodology followed three main stages. First, a dataset of 3,648 hourly records from 55 buildings was compiled to develop building-level consumption models. Second, Principal Component Analysis was applied for dimensionality reduction, and clustering validation techniques were used to determine the optimal number of demand groups. Mini-Batch K-Means was then employed to classify buildings into high-, medium-, and low-demand clusters. Finally, short-term load forecasting was performed at the cluster level using multiple statistical and deep learning models, including ARIMA, SARIMA, Prophet, LSTM, and GRU. Results showed Prophet offered the most reliable forecasts, while Mini-Batch K-Means achieved stable clustering performance. By integrating clustering with forecasting, the framework enabled a fairer, data-driven load shedding strategy that reduces inefficiencies and supports climate change mitigation through sustainable energy management.
Authors: Saman Mehrnia, Hui Song, Nameer Al Khafaf, Mahdi Jalili, Lasantha Meegahapola, Brendan McGrath
The uptake of battery electric vehicles (BEVs) is increasing to reduce greenhouse gas emissions in the transport sector. The rapid adoption of BEVs depends significantly on the coordinated charging/discharging infrastructure. Without it, uncontrolled and erratic charging patterns could lead to increased power losses and voltage fluctuations beyond acceptable thresholds. BEV charge scheduling presents a multi-objective optimization (MOO) challenge, demanding a balance between minimizing network impact and maximizing the benefits for electric vehicle charging station (EVCS) operators and BEV owners. In this paper, we develop an MOO framework incorporating a carbon emission program and a dynamic economic dispatch problem, allowing BEV users to respond by charging and discharging through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies according to the optimal electricity price and compensation. Furthermore, we integrate dynamic economic dispatch with time-of-use tariffs to obtain optimal market electricity prices and reduce total costs over 24 hours. Our experimental results on a sample network show that the proposed scheduling increases participation in V2G services by over 10%, increases EVCS benefits by over 20%, and reduces network losses. Furthermore, increased rates of charging/discharging, coupled with more significant carbon revenue benefits for BEV users and EVCS, contribute to better offsetting battery degradation costs.
Authors: Zhenghua Xu, Dominic Gross, George Alin Raducu, Hesam Khazraj, Nicolaos A. Cutululis
HVDC-connected offshore wind power plants (OWPPs) are expected to provide inertial response and frequency containment reserve (FCR) to help address the frequency control challenges caused by the growing penetration of power electronics in power systems. Initially dominated by communication-based and grid-following (GFL) control, recent efforts have shifted towards incorporating communication-free and grid-forming (GFM) control into HVDC-OWPP systems to enhance their frequency response capability. This paper proposes a holistic GFM control based on dual-port GFM control to improve the coordination across the entire AC-DC-AC dynamics. A frequency response model of a typical HVDC-OWPP system is developed for GFM control design. Then, dual-port GFM control and virtual synchronous generator control are implemented respectively on the HVDC system and OWPP of the typical system, where the asynchronism of onshore and offshore frequencies is revealed. Next, holistic GFM control is proposed to improve the synchronization and DC voltage regulation. Finally, simulations on the delivery of FCR and inertial response are carried out to verify the feasibility and effectiveness of the proposed control.
Authors: Paul Bannmüller, Périne Cunat, Ali Rajaei, Jochen Cremer
The ongoing energy transition places significant pressure on the transmission network due to increasing shares of renewables and electrification. To mitigate grid congestion, transmission system operators need decision support tools to suggest remedial actions, such as transmission network reconfigurations or redispatch. However, these tools are prone to model inaccuracies and may not provide relevant suggestions with regard to important unmodeled constraints or operator preferences. We propose a human-in-the-loop modeling-to-generate alternatives (HITL-MGA) approach to address these shortcomings by generating diverse topology reconfiguration alternatives. Case studies on the IEEE 57-bus and IEEE 118-bus systems show the method can leverage expert feedback and improve the quality of the suggested remedial actions.
Authors: Kevin Wilkinghoff, Takuya Fujimura, Keisuke Imoto, Jonathan Le Roux, Zheng-Hua Tan, Tomoki Toda
When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, while many practical applications also require them to be insensitive to changes in acoustic domains. Examples of such domain shifts include changing the type of microphone or the location of acoustic sensors, which can have a much stronger impact on the acoustic signal than subtle anomalies themselves. Moreover, users typically aim to train a model only on source domain data, which they may have a relatively large collection of, and they hope that such a trained model will be able to generalize well to an unseen target domain by providing only a minimal number of samples to characterize the acoustic signals in that domain. In this work, we review and discuss recent publications focusing on this domain generalization problem for anomalous sound detection in the context of the DCASE challenges on acoustic machine condition monitoring.
Authors: Heyu Guo, Ruiyi Shen, Florian Kosterhon, Yasaman Ghasempour
The integration of communication and sensing functions within mmWave systems has gained attention due to the potential for enhanced passive sensing and improved communication reliability. State-of-the-art techniques separate these two functions in frequency, use of hardware, or time, i.e., sending known preambles for channel sensing or unknown symbols for communications. In this paper, we introduce Panoptic, a novel system architecture for integrated communication and sensing sharing the same hardware, frequency, and time resources. Panoptic jointly detects unknown symbols and channel components from data-modulated signals. The core idea is a new beam manipulation technique, which we call compressive sidelobe forming, that maintains a directional mainlobe toward the intended communication nodes while acquiring unique spatial information through pseudorandom sidelobe perturbations. We implemented Panoptic on 60 GHz mmWave radios and conducted extensive over-the-air experiments. Our results show that Panoptic achieves reflector angular localization error of less than 2°while at the same time supporting mmWave data communication with a negligible BER penalty when compared with conventional communication-only mmWave systems.
Authors: Yu Kawano, Zhiyong Sun
In this paper, we exploit a diagonally dominant structure for the decentralized stabilization of unknown nonlinear time-delayed networks. To this end, we first introduce a novel generalization of virtual contraction analysis to diagonally dominant time-delayed control systems. We then show that nonlinear time-delayed networks can be stabilized using diagonal high-gains, provided that the input matrices satisfy certain generalized (column/row) diagonally dominant conditions. To enable stabilization of unknown networks, we further propose a distributed adaptive tuning rule for each individual gain function, guaranteeing that all closed-loop trajectories converge to the origin while the gains converge to finite values. The effectiveness of the proposed decentralized adaptive control is illustrated through a case study on epidemic spreading control in SIS networks with transmission delays.
Authors: Shiyong Chen, Shengqian Han
Holographic multiple-input multiple-output (HMIMO) has emerged as a potential technique for enhancing spectral efficiency (SE) while maintaining low hardware costs and power consumption. Conventional alternating optimization algorithms have been widely employed to design HMIMO beamforming, but their high computational complexity hinders real-time deployment. Graph neural networks (GNNs) provide a learning-based alternative with low inference time. However, in HMIMO systems, the fixed phase pattern, which captures the distribution of reference-wave phases across the holographic surface disrupts the permutation equivariance (PE) property, which is essential for GNN design. To address this issue, we reformulate the beamforming problem into a PE-compliant equivalent form and propose a novel network architecture consisting of a gradient-based GNN (GGNN) followed by two projection modules. Simulation results demonstrate that the proposed method achieves higher SE with significantly reduced inference time than alternating optimization methods and exhibits superior generalizability compared to other learning-based baselines.
Authors: Nafize Ishtiaque Hossain, Kundan Saha, Atul Sharma, Sameer Sonkusale
We report a scalable, self-powered in planta sensor platform for continuous monitoring of plant hydration and growth. The system integrates two components a leaf mounted tattoo sensor for estimating vapor pressure deficit and a kirigami inspired strain sensor for tracking radial stem growth. Uniquely, the tattoo sensor serves a dual function measuring temperature and humidity beneath the leaf surface while simultaneously harvesting power from ambient moisture via a vanadium pentoxide nanosheet membrane. This moist-electric generator configuration enables energy-autonomous operation, delivering a power density of 0.1114 miroW per square cm. The V2O5 based sensor exhibits high sensitivity to humidity and temperature, enabling accurate VPD estimation for over 10 days until leaf senescence. The eutectogel based kirigami strain sensor, wrapped around the stem, offers a gauge factor of 1.5 and immunity to unrelated mechanical disturbances, allowing continuous growth tracking for more than 20 days. Both sensors are fabricated via cleanroom-free, roll to roll compatible methods, underscoring their potential for large-scale agricultural deployment to monitor abiotic stress and improve crop management.
Authors: Peihao Yan, Jie Lu, Huacheng Zeng, Y. Thomas Hou
Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs. xSlice is an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states, including time-varying wireless channel conditions, user mobility, traffic fluctuations, and changes in user demand. To address these network dynamics, we first formulate the Quality-of-Service (QoS) optimization problem as a regret minimization problem by quantifying the QoS demands of all traffic sessions through weighting their throughput, latency, and reliability. We then develop a deep reinforcement learning (DRL) framework that utilizes an actor-critic model to combine the advantages of both value-based and policy-based updating methods. A graph convolutional network (GCN) is incorporated as a component of the DRL framework for graph embedding of RAN data, enabling xSlice to handle a dynamic number of traffic sessions. We have implemented xSlice on an O-RAN testbed with 10 smartphones and conducted extensive experiments to evaluate its performance in realistic scenarios. Experimental results show that xSlice can reduce performance regret by 67% compared to the state-of-the-art solutions. Source code is available on GitHub [1].
Authors: Jinshui Zhang, Angel V Peterchev, Stefan M Goetz
Modular multilevel converters have promising applications in clean energy, electric vehicles, and biomedical instrumentation, but need many modules to achieve fine output granularity, particularly of the voltage. Asymmetric multilevel circuits introduce differences in module voltages so that the quantity of output levels grows exponentially with the number of modules. Nearest-level modulation (NLM) is preferred over carrier-based methods in asymmetric circuits for its simplicity. However, the large number of output levels can overwhelm NLM and cause excessive transistor switching on some modules and output voltage spikes. We propose a conditional nearest-level modulation (cNLM) by incorporating mathematical penalty models to regulate switching dynamics. This approach improves output quality and reduces switching rates. Additionally, we present cNLM variations tailored for specific functions, such as enforcing a minimum switching interval. Experimental validation on an asymmetric multilevel prototype demonstrates that cNLM reduces the total output distortion from 66.3% to 15.1% while cutting the switching rate to just 8% of the original NLM.
Authors: Zhongwen Sun, Wei Chen, Yuxuan Sun, Bo Ai
With the arrival of 6G, the Internet of Things (IoT) traffic is becoming more and more complex and diverse. To meet the diverse service requirements of IoT devices, massive machine-type communications (mMTC) becomes a typical scenario, and more recently, grant-free random access (GF-RA) presents a promising direction due to its low signaling overhead. However, existing GF-RA research primarily focuses on improving the accuracy of user detection and data recovery, without considering the heterogeneity of traffic. In this paper, we investigate a non-orthogonal GF-RA scenario where two distinct types of traffic coexist: event-triggered traffic with alarm devices (ADs), and status update traffic with monitor devices (MDs). The goal is to simultaneously achieve high detection success rates for ADs and high information timeliness for MDs. First, we analyze the age-based random access scheme and optimize the access parameters to minimize the average age of information (AoI) of MDs. Then, we design an age-based prior information aided autoencoder (A-PIAAE) to jointly detect active devices, together with learned pilots used in GF-RA to reduce interference between non-orthogonal pilots. In the decoder, an Age-based Learned Iterative Shrinkage Thresholding Algorithm (LISTA-AGE) utilizing the AoI of MDs as the prior information is proposed to enhance active user detection. Theoretical analysis is provided to demonstrate the proposed A-PIAAE has better convergence performance. Experiments demonstrate the advantage of the proposed method in reducing the average AoI of MDs and improving the successful detection rate of ADs.
Authors: Huiling Liu, Junshan Luo, Shilian Wang, Fanggang Wang, Theodoros A. Tsiftsis, Symeon Chatzinotas
Advancements toward 6G have intensified demands for ultra-reliable low-latency communication, positioning shortpacket communications as a critical technology for autonomous aerial vehicle (AAV) networks. However, the open broadcast nature introduces significant security vulnerabilities. Although physical-layer security offers a low-complexity solution by exploiting wireless channel randomness, the AAV communication performance severely degrades in weak-coverage or non-line-of sight scenarios. To overcome these limitations, this paper proposes a short-packet communications framework for AAV networks that leverages reconfigurable intelligent surfaces (RIS) with the aim of extending coverage and enhancing secrecy capabilities. Analytical frameworks are developed to evaluate the average secrecy throughput (AST) in finite blocklength constraints for both external and internal avesdropping scenarios, which incorporates non-orthogonal multiple access with imperfect successive interference cancellation. Asymptotic approximations of AST are derived as transmit power approaches infinity. Furthermore, we formulate a blocklength optimization problem to maximize the AST, effectively resolving the trade-offs among delay, reliability, and secrecy. Extensive simulations validate the analytical frameworks, which reveal that large-scale RIS deployment significantly boosts AST, and the power allocation coefficient exhibits dual effects in the internal eavesdropping scenario. These observations provide useful insights for designing reliable and secure lowlatency AAV communications systems.
Authors: Stelios Zarifis, Ioannis Kordonis, Petros Maragos
Stochastic forecasting is critical for efficient decision-making in uncertain systems, such as energy markets and finance, where estimating the full distribution of future scenarios is essential. We propose Diffusion Scenario Tree (DST), a general framework for constructing scenario trees for multivariate prediction tasks using diffusion-based probabilistic forecasting models. DST recursively samples future trajectories and organizes them into a tree via clustering, ensuring non-anticipativity (decisions depending only on observed history) at each stage. We evaluate the framework on the optimization task of energy arbitrage in New York State's day-ahead electricity market. Experimental results show that our approach consistently outperforms the same optimization algorithms that use scenario trees from more conventional models and Model-Free Reinforcement Learning baselines. Furthermore, using DST for stochastic optimization yields more efficient decision policies, achieving higher performance by better handling uncertainty than deterministic and stochastic MPC variants using the same diffusion-based forecaster.
Authors: Joan Gutierrez-Florensa, Alvaro Ortega, Lukas Sigrist, Federico Milano
Accurate frequency estimation is critical for the control, monitoring and protection of electrical power systems, in particular, of systems with a high penetration of power electronics. This paper introduces the novel concept of Quasi Steady-State (QSS) frequency as a quantity that fills the gap between stationary and instantaneous frequency. QSS frequency coincides with the fundamental frequency of an AC voltage in any stationary conditions, including unbalanced and non-sinusoidal, and is able to capture the time-varying fundamental frequency in transient conditions. The paper also proposes a metric borrowed from fluid dynamics, namely, the time derivative of the circulation, to define the scope of validity of the QSS frequency. Analytical examples as well as a case study based on a fully-fledged EMT model of the IEEE 39-bus system serve to illustrate, respectively, the properties of the QSS frequency and its behavior in transient conditions.
Authors: Danish Rizvi, David Boyle
This study departs from the prevailing assumption of independent Transmission and Reflection Coefficients (TRC) in Airborne Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface (STAR-RIS) research. Instead, we explore a novel multi-user downlink communication system that leverages a UAV-mounted STAR-RIS (Aerial-STAR) incorporating a coupled TRC phase shift model. Our key contributions include the joint optimization of UAV trajectory, active beamforming vectors at the base station, and passive RIS TRCs to enhance communication efficiency, while considering UAV energy constraints. We design the TRC as a combination of discrete and continuous actions, and propose a novel Dual Actor Deep Deterministic Policy Gradient (DA-DDPG) algorithm. The algorithm relies on two separate actor networks for high-dimensional hybrid action space. We also propose a novel harmonic mean index (HFI)-based reward function to ensure communication fairness amongst users. For comprehensive analysis, we study the impact of RIS size on UAV aerodynamics showing that it increases drag and energy demand. Simulation results demonstrate that the proposed DA-DDPG algorithm outperforms conventional DDPG and DQN-based solutions by 24% and 97%, respectively, in accumulated reward. Three-dimensional UAV trajectory optimization achieves 28% higher communication efficiency compared to two-dimensional and altitude optimization. The HFI based reward function provides 41% lower QoS denial rates as compared to other benchmarks. The mobile Aerial-STAR system shows superior performance over fixed deployed counterparts, with the coupled phase STAR-RIS outperforming dual Transmit/Reflect RIS and conventional RIS setups. These findings highlight the potential of Aerial-STAR systems and the effectiveness of our proposed DA-DDPG approach in optimizing their performance.
Authors: Rashid Mushkani
Automation now steers building HVAC, distribution grids, and traffic signals, yet residents rarely have authority to pause or redirect these systems when they harm inclusivity, safety, or accessibility. We formalize a Right-to-Override (R2O) - defining override authorities, evidentiary thresholds, and domain-validated safe fallback states - and introduce a Deliberative Audit Method (DAM) with playbooks for pre-deployment walkthroughs, shadow-mode trials, and post-incident review. We instantiate R2O/DAM in simulations of smart-grid load shedding, building HVAC under occupancy uncertainty, and multi-agent traffic signals. R2O reduces distributional harm with limited efficiency loss: load-shedding disparity in unserved energy drops from 5.61x to 0.69x with constant curtailment; an override eliminates two discomfort-hours for seniors at an energy cost of 77 kWh; and median pedestrian wait falls from 90.4 s to 55.9 s with a 6.0 s increase in mean vehicle delay. We also contribute a policy standard, audit worksheets, and a ModelOps integration pattern to make urban automation contestable and reviewable.
Authors: Xuyuan Kang, Xiao Wang, Jingjing An, Da Yan
Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings. Optimal TES control and management are essential to improve the performance of the cooling system. Most existing TES systems operate on a fixed schedule, which cannot take full advantage of its load shifting capability, and requires extensive investigation and optimization. This study proposed a novel integrated load prediction and optimized control approach for ice-based TES in commercial buildings. A cooling load prediction model was developed and a mid-day modification mechanism was introduced into the prediction model to improve the accuracy. Based on the predictions, a rule-based control strategy was proposed according to the time-of-use tariff; the mid-day control adjustment mechanism was introduced in accordance with the mid-day prediction modifications. The proposed approach was applied in the ice-based TES system of a commercial complex in Beijing, and achieved a mean absolute error (MAE) of 389 kW and coefficient of variance of MAE of 12.5%. The integrated prediction-based control strategy achieved an energy cost saving rate of 9.9%. The proposed model was deployed in the realistic building automation system of the case building and significantly improved the efficiency and automation of the cooling system.
Authors: Praveen Verma, Di Shi, Yanzhu Ye, Fengyu Wang, Ying Zhang
Load redistribution (LR) attacks represent a practical and sophisticated form of false data injection (FDI) attacks, where the attacker manipulates grid data to influence economic operations of the grid through misleading security constrained economic dispatch (SCED) decisions. Traditionally, LR attack models operate under the assumption that generator measurements are secure and immune to tampering. However, the increasing integration of solar generation into power grids challenges this assumption, exposing new vulnerabilities. This paper proposes an enhanced load redistribution attack model, addressing new vulnerabilities introduced by the increasing integration of solar generation in power grids. The study demonstrates that manipulating solar generation data significantly disrupts grid economics, with peak impacts during periods of high solar generation.
Authors: Yuezhang He, Hongxi Luo, Yuancheng Lin, Carl J. Talsma, Anna Li, Zhenqian Wang, Yujuan Fang, Pei Liu, Jesse D. Jenkins, Eric Larson, Zheng Li
High costs of green hydrogen and of carbon capture, utilization, and sequestration (CCUS) have hindered policy ambition and slowed real-world deployment, despite their importance for decarbonizing hard-to-abate sectors, including cement and methanol. Given the economic challenges of adopting CCUS in cement and green hydrogen in methanol production separately, we propose a renewable-powered co-production system that couples electrolytic hydrogen and CCUS through molecule exchange. We optimize system configurations using an hourly-resolved, process-based model incorporating operational flexibility, and explore integrated strategies for plant-level deployment and CO2 source-sink matching across China. We find that co-production could reduce CO2 abatement costs to USD 41-53 per tonne by 2035, significantly lower than approximately USD 75 for standalone cement CCUS and over USD 120 for standalone renewable-based methanol. Co-production is preferentially deployed at cement plants in renewable-rich regions, potentially reshaping national CO2 infrastructure planning. This hydrogen-CCUS coupling paradigm could accelerate industrial decarbonization and scaling for other applications.
Authors: Giacomo Bastianel, Dirk Van Hertem, Hakan Ergun, Line Roald
The increasing renewable penetration introduces significant uncertainty in power system operations. At the same time, the existing transmission grid is often already congested, and urgently needed reinforcements are frequently delayed due to several constraints. To address these challenges, adjusting the grid topology based on congestion patterns is considered a non-costly remedy to guarantee efficient power transmission. Based on this idea, this paper proposes a grid topology optimization model combining optimal transmission switching and busbar splitting for AC and hybrid AC/DC grids. The methodology incorporates RES forecast uncertainty through a scenario-based stochastic optimization approach, using real offshore wind data and K-means clustering to generate representative forecast error scenarios. The proposed model includes several formulations to be compared with a plain optimal power flow (OPF) model: hourly optimizing the topology, one topology for 24 hours, or a limited number of switching actions over a day. The grid topology optimization model is formulated as a Mixed-Integer Quadratic Convex Problem, optimized based on the day-ahead (D-1) RES forecast and validated for AC-feasibility via an AC-OPF formulation. Based on the generation setpoints of the feasibility check, a redispatch simulation based on the measured (D) RES realization is then computed. The methodology is tested on an AC 30-bus test case and a hybrid AC/DC 50-bus test case, for a 24-hours (30-bus) and a 14-days (both test cases) time series. The results highlight the economic benefits brought by grid topology optimization for congested test cases with high penetration of RES. In addition, the results demonstrate that accounting for RES uncertainty with at least 6 to 8 scenarios leads to lower or comparable total costs to deterministic day-ahead forecasts, even when limiting the frequency of topological actions.
Authors: Taiki Nakano, Ahmed Aboudonia, Jaap Eising, Andrea Martinelli, Florian Dörfler, John Lygeros
We propose data-driven decentralized control algorithms for stabilizing interconnected systems. We first derive a data-driven condition to synthesize a local controller that ensures the dissipativity of the local subsystems. Then, we propose data-driven decentralized stability conditions for the global system based on the dissipativity of each local system. Since both conditions take the form of linear matrix inequalities and are based on dissipativity theory, this yields a unified pipeline, resulting in a data-driven decentralized control algorithm. As a special case, we also consider stabilizing systems interconnected through diffusive coupling and propose a control algorithm. We validate the effectiveness and the scalability of the proposed control algorithms in numerical examples in the context of microgrids.
Authors: Saifur Rahman, Syed Luqman Shah, Salman Khan, Jalal Khan, Muhammad Irfan, Maaz Shafi, Said Muhammad, Fazal Muhammad, Mohammad Shahed Akond
Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep learning (DL)-based channel estimation framework that reduces pilot overhead by grouping RIS elements and reconstructing the cascaded channel from partial pilot observations. Furthermore, conventional DL models trained under single-user settings suffer from poor generalization across new user locations and propagation scenarios. We develop a distributed machine learning (DML) strategy in which the BS and users collaboratively train a shared neural network using diverse channel datasets collected across the network, thereby achieving robust generalization. Building on this foundation, we design a hierarchical DML neural architecture that first classifies propagation conditions and then employs scenario-specific feature extraction to further improve estimation accuracy. Simulation results confirm that the proposed framework substantially reduces pilot overhead and complexity while outperforming conventional methods and single-user models in channel estimation accuracy. These results demonstrate the practicality and effectiveness of the proposed approach for 6G RIS-assisted systems.
Authors: Xinan Wang, Di Shi, Fengyu Wang
This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: (1) a YOLOv7 segmentation model for fast and robust object localization, (2) a ConvNeXt-based feature extractor trained with triplet loss to generate discriminative embeddings, and (3) a feature-assisted IoU tracker that ensures resilient multi-object tracking under occlusion and motion. To enable scalable field deployment, the pipeline is optimized for deployment on low-cost edge hardware using mixed-precision inference. The system supports incremental updates by adding embeddings from previously unseen objects into a reference database without requiring model retraining. Extensive experiments on real-world surveillance and drone video datasets demonstrate the framework's high accuracy and robustness across diverse FOI scenarios. In addition, hardware benchmarks on NVIDIA Jetson devices confirm the framework's practicality and scalability for real-world edge applications.
Authors: Le Xia, Yao Sun, Haijian Sun, Rose Qingyang Hu, Dusit Niyato, Muhammad Ali Imran
Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the involved wireless resource management becomes complicated and challenging due to the unique semantic performance measurements and energy-consuming semantic coding mechanism. To this end, this paper jointly investigates power control and spectrum reuse problems for energy-efficient D2D SemCom cellular networks. Concretely, we first model the user preference-aware semantic triplet transmission and leverage a novel metric of semantic value to identify the semantic information importance conveyed in SemCom. Then, we define the additional power consumption from semantic encoding in conjunction with basic power amplifier dissipation to derive the overall system energy efficiency (semantic-value/Joule). Next, we formulate an energy efficiency maximization problem for joint power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management solution by employing the fractional-to-subtractive problem transformation and decomposition while developing a three-stage method with theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the adequate performance superiority of our proposed solution compared with different benchmarks.
Authors: Ning Qi, Xiaolong Jin, Kai Hou, Zeyu Liu, Hongjie Jia, Wei Wei
This paper proposes a novel privacy-preserving uncertainty disclosure framework, enabling system operators to release marginal value function bounds to reduce the conservativeness of interval forecast and mitigate excessive withholding, thereby enhancing storage dispatch and social welfare. We develop a risk-averse storage arbitrage model based on stochastic dynamic programming, explicitly accounting for uncertainty intervals in value function training. Real-time marginal value function bounds are derived using a rolling-horizon chance-constrained economic dispatch formulation. We rigorously prove that the bounds reliably cap the true opportunity cost and dynamically converge to the hindsight value. We verify that both the marginal value function and its bounds monotonically decrease with the state of charge (SoC) and increase with uncertainty, providing a theoretical basis for risk-averse strategic behaviors and SoC-dependent designs. An adjusted storage dispatch algorithm is further designed using these bounds. We validate the effectiveness of the proposed framework via an agent-based simulation on the ISO-NE test system. Under 50% renewable capacity and 35% storage capacity, the proposed bounds enhance storage response by 38.91% and reduce the optimality gap to 3.91% through improved interval predictions. Additionally, by mitigating excessive withholding, the bounds yield an average system cost reduction of 0.23% and an average storage profit increase of 13.22%. These benefits further scale with higher prediction conservativeness, storage capacity, and system uncertainty.
Authors: Giorgi Tsintsadze, Haran Manoharan, Aaron Harmon, Daniel Commerou, Connor Buneta, Brian Booth, Daryl Beetner
Because GPS signals are weak, electronic systems and components that are placed near GPS receivers can easily cause disruptive electromagnetic interference through their unintended radiated emissions. In this paper, EMC limit level guidelines are presented for electronics that are intended to be placed near to GPS receivers, as often happens in automotive and other applications. One of the challenges of defining limit-levels for systems intended to be integrated with GPS receivers is that the impact of noise at the input of the receiver may vary substantially depending on the form of the noise due to the correlator function implemented by GPS receiver. The quality of the correlated signal is typically represented using the carrier-to-noise ratio ($C / N_0$). A theoretical model predicting the degredation of the carrier-to-noise ratio with radio frequency interference is presented in this paper and is validated with realistic noise sources. The model is then used to develop guidelines to assess the impact of unintended emissions from electronic devices on nearby GPS receivers based on the frequency, bandwidth, and magnitude of the noise. These guidelines provide a more nuanced method of evaluating emissions than simple limit lines that are used by many emissions standards.
Authors: Azadeh Tabeshnezhad, Yuqing Zhu, Artem Vilenskiy, Van Ly Nguyen, A. Lee Swindlehurst, Tommy Svensson
Non-orthogonal multiple access (NOMA) is a promising technology for next-generation wireless communication systems due to its enhanced spectral efficiency. In this paper, we consider an uplink NOMA system operating together with a high-dimensional absorptive reconfigurable intelligent surface (A-RIS). We aim to minimize the total power transmitted by the users in order to meet signal-to-interference-plus-noise constraints at the base station in the presence of a jammer. We propose an iterative algorithm to solve the high-dimensional non-convex optimization problem using linear programming to find the transmit powers and a fractional programming algorithm based on the Dinkelbach algorithm with a sequential convex relaxation procedure to optimize the reflection coefficients. We show that our algorithm converges on large optimization problems, with a jammer comprising as many as $64$ antennas, and an A-RIS with $128$ elements. Our numerical results show that, compared with a standard RIS that reflects all impinging energy, the A-RIS can dramatically reduce the users' required transmit power and successfully mitigate interference from the jammer. The absorption capability of the A-RIS is in particular useful in cases when the number of jammer antennas is of the same order as the number of A-RIS elements.
Authors: Thinh Viet Le, Md Obaidur Rahman, Vassilis Kekatos
Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy transition. To expedite such studies, this work leverages recent advances in quantum computing to find or predict AC PF solutions using a variational quantum circuit (VQC). VQCs are trainable models that run on modern-day noisy intermediate-scale quantum (NISQ) hardware to accomplish elaborate optimization and machine learning (ML) tasks. Our first contribution is to pose a single instance of the AC PF as a nonlinear least-squares fit over the VQC trainable parameters (weights) and solve it using a hybrid classical/quantum computing approach. The second contribution is to feed PF specifications as features into a data-embedded VQC and train the resultant quantum ML (QML) model to predict general PF solutions. The third contribution is to develop a novel protocol to efficiently measure AC-PF quantum observables by exploiting the graph structure of a power network. Preliminary numerical tests indicate that the proposed VQC models attain enhanced prediction performance over a deep neural network despite using much fewer weights. The proposed quantum AC-PF framework sets the foundations for addressing more elaborate grid tasks via quantum computing.
Authors: Jiawei Wang, Haowei Sun, Xintao Yan, Shuo Feng, Jun Gao, Henry X. Liu
Safe and scalable deployment of end-to-end (E2E) autonomous driving requires extensive and diverse data, particularly safety-critical events. Existing data are mostly generated from simulators with a significant sim-to-real gap or collected from on-road testing that is costly and unsafe. This paper presents TeraSim-World, an automated pipeline that synthesizes realistic and geographically diverse safety-critical data for E2E autonomous driving at anywhere in the world. Starting from an arbitrary location, TeraSim-World retrieves real-world maps and traffic demand from geospatial data sources. Then, it simulates agent behaviors from naturalistic driving datasets, and orchestrates diverse adversities to create corner cases. Informed by street views of the same location, it achieves photorealistic, geographically grounded sensor rendering via the frontier video generation model Cosmos-Drive. By bridging agent and sensor simulations, TeraSim-World provides a scalable and critical data synthesis framework for training and evaluation of E2E autonomous driving systems. Codes and videos are available at this https URL .
Authors: An Nguyen, Hung Pham, Cuong Do
This paper presents a cost optimization framework for electric vehicle (EV) charging stations that leverages on-site photovoltaic (PV) generation and explicitly accounts for electricity price uncertainty through a Bertsimas--Sim robust formulation. The model is formulated as a linear program that satisfies vehicle energy demands, respects charging and grid capacity constraints, and minimizes procurement cost. Evaluations on real charging data from the Caltech ACN dataset show average savings of about 12\% compared to a first-come--first-served baseline, with peak monthly reductions up to 19.2\%. A lightweight sensitivity analysis indicates that a modest $\sim$5\% increase in nominal cost can reduce worst-case exposure by 14\%. Computational tests confirm real-time feasibility, with instances of up to 50 concurrent EVs solved in under 5 seconds on a standard laptop. The proposed method provides a practical, grid-friendly, and scalable solution for future EV charging operations.
Authors: Siying Huang, Yifen Mu, Ge Chen
The increasing integration of renewable energy introduces a great challenge to the supply and demand balance of the power grid. To address this challenge, this paper formulates a Stackelberg Markov game (SMG) between an aggregator and multiple users, where the aggregator sets electricity prices and users make demand and storage decisions. Considering that users' storage levels are private information, we introduce private states and propose the new concepts of private Markovian strategies (PMS) and private Markovian equilibrium (PME). We establish the existence of a pure PME in the lower-level Markov game and prove that it can be computed in polynomial time. Notably, computing equilibrium in general Markov games is hard, and polynomial-time algorithms are rarely available. Based on these theoretical results, we develop a scalable solution framework combining centralized and decentralized algorithms for the lower-level PME computation with upper-level pricing optimization. Numerical simulations with up to 50 users based on real data validate the effectiveness and scalability of the proposed methods, whereas prior studies typically consider no more than 5 users.
Authors: Sel Ly, Kapil Chauhan, Anshuman Singh, Hung Dinh Nguyen
The probabilistic power flow (PPF) problem is essential to quantifying the distribution of the nodal voltages due to uncertain injections. The conventional PPF problem considers a fixed topology, and the solutions to such a PPF problem are associated with this topology. A change in the topology might alter the power flow patterns and thus require the PPF problem to be solved again. The previous PPF model and its solutions are no longer valid for the new topology. This practice incurs both inconvenience and computation burdens as more contingencies are foreseen due to high renewables and a large share of electric vehicles. This paper presents a novel topology-adaptive approach, based on the meta-model Neural Process (MMNP), for finding the solutions to PPF problems under varying N-1 topologies, particularly with one-line failures. By leveraging context set-based topology representation and conditional distribution over function learning techniques, the proposed MMNP enhances the robustness of PPF models to topology variations, mitigating the need for retraining PPF models on a new configuration. Simulations on an IEEE 9-bus system and IEEE 118-bus system validate the model's performance. The maximum %L1-relative error norm was observed as 1.11% and 0.77% in 9-bus and 118-bus, respectively. This adaptive approach fills a critical gap in PPF methodology in an era of increasing grid volatility.
Authors: Nacira Agram, Fred Espen Benth, Giulia Pucci, Jan Rems
We study a stochastic model for the installation of renewable energy capacity under demand uncertainty and jump driven dynamics. The system is governed by a multidimensional Ornstein-Uhlenbeck (OU) process driven by a subordinator, capturing abrupt variations in renewable generation and electricity load. Installation decisions are modeled through control actions that increase capacity in response to environmental and economic conditions. We consider two distinct solution approaches. First, we implement a structured threshold based control rule, where capacity is increased proportionally when the stochastic capacity factor falls below a fixed level. This formulation leads to a nonlinear partial integro-differential equation (PIDE), which we solve by reformulating it as a backward stochastic differential equation with jumps. We extend the DBDP solver in \cite{hure2020deep} to the pure jump setting, employing a dual neural network architecture to approximate both the value function and the jump sensitivity. Second, we propose a fully data driven deep control algorithm that directly learns the optimal feedback policy by minimizing the expected cost functional using neural networks. This approach avoids assumptions on the form of the control rule and enables adaptive interventions based on the evolving system state. Numerical experiments highlight the strengths of both methods. While the threshold based BSDE approach offers interpretability and tractability, the deep control strategy achieves improved performance through flexibility in capacity allocation. Together, these tools provide a robust framework for decision support in long term renewable energy expansion under uncertainty.
Authors: Ruining Yang, Jingyuan Zhou, Qiqing Wang, Jinhao Liang, Kaidi Yang
With recent advancements in Connected Autonomous Vehicles (CAVs), Green Light Optimal Speed Advisory (GLOSA) emerges as a promising eco-driving strategy to reduce the number of stops and idle time at intersections, thereby reducing energy consumption and emissions. Existing studies typically improve energy and travel efficiency for individual CAVs without considering their impacts on the entire mixed-traffic platoon, leading to inefficient traffic flow. While Reinforcement Learning (RL) has the potential to achieve platoon-level control in a mixed-traffic environment, the training of RL is still challenged by (i) car-following safety, i.e., CAVs should not collide with their immediate preceding vehicles, and (ii) red-light safety, i.e., CAVs should not run red lights. To address these challenges, this paper develops a platoon-centric, safe RL-based GLOSA system that uses a multi-agent controller to optimize CAV speed while achieving a balance between energy consumption and travel efficiency. We further incorporate Control Barrier Functions (CBFs) into the RL-based policy to provide explicit safety guarantees in terms of car-following safety and red-light safety. Our simulation results illustrate that our proposed method outperforms state-of-the-art methods in terms of driving safety and platoon energy consumption.
Authors: Alireza Shooshtari, Antonio Pepiciello, José Luis Domínguez-García
The role of energy communities in grid operations is highly dependent on the spatial distribution of their participants. In particular, when local energy producers and consumers are concentrated in different feeders, economic incentives from energy communities have the potential to affect local grid congestion. To address this challenge, we propose a feeder-aware allocation strategy that reflects grid topology in energy sharing. This strategy prioritizes energy sharing within the same feeder, thus incentivizing local generation-demand balance and improving grid operation. Different sharing coefficients are tested, such as equal, proportional, and rank-based, in both static and dynamic formulations. The proposed strategy is tested on data from a real energy community, whose participants are assumed to be distributed across four feeders. The analysis is carried out from the perspectives of the community as a whole, individual feeders, and single participants. Simulation results show that the feeder-aware strategy, in addition to promoting local energy balance, leads to higher and more stable revenues for most participants.
Authors: Bálint Hartmann, Michelle T. Cirunay
Reliable electricity supply depends on the seamless operation of high-voltage grid infrastructure spanning both transmission and sub-transmission levels. Beneath this apparent uniformity lies a striking structural diversity, which leaves a clear imprint on system vulnerability. In this paper, we present harmonized topological models of the high-voltage grids of 15 European countries, integrating all elements at voltage levels above 110 kV. Topological analysis of these networks reveals a simple yet robust pattern: node degree distributions consistently follow an exponential decay, but the rate of decay varies significantly across countries. Through a detailed and systematic evaluation of network tolerance to node and edge removals, we show that the decay rate delineates the boundary between systems that are more resilient to failures and those that are prone to large-scale disruptions. Furthermore, we demonstrate that this numerical boundary is highly sensitive to which layers of the infrastructure are included in the models. To our knowledge, this study provides the first quantitative cross-country comparison of 15 European high-voltage networks, linking topological properties with vulnerability characteristics.
Authors: Xinyu He, Chenhan Xiao, Haoran Li, Ruizhong Qiu, Zhe Xu, Yang Weng, Jingrui He, Hanghang Tong
Modern power systems are becoming increasingly dynamic, with changing topologies and time-varying loads driven by renewable energy variability, electric vehicle adoption, and active grid reconfiguration. Despite these changes, publicly available test cases remain scarce, due to security concerns and the significant effort required to anonymize real systems. Such limitations call for generative tools that can jointly synthesize grid structure and nodal dynamics. However, modeling the joint distribution of network topology, branch attributes, bus properties, and dynamic load profiles remains a major challenge, while preserving physical feasibility and avoiding prohibitive computational costs. We present PowerGrow, a co-generative framework that significantly reduces computational overhead while maintaining operational validity. The core idea is dependence decomposition: the complex joint distribution is factorized into a chain of conditional distributions over feasible grid topologies, time-series bus loads, and other system attributes, leveraging their mutual dependencies. By constraining the generation process at each stage, we implement a hierarchical graph beta-diffusion process for structural synthesis, paired with a temporal autoencoder that embeds time-series data into a compact latent space, improving both training stability and sample fidelity. Experiments across benchmark settings show that PowerGrow not only outperforms prior diffusion models in fidelity and diversity but also achieves a 98.9\% power flow convergence rate and improved N-1 contingency resilience. This demonstrates its ability to generate operationally valid and realistic power grid scenarios.
Authors: Saman Mazaheri Khamaneh, Tong Wu, Wei Sun, Cong Chen
Modern power systems face increasing vulnerability to sophisticated cyber-physical attacks beyond traditional N-1 contingency frameworks. Existing security paradigms face a critical bottleneck: efficiently identifying worst-case scenarios and rapidly coordinating defensive responses are hindered by intensive computation and time delays, during which cascading failures can propagate. This paper presents a novel tri-level robust constrained reinforcement learning (RCRL) framework for robust power system security. The framework generates diverse system states through AC-OPF formulations, identifies worst-case N-K attack scenarios for each state, and trains policies to mitigate these scenarios across all operating conditions without requiring predefined attack patterns. The framework addresses constraint satisfaction through Beta-blending projection-based feasible action mapping techniques during training and primal-dual augmented Lagrangian optimization for deployment. Once trained, the RCRL policy learns how to control observed cyber-physical attacks in real time. Validation on IEEE benchmark systems demonstrates effectiveness against coordinated N-K attacks, causing widespread cascading failures throughout the network. The learned policy can successfully respond rapidly to recover system-wide constraints back to normal within 0.21 ms inference times, establishing superior resilience for critical infrastructure protection.
Authors: Peng Chen, Jing Liang, Hui Song, Kang-Jia Qiao, Cai-Tong Yue, Kun-Jie Yu, Ponnuthurai Nagaratnam Suganthan, Witold Pedrycz
The increasing labor costs in agriculture have accelerated the adoption of multi-robot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load-dependent speed variations and battery limitations. This paper defines the multi-objective agricultural multi-electrical-robot task allocation (AMERTA) problem, which systematically incorporates these often-overlooked real-world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual-phase initialization method, task sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state-of-the-art algorithms. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multi-robot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation.
Authors: Maísa Beraldo Bandeira, Alexander Engelmann, Timm Faulwasser
The increasing number of flexible devices and distributed energy resources in power grids renders the coordination of transmission and distribution systems increasingly complex. In this paper, we discuss and compare two different approaches to optimization-based complexity reduction: Flexibility aggregation via Approximate Dynamic Programming (ADP) and distributed optimization via the Alternating Direction Method of Multipliers (ADMM). Flexibility aggregation achieves near-optimal solutions with minimal communication. However, its performance depends on the quality of the approximation used. In contrast, ADMM attains results closer to the centralized solution but requires significantly more communication steps. We draw upon a case study combining different matpower benchmarks to compare both methods.
Authors: Youssef Shaker, Jun Wen Law, Audun Botterud, Dharik Mallapragada
Policies focused on deep decarbonization of regional economies emphasize electricity sector decarbonization alongside electrification of end-uses. There is growing interest in utilizing hydrogen (H2) produced via electricity to displace fossil fuels in difficult-to-electrify sectors. One such case is heavy-duty vehicles (HDV), which represent a substantial and growing share of transport emissions as light-duty vehicles electrify. Here, we assess the bulk energy system impact of decarbonizing the HDV segment via either H2, or drop-in synthetic liquid fuels produced from H2 and CO2. Our analysis soft-links two modeling approaches: (a) a bottom-up transport demand model producing a variety of final energy demand scenarios for the same service demand and (b) a multi-sectoral capacity expansion model that co-optimizes power, H2 and CO2 supply chains under technological and policy constraints to meet exogenous final energy demands. Through a case study of Western Europe in 2040 under deep decarbonization constraints, we quantify the energy system implications of different levels of H2 and synthetic fuels adoption in the HDV sector under scenarios with and without CO2 sequestration. In the absence of CO2 storage, substitution of liquid fossil fuels in HDVs is essential to meet the deep decarbonization constraint across the modeled power, H2 and transport sectors. Additionally, utilizing H2 HDVs reduces decarbonization costs and fossil liquids demand, but could increase natural gas consumption. While H2 HDV adoption reduces the need for direct air capture (DAC), synthetic fuel adoption increases DAC investments and total system costs. The study highlights the trade-offs across transport decarbonization pathways, and underscores the importance of multi-sectoral consideration in decarbonization studies.
Authors: Davood Keshavarzi, Alexander Koehler, Stefan M. Goetz
An increasing integration of photovoltaic units, electric vehicle chargers, heat pumps, and energy storage systems challenges low-voltage power grids and can cause voltage range violation, loss of stability, (local) overload of lines, and power management problems. Research suggested universal power-flow control (UPFC) to solve power management problems. In contrast to bulky, slow, and costly conventional UPFCs with their shunt and series transformers, this paper presents a highly compact and current-dense power-flow controller, which can serve between different feeders in the low-voltage power grids. The enabler is a systematic combination of silicon car-bide (SiC) with silicon (Si) transistors and a strict partial-power topology built around a multi-active bridge. The circuit links an active-front-end converter as a shunt stage through a multi-active-bridge converter bidirectionally with low-voltage series-injection modules floating with their respective phases. The topology can use small power to control high currents through the low-voltage series-injection modules. The multi-active bridge serves as a multi-input-output power router that exchanges energy between all elements. We assess the design as well as the implementation considerations of the proposed power-flow controller mathematically and verify its performance in simulation and real systems.
Authors: Ning Qi, Xiaolong Jin, Kai Hou, Zeyu Liu, Hongjie Jia, Wei Wei
This paper proposes a novel privacy-preserving uncertainty disclosure framework, enabling system operators to release marginal value function bounds to reduce the conservativeness of interval forecast and mitigate excessive withholding, thereby enhancing storage dispatch and social welfare. We propose a risk-averse analytical storage arbitrage model based on stochastic dynamic programming and explicitly account for uncertainty intervals in value function training. We derive real-time marginal value function bounds using a rolling-horizon chance-constrained economic dispatch formulation. We rigorously prove that the bounds reliably cap the true opportunity cost and dynamically converge to the hindsight value. We verify that both the marginal value function and its bounds monotonically decrease with the state of charge and increase with uncertainty, providing a theoretical basis for risk-averse strategic behaviors and SoC-dependent designs. We validate the effectiveness of the proposed framework via an agent-based simulation on the ISO-NE test system. Under 50% renewable capacity and 35% storage capacity, the proposed bounds enhance storage response by 38.91% and reduce the optimality gap to 3.91% through improved interval predictions. Additionally, by mitigating excessive withholding, the bounds yield an average system cost reduction of 0.23% and an average storage profit increase of 13.22%. These benefits further scale with higher prediction conservativeness, storage capacity, and system uncertainty.
Authors: Irina Subotić, Dominic Groß, Alexander Winkens, Julian Jansen, Florian Klein-Helmkamp, Andreas Ulbig
This work investigates the transient and dynamical behavior of hybrid AC/DC systems using dual-port grid-forming (GFM) control. A generalized modeling framework for hybrid AC/DC networks is first introduced that accounts for converter, control, and network circuit dynamics and arbitrary network topologies. This modeling framework is applied to low-voltage networks to analyze the performance of dual-port grid-forming (GFM) control. The results demonstrate that active damping by dual-port GFM control is effective at improving the transient response and mitigating oscillations. In contrast, the steady-state response characteristics can be adjusted independently with minimal impact on damping characteristics. The dynamic model and results are validated through hardware experiments for three prototypical system architectures. Furthermore, we demonstrate that low-voltage DC distribution interfaced by AC/DC converters using dual-port GFM control, can serve both as the sole interconnection between AC distribution systems and in parallel to an AC connection, thereby enhancing the operational flexibility of low- and medium-voltage distribution networks.
Authors: Quan Nguyen, Christine Holland, Siddharth Sridhar
The installation of electric vehicle (EV) charging stations in buildings is inevitable, as states push for increased EV adoption to support decarbonization efforts. This transition could force the need for grid infrastructure upgrades and enhanced controls to support reliable power delivery to end-use loads, and overall economic operation. This paper evaluates strategies that address these needs on two fronts: i) optimal sizing of service transformers and battery energy storage systems (BESS), and ii) optimized coordination between EV charging, BESS operation, and building demand. These strategies are applied to a school campus setting, consisting of building and EV charging loads, to provide an illustration of energy management in commercial buildings with EV fleets. A rolling-window optimization approach is applied to determine i) optimal sizing of the service transformer and BESS and ii) optimal control of EV charging and BESS charge/discharge schedules. The design and control strategies are validated in a 20-year time horizon with an annually increasing number of EVs (buses and vans). In addition, an economic analysis is also carried out to show the costs and benefits of each design as a medium- and long-term investment.
Authors: Chris Giotitsas, Pedro H. J. Nardelli, Vasilis Kostakis, Arun Narayanan
The discussions around the unsustainability of the dominant socio-economic structures have yet to produce solutions to address the escalating problems we face as a species. Such discussions, this paper argues, are hindered by the limited scope of the proposed solutions within a business-as-usual context as well as by the underlying technological rationale upon which these solutions are developed. In this paper, we conceptualize a radical sustainable alternative to the energy conundrum based on an emerging mode of production and a commons-based political economy. We propose a commons-oriented Energy Internet as a potential system for energy production and consumption, which may be better suited to tackle the current issues society faces. We conclude by referring to some of the challenges that the implementation of such a proposal would entail.
Authors: Safa Mohammed Sali, Hoach The Nguyen, Ameena Saad Al-Sumaiti
This paper introduces a single-switch high-gain voltage-multiplier coupled quadratic boost converter (HGVM-QBC), developed from the conventional quadratic boost converter (QBC). The proposed topology is designed to achieve higher voltage gain, lower semiconductor voltage stress, and continuous current operation, making it particularly suitable for small-scale photovoltaic (PV) systems. By incorporating a voltage multiplier cell into the QBC, the converter significantly improves voltage boosting capability while mitigating stress on switching devices. In this configuration, the output voltage is obtained by combining the voltages across multiple output capacitors, thereby enhancing the overall voltage level. A detailed comparative study with recently reported converter topologies demonstrates the superior gain and reduced device stress offered by the HGVM-QBC. The design is validated through MATLAB/Simulink simulations, which confirm improved performance in terms of gain and voltage stress. Furthermore, an experimental prototype achieves an output of 151 Vdc from a 12 Vdc input at a 55% duty cycle, corresponding to a gain of 12.59. These results establish the HGVM-QBC as an efficient and reliable solution for PV applications that demand high voltage output from low input sources.
Authors: Wenqi Cui, Yiheng Xie, Steven Low, Adam Wierman, Baosen Zhang
High variability of solar PV and sudden changes in load (e.g., electric vehicles and storage) can lead to large voltage fluctuations in the distribution system. In recent years, a number of controllers have been designed to optimize voltage control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time, such that the control actions converge before the load changes again. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to voltage control in power systems with significant time-varying net load. We leverage advances in short-term load forecasting, where the net load in the system can be partially predicted using local measurements. We integrate these predictions into the design of adaptive controllers, and prove that the overall control architecture achieves input-to-state stability in a decentralized manner. We optimize the control policy through reinforcement learning. Case studies are conducted using time-varying load data from a real-world distribution system.
Authors: Jorge Ventura, Jaroslav Hrdina, Aleš Návrat, Marek Stodola, Ahmad Eid, Santiago Sanchez-Acevedo, Francisco G. Montoya
Power systems with high penetration of inverter-based resources (IBR) present significant challenges for conventional protection schemes, with traditional distance protection methods failing to detect line-to-line faults during asymmetric conditions. This paper presents a methodology for electrical fault detection and classification using ellipse fitting and geometric algebra applied to voltage and current space curves. The approach characterizes electrical faults by fitting ellipses to voltage vector data, enabling fault detection with only a quarter-cycle. The method employs bivector components for line-to-ground fault classification, while ellipse parameters identify line-to-line and three-phase faults. The geometric representation preserves voltage or current curve shapes in three-dimensional space, overcoming Clarke transform limitations when zero-sequence components are present. Validation using simulations and laboratory experiments demonstrates accurate fault identification and magnitude estimation, providing enhanced power system protection capabilities.
Authors: Amir Bahador Javadi, Amin Kargarian, Mort Naraghi-Pour
The increasing penetration of renewable energy sources introduces significant uncertainty in power system operations, making traditional deterministic unit commitment approaches computationally expensive. This paper presents a machine learning surrogate modeling approach designed to reformulate the feasible design space of the two-stage stochastic unit commitment (TSUC) problem, reducing its computational complexity. The proposed method uses a support vector machine (SVM) to construct a surrogate model based on the governing equations of the learner. This model replaces the original 2|L| * |S| transmission line flow constraints, where |S| is the number of uncertainty scenarios and |L| is the number of transmission lines with |S| much less than |L|, with a significantly reduced set of 1 * |S| linear inequality constraints. The approach is theoretically grounded in the polyhedral structure of the feasible region under the DC power flow approximation, enabling the transformation of 2|L| line flow limit constraints into a single linear constraint. The surrogate model is trained using data generated from computationally efficient DC optimal power flow simulations. Simulation results on the IEEE 57-bus and 118-bus systems demonstrate SVM halfspace constraint accuracy of 99.72% and 99.88%, respectively, with TSUC computational time reductions of 46% and 31% and negligible generation cost increases (0.63% and 0.88% on average for IEEE 57- and 118-bus systems, respectively). This shows the effectiveness of the proposed approach for practical power system operations under renewable energy uncertainty.
Authors: Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Zhiqin Wang
Simultaneous wireless information and power transfer (SWIPT) has attracted sustained interest. We propose a null-space-based transmission scheme for multiuser SWIPT serving both energy users (EUs) and information users (IUs). Under a practical nonlinear energy-harvesting (EH) model and multiple waveform options, we revisit the role of dedicated energy beams (EBs). We show that, in general, dedicated EBs are unnecessary because information beams (IBs) with Gaussian signaling can simultaneously support wireless energy transfer (WET) and wireless information transfer (WIT), unless special energy-centric waveforms (e.g., deterministic sinusoidal waveforms) are employed and provide sufficient gains. Guided by these insights, we formulate an optimization problem for EB design to enable dedicated waveform transmission for WET, and we develop a low-complexity algorithm that reduces computation by ignoring the WET contribution of IBs during optimization. Numerical results corroborate that deterministic sinusoidal waveforms outperform Gaussian signaling when the received RF power lies in the EH high-efficiency region, making dedicated EBs beneficial. The proposed scheme achieves computational complexity reductions of 91.43\% and 98.54\% for the cases $M=8,,K^I=K^E=2$ and $M=16,,K^I=K^E=4$, respectively, with negligible performance loss, thereby validating the efficiency of the low-complexity algorithm.
Authors: Ahmed Al-Tahmeesschi, Yi Chu, Gurdeep Singh, Charles Turyagyenda, Dritan Kaleshi, David Grace, Hamed Ahmadi
The growing demand for high-speed, ultra-reliable, and low-latency communications in 5G and beyond networks has significantly driven up power consumption, particularly within the Radio Access Network (RAN). This surge in energy demand poses critical operational and sustainability challenges for mobile network operators, necessitating innovative solutions that enhance energy efficiency without compromising Quality of Service (QoS). Open Radio Access Network (O-RAN), spearheaded by the O-RAN Alliance, offers disaggregated, programmable, and intelligent architectures, promoting flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach adds complexity, particularly in managing power consumption across diverse network components such as Open Radio Units (RUs). In this paper, we propose a hybrid xApp leveraging heuristic methods and unsupervised machine learning, integrated with digital twin technology through the TeraVM AI RAN Scenario Generator (AI-RSG). This approach dynamically manages RU sleep modes to effectively reduce energy consumption. Our experimental evaluation in a realistic, large-scale emulated Open RAN scenario demonstrates that the hybrid xApp achieves approximately 13% energy savings, highlighting its practicality and significant potential for real-world deployments without compromising user QoS.
Authors: Francisco G. Montoya, Santiago Sánchez Acevedo
Coordinate transformations provide dimensional reduction benefits across power system analysis, electric machine modeling, and power electronic converter control. This paper introduces a novel transformation based on Geometric Algebra that directly identifies the plane containing unbalanced quantity loci through bivector analysis. The method provides a direct transformation valid for any degree of unbalance in $n$-phase, $(n+1)$-wire sinusoidal systems, requiring only two voltage or current measurements at different time instants. Through pure geometric reasoning, we demonstrate that our approach generalizes existing techniques while extending naturally to multi-dimensional systems. Experimental validation using real-time digital simulation and physical laboratory testing confirms the method's effectiveness under realistic conditions. Power electronics converter control implementation demonstrates significant practical advantages, eliminating zero component oscillations present in Clarke transformation under unbalanced conditions and enabling more effective control architectures. The combination of computational efficiency, robustness, and practical applicability represents a significant advancement for power system control applications.
Authors: Yijie Liu, Weidong Mei, He Sun, Dong Wang, Peilan Wang
Beyond diagonal reconfigurable intelligent surface (BD-RIS), with its enhanced degrees of freedom compared to conventional RIS, has demonstrated notable potential for enhancing wireless communication performance. However, a key challenge in employing BD-RIS lies in accurately acquiring its channel state information (CSI) with both the base station (BS) and users. Existing BD-RIS channel estimation methods rely mainly on dedicated pilot signals, which increase system overhead and may be incompatible with current communication protocols. To overcome these limitations, this letter proposes a new single-layer neural network (NN)-enabled channel estimation method utilizing only the easily accessible received power measurements at user terminals. In particular, we show that the received signal power can be expressed in a form similar to a single-layer NN, where the weights represent the BD-RIS's CSI. This structure enables the recovery of CSI using the backward propagation, based on power measurements collected under varying training reflection coefficients. Numerical results show that our proposed method can achieve a small normalized mean square error (NMSE), particularly when the number of training reflections is large.
Authors: Mona Ghassemi
To achieve net-zero emissions by 2050, all-electric transportation is a promising option. In the U.S., the transportation sector contributes the largest share (29 percent) of greenhouse gas emissions. While electric vehicles are approaching maturity, aviation is only beginning to develop electrified aircraft for commercial flights. More than 75 percent of aviation emissions come from large aircraft, and this impact will worsen with 4-5 percent annual air travel growth. Aircraft electrification has led to two types: more electric aircraft (MEA) and all-electric aircraft (AEA). A MEA replaces subsystems such as hydraulics with electric alternatives, whereas an AEA uses electrically driven subsystems and provides thrust fully from electrochemical energy units (EEUs). For wide-body AEA, thrust demand is about 25 MW plus 1 MW for non-thrust loads, creating major challenges for electric power system (EPS) design. Achieving maximum power density requires minimizing mass and volume. Increasing voltage into the kilovolt range using medium-voltage direct current (MVDC) is a feasible option to enhance power transfer. Consequently, designing an MVDC EPS for wide-body AEA is critical. Because EPS failures could jeopardize passenger safety, reliability and resilience are essential. This chapter presents a load-flow model for DC systems to determine power flows in both normal and single-contingency conditions, followed by analysis of optimal MVDC EPS architectures. A complete EPS for wide-body AEA is introduced, with EEUs and non-propulsion loads located, distances estimated, and flow studies performed. Multiple architectures are evaluated for reliability, power density, power loss, and cost to identify optimal solutions.
Authors: Diana Vieira Fernandes, Soummya Kar, Carlos Santos Silva
Distribution networks face challenges from the increasing deployment of Distributed Energy Resources (DERs) and the emergence of bidirectional power flows. We propose a decentralized Volt/VAr control method based on a saddle-point reformulation and consensus+innovation (C+I) updates. Each agent at a controllable bus computes and enforces its own set-points using only neighbor communication. Our method embeds passive buses directly, preserves network physics through a linearized Jacobian model, and avoids any supervisory nodes. Simulation results on a modified CIGRE low-voltage network show voltage stability improvement within operational limits, indicating the viability of a fully decentralized (edge-based) Volt/VAr control solution.
Authors: Julian Oelhaf, Georg Kordowich, Mehran Pashaei, Christian Bergler, Andreas Maier, Johann Jäger, Siming Bayer
The integration of renewable and distributed energy resources reshapes modern power systems, challenging conventional protection schemes. This scoping review synthesizes recent literature on machine learning (ML) applications in power system protection and disturbance management, following the PRISMA for Scoping Reviews framework. Based on over 100 publications, three key objectives are addressed: (i) assessing the scope of ML research in protection tasks; (ii) evaluating ML performance across diverse operational scenarios; and (iii) identifying methods suitable for evolving grid conditions. ML models often demonstrate high accuracy on simulated datasets; however, their performance under real-world conditions remains insufficiently validated. The existing literature is fragmented, with inconsistencies in methodological rigor, dataset quality, and evaluation metrics. This lack of standardization hampers the comparability of results and limits the generalizability of findings. To address these challenges, this review introduces a ML-oriented taxonomy for protection tasks, resolves key terminological inconsistencies, and advocates for standardized reporting practices. It further provides guidelines for comprehensive dataset documentation, methodological transparency, and consistent evaluation protocols, aiming to improve reproducibility and enhance the practical relevance of research outcomes. Critical gaps remain, including the scarcity of real-world validation, insufficient robustness testing, and limited consideration of deployment feasibility. Future research should prioritize public benchmark datasets, realistic validation methods, and advanced ML architectures. These steps are essential to move ML-based protection from theoretical promise to practical deployment in increasingly dynamic and decentralized power systems.
Authors: Mohammed M. H. Qazzaz, Abdelaziz Salama, Maryam Hafeez, Syed A. R. Zaidi
Open Radio Access Network (O-RAN) architecture provides an intrinsic capability to exploit key performance monitoring (KPM) within Radio Intelligence Controller (RIC) to derive network optimisation through xApps. These xApps can leverage KPM knowledge to dynamically switch on/off the associated RUs where such a function is supported over the E2 interface. Several existing studies employ artificial intelligence (AI)/Machine Learning (ML) based approaches to realise such dynamic sleeping for increased energy efficiency (EE). Nevertheless, most of these approaches rely upon offloading user equipment (UE) to carve out a sleeping opportunity. Such an approach inherently creates load imbalance across the network. Such load imbalance may impact the throughput performance of offloaded UEs as they might be allocated a lower number of physical resource blocks (PRBs). Maintaining the same PRB allocation while addressing the EE at the network level is a challenging task. To that end, in this article, we present a comprehensive ML-based framework for joint optimisation of load balancing and EE for ORAN deployments. We formulate the problem as a multi-class classification system that predictively evaluates potential RU configurations before optimising the EE, mapping network conditions to three load balance categories (Well Balanced, Moderately Balanced, Imbalanced). Our multi-threshold approach (Conservative, Moderate, Aggressive) accommodates different operational priorities between energy savings and performance assurance. Experimental evaluation using 4.26 million real network measurements from simulations demonstrates that our Random Forest model achieves 98.3% F1-macro performance, representing 195% improvement over traditional baseline strategies.
Authors: Xinyu Zhou, Jun Zhao, Huimei Han, Claude Guet
Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.
Authors: Jonas Birgersson, Marc A. Weiss, Jimmy Chen, Daniel Kammen, Tomas Kåberger, Franklin Carrero-Martínez, Joakim Wernberg, Michael Menser, Newsha K. Ajami
In developing EnergyNet we have leveraged and are extending lessons from telecom's shift from a centralized, circuit-switched phone system to decentralized, packet-switched data networks. EnergyNet utilizes 1) an Energy Router that enforces galvanic separation and utilizes software-controlled energy flows over a DC backplane, 2) Energy Local and Wide Area Networks (ELAN/EWAN) based on DC microgrids that interconnect through an open Energy Protocol (EP), and 3) a control plane comprised of the Energy Router Operating System (EROS) and EP Server which is managed at operator scale through an Energy Network Management System (ENMS). We distinguish the architectural contribution (Tier-1 including components, interfaces, and operating model) from expected outcomes contingent on adoption (Tier-2). The latter includes local-first autonomy with global interoperability, near-real-time operation with local buffering, removal of EV-charging bottlenecks, freed grid capacity for data centers and industrial electrification, as well as a trend toward low, predictable, fixed-cost clean energy. Evidence from early municipal demonstrators illustrates feasibility and migration paths. The contribution is a coherent, open, and testable blueprint for software-defined, decentralized energy distribution, aligning power-systems engineering with networking principles and offering a practical route from legacy, synchronous grids to resilient, digitally routed energy distribution systems.
Authors: Phillippe K. Phanivong, Duncan S. Callaway
Regulators and utilities have been exploring hourly retail electricity pricing, with several existing programs providing day-ahead hourly pricing schedules. At the same time, customers are deploying distributed energy resources and smart energy management systems that have significant flexibility and can optimally follow price signals. In aggregate, these optimally controlled loads can create congestion management issues for distribution system operators (DSOs). In this paper, we describe a new linear pricing mechanism for day-ahead retail electricity pricing that provides a signal for customers to follow to mitigate over-consumption while still consuming energy at hours that are preferential for system performance. We show that by broadcasting a linear price designed for price-signal control of cost-optimizing loads, we can shape customer load profiles to provide congestion management without the need for bi-directional communication or customer bidding programs.
Authors: S Krishna Niketh, Sagar Babu Mitikiri, V Vignesh, Vedantham Lakshmi Srinivas, Mayukha Pal
The increasing reliance on cyber physical infrastructure in modern power systems has amplified the risk of targeted cyber attacks, necessitating robust and adaptive resilience strategies. This paper presents a mathematically rigorous game theoretic framework to evaluate and enhance microgrid resilience using a combination of quantitative resilience metrics Load Served Ratio LSR, Critical Load Resilience CLR, Topological Survivability Score TSS, and DER Resilience Score DRS. These are integrated into a unified payoff matrix using the Analytic Hierarchy Process AHP to assess attack defense interactions. The framework is formalized as a finite horizon Markov Decision Process MDP with formal convergence guarantees and computational complexity bounds. Three case studies are developed 1. static attacks analyzed via Nash equilibrium, 2. severe attacks incorporating high impact strategies, and 3. adaptive attacks using Stackelberg games, regret matching, softmax heuristics, and Multi Agent Q Learning. Rigorous theoretical analysis provides convergence proofs with explicit rates , PAC learning sample complexity bounds, and computational complexity analysis. The framework is tested on an enhanced IEEE 33bus distribution system with DERs and control switches, demonstrating the effectiveness of adaptive and strategic defenses in improving cyber physical resilience with statistically significant improvements of 18.7% 2.1% over static approaches.
Authors: Ueli Schilt, Somesh Vijayananda, Sarah Schneeberger, Manuel Meyer, Santhosh Iyyakkunnel, Pascal Marc Vecsei, Philipp Schuetz
Achieving net-zero targets requires the phase-out of fossil-based heating. A major challenge is the seasonal mismatch between renewable heat supply and demand. District heating networks often dispose of excess heat in summer and rely on fossil backups in winter. Large-scale thermal energy storage offers a solution by storing surplus summer heat for use during winter, thus reducing the need for fossil fuels. This study investigates the feasibility of a large-scale thermal storage system at a power production site that supplies a large district heating network in the city of Bern, Switzerland. Specifically, the study examines the potential of a geothermal storage system to offset fossil fuel heat generation in winter by utilising heat stored during the summer months. Using a Python-based multi-energy system model, we simulate the optimal operation of the geothermal storage system with respect to cost and emissions, considering both supply and demand on an hourly basis over one year. Multi-objective optimisation is applied to generate a Pareto-optimal front. The results show that the geothermal storage system eliminates the requirement of 8 GWh of gas-powered heat supply and increases the waste heat utilisation by 20%, therefore lowering emissions. This effect is further increased when combined with an expansion of the district heating network, as individual, emission-heavy heaters are replaced by low-emission heat from the district heating network. The findings presented in this study can prove useful when evaluating similar systems across Switzerland.
Authors: Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin
Implementing economy-wide decarbonization strategies based on decarbonizing the power grid via variable renewable energy (VRE) expansion and electrification of end-uses requires new approaches for energy infrastructure planning that consider, among other factors, weather-induced uncertainty in demand and VRE supply. An energy planning model that fails to account for these uncertainties can hinder the intended transition efforts to a low-carbon grid and increase the risk of supply shortage especially during extreme weather conditions. Here, we consider the generation and transmission expansion problem of joint power-gas infrastructure and operations planning under the uncertainty of both demand and renewable supply. We propose two distributionally robust optimization approaches based on moment (MDRO) and Wasserstein distance (WDRO) ambiguity sets to endogenize these uncertainties and account for the change in the underlying distribution of these parameters that is caused by the climate change, among other factors. Furthermore, our model considers the risk-aversion of the energy planners in the modeling framework via the conditional value-at-risk (CVaR) metric. An equivalent mixed-integer linear programming (MILP) reformulation of both modeling frameworks is presented, and a computationally efficient approximation scheme to obtain near-optimal solutions is proposed. We demonstrate the resulting DRO planning models and solution strategy via a New England case study under different levels of end-use electrification and decarbonization targets. Our experiments systematically explore different modeling aspects and compare the DRO models with stochastic programming (SP) results.
Authors: Xin Chen, Xiaoyang Wang, Ana Colacelli, Matt Lee, Le Xie
The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, this review aims to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation.
Authors: Mehdi Davoudi, Junjie Qin, Xiaojun Lin
This study investigates market-driven long-term investment decisions in distributed solar panels by individual investors. We consider a setting where investment decisions are driven by expected revenue from participating in short-term electricity markets over the panel's lifespan. These revenues depend on short-term markets equilibria, i.e., prices and allocations, which are influenced by aggregate invested panel capacity participating in the markets. We model the interactions among investors by a non-atomic game and develop a framework that links short-term markets equilibria to the resulting long-term investment equilibrium. Then, within this framework, we analyze three market mechanisms: (a) a single-product real-time energy market, (b) a product-differentiated real-time energy market that treats solar energy and grid energy as different products, and (c) a contract-based panel market that trades claims or rights to the production of certain panel capacity ex-ante, rather than the realized solar production ex-post. For each, we derive expressions for short-term equilibria and the associated expected revenues, and analytically characterize the corresponding long-term Nash equilibrium aggregate capacity. We compare the solutions of these characterizing equations under different conditions and theoretically establish that the product-differentiated market always supports socially optimal investment, while the single-product market consistently results in under-investment. We also establish that the contract-based market leads to over-investment when the extra valuations of users for solar energy are small. Finally, we validate our theoretical findings through numerical experiments.
Authors: Xin Chen, Xiaoyang Wang, Ana Colacelli, Matt Lee, Le Xie
The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, this review aims to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation.
Authors: Xiemin Mo, Tao Liu
High penetration of renewable energy sources intensifies frequency fluctuations in multi-area power systems, challenging both stability and operational safety. This paper proposes a novel distributed frequency control method that ensures transient frequency safety and enforces generation capacity constraints, while achieving steady-state frequency restoration and optimal economic operation. The method integrates a feedback optimization (FO)-based controller and a safety corrector. The FO-based controller generates reference setpoints by solving an optimization problem, driving the system to the steady state corresponding to the optimal solution of this problem. The safety corrector then modifies these references using control barrier functions to maintain frequencies within prescribed safe bounds during transients while respecting capacity constraints. The proposed method combines low computational burden with improved regulation performance and enhanced practical applicability. Theoretical analysis establishes optimality, asymptotic stability, and transient frequency safety for the closed-loop system. Simulation studies show that, compared with conventional FO-based schemes, the method consistently enforces frequency safety and capacity limits, achieves smaller frequency deviations and faster recovery, thereby demonstrating its practical effectiveness and advantages.
Authors: Badr Al Faiya, Stephen McArthur, Ivana Kockar
Distribution networks will experience more installations of distributed generation (DG) that is unpredictable and stochastic in nature. Greater distributed control and intelligence will allow challenges such as voltage control to be handled effectively. The partitioning of power networks into smaller clusters provides a method to split the control problem into manageable sub-problems. This paper presents a community detection-based partitioning technique for distribution networks considering local DGs, allowing them to be grouped and controlled in a distributed manner by using local signals and measurements. This method also allows each community to control the voltage using only neighboring DGs, and for each community to self-organize to reflect varying DG conditions and to maintain stable control. Simulations demonstrate that the partitioning of the large distribution network is effective, and each community is able to self-organize and to regulate the voltage independently using only its local DGs.
Authors: Phuoc Sang Nguyen, Ghavameddin Nourbakhsh, Gerard Ledwich
Modern power systems face new operational hurdles due to the increasing adoption of inverter-coupled distributed energy resources, which impact system stability and control. Central to these challenges is the dynamic nature of grid impedance. To address this, a novel real-time estimation algorithm based on the Discrete Fourier Transform is proposed. This algorithm is embedded within an Advanced Angle Estimation Kalman Filter framework that employs a Linear Quadratic Regulator for current control (AAEKF-LQR). The impedance data directly informs and refines the controller's phase angle estimation. Simulation analyses demonstrate robust collaboration between the estimator and controller, sustaining system stability under weak grid conditions. The technique proves capable of delivering swift and accurate impedance updates during grid variations, which is crucial for maintaining stable inverter operation
Authors: Nicholas Julian Behr, Mattia Bianchi, Keith Moffat, Saverio Bolognani, Florian Dörfler
Online Feedback Optimization (OFO) controllers iteratively drive a plant to an optimal operating point that satisfies input and output constraints, relying solely on the input-output sensitivity as model information. This paper introduces PRIME (PRoximal Iterative MarkEts), a novel OFO approach based on proximal-point iterations. Unlike existing OFO solutions, PRIME admits a market-based implementation, where self-interested actors are incentivized to make choices that result in safe and efficient operation, without communicating private costs or constraints. Furthermore, PRIME can handle non-smooth objective functions, achieve fast convergence rates and rapid constraint satisfaction, and effectively reject measurement noise. We demonstrate PRIME on an AC optimal power flow problem, obtaining an efficient real-time nonlinear local marginal pricing scheme.
Authors: Kecheng Zhang, Ya-Feng Liu, Zhongbin Wang, Weijie Yuan, Musa Furkan Keskin, Henk Wymeersch, Shuqiang Xia
Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm.
Authors: Ahmad Bazzi, Roberto Bomfin, Marco Mezzavilla, Sundeep Rangan, Theodore Rappaport, Marwa Chafii
Driven by the pursuit of gigabit-per-second data speeds for future 6G mobile networks, in addition to the support of sensing and artificial intelligence applications, the industry is expanding beyond crowded sub-6 GHz bands with innovative new spectrum allocations. In this paper, we chart a compelling vision for 6G within the frequency range 3 (FR3) spectrum, i.e. $7.125$-$24.25$ $\GHz$, by delving into its key enablers and addressing the multifaceted challenges that lie ahead for these new frequency bands. Here we highlight the physical properties of this never-before used spectrum for cellular by reviewing recent channel measurements for outdoor and indoor environments, including path loss, delay and angular spreads, and material penetration loss, all which offer insights that underpin future 5G/6G wireless communication designs. Building on the fundamental knowledge of the channel properties, we explore FR3 spectrum agility strategies that balance coverage and capacity tradeoffs, while examining coexistence with incumbent systems, such as satellites, radio astronomy, and earth exploration. Moreover, we discuss the potential of massive multiple-input multiple-output technologies, challenges for commercial deployment, and potential solutions for FR3, including multiband sensing for FR3 integrated sensing and communications. Finally, we outline 6G standardization features that are likely to emerge from 3GPP radio frame innovations and open radio access network developments.
Authors: Brian Nelson, Hussein Moradi, Behrouz Farhang-Boroujeny
The idea of ultra-wideband (UWB) communications for short ranges (up to a few tens of meters) has been around for nearly three decades. However, despite significant efforts by the industry, UWB deployment has not yet reached its predicted potential. This article, thus, seeks to rectify this situation by providing a practical examination of UWB interference conditions. Through a spectrum survey of today's wireless environments, we explore the interference that UWB devices may face from a perspective of outage probability in both high- and low-rate configurations. We find that by suppressing interference, the outage probability can be reduced by one or more orders of magnitude. In the non-line-of sight channels, in particular, we find that both interference suppression and bandwidth expansion are required to support the minimum data rates suggested in the IEEE802.15.4 series of standards. We connect these findings to a recently proposed UWB signaling method based on filter banks and show this method fulfills the above requirements for implementing effective UWB systems.
Authors: Sebastian Macaluso, Giovanni Geraci, Elías F. Combarro, Sergi Abadal, Ioannis Arapakis, Sofia Vallecorsa, Eduard Alarcón
The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum computing (QC) emerges as a promising technology for efficient large-scale optimization. We present our vision of leveraging QC to tackle key classes of problems in future mobile networks. By analyzing and identifying common features, particularly their graph-centric representation, we propose a unified strategy involving QC algorithms. Specifically, we outline a methodology for optimization using quantum annealing as well as quantum reinforcement learning. Additionally, we discuss the main challenges that QC algorithms and hardware must overcome to effectively optimize future networks.
Authors: Friedrich Wolf-Monheim
Next to decision tree and k-nearest neighbours algorithms deep convolutional neural networks (CNNs) are widely used to classify audio data in many domains like music, speech or environmental sounds. To train a specific CNN various spectral and rhythm features like mel-scaled spectrograms, mel-frequency cepstral coefficients (MFCC), cyclic tempograms, short-time Fourier transform (STFT) chromagrams, constant-Q transform (CQT) chromagrams and chroma energy normalized statistics (CENS) chromagrams can be used as digital image input data for the neural network. The performance of these spectral and rhythm features for audio category level as well as audio class level classification is investigated in detail with a deep CNN and the ESC-50 dataset with 2,000 labeled environmental audio recordings using an end-to-end deep learning pipeline. The evaluated metrics accuracy, precision, recall and F1 score for multiclass classification clearly show that the mel-scaled spectrograms and the mel-frequency cepstral coefficients (MFCC) perform significantly better then the other spectral and rhythm features investigated in this research for audio classification tasks using deep CNNs.
Authors: Muzaffar Habib, Adnan Maqsood, Adnan Fayyaz ud Din
This study presents a novel reinforcement learning (RL)-based control framework aimed at enhancing the safety and robustness of the quadcopter, with a specific focus on resilience to in-flight one propeller failure. Addressing the critical need of a robust control strategy for maintaining a desired altitude for the quadcopter to safe the hardware and the payload in physical applications. The proposed framework investigates two RL methodologies Dynamic Programming (DP) and Deep Deterministic Policy Gradient (DDPG), to overcome the challenges posed by the rotor failure mechanism of the quadcopter. DP, a model-based approach, is leveraged for its convergence guarantees, despite high computational demands, whereas DDPG, a model-free technique, facilitates rapid computation but with constraints on solution duration. The research challenge arises from training RL algorithms on large dimensions and action domains. With modifications to the existing DP and DDPG algorithms, the controllers were trained not only to cater for large continuous state and action domain and also achieve a desired state after an inflight propeller failure. To verify the robustness of the proposed control framework, extensive simulations were conducted in a MATLAB environment across various initial conditions and underscoring its viability for mission-critical quadcopter applications. A comparative analysis was performed between both RL algorithms and their potential for applications in faulty aerial systems.
Authors: Shima Poorgholam-Khanjari, Paniz Foshat, Mingqi Zhang, Valentino Seferai, Martin Weides, Kaveh Delfanazari
The performance and scalability of superconducting quantum circuits are fundamentally constrained by non-equilibrium quasiparticles, which induce microwave losses that limit resonator quality factors and qubit coherence times. Understanding and mitigating these excitations is therefore central to advancing scalable quantum technologies. Here, we demonstrate on-chip microwave sensing of quasiparticles in high-Q {\alpha}-tantalum coplanar waveguide resonators on silicon, operated in the single-photon regime. Temperature-dependent measurements reveal persistent non-equilibrium quasiparticles at millikelvin temperatures, producing a measurable suppression of the internal quality factor (Qi) relative to theoretical expectations. By benchmarking across materials, we find that the quasiparticle density in {\alpha}-Ta is approximately one-third that of NbN at equivalent normalised temperatures (T/Tc), directly correlating with reduced microwave loss. Our methodology establishes a scalable platform for probing quasiparticle dynamics and points towards new routes for engineering superconducting circuits with improved coherence, with impact on qubit readout resonators, kinetic-inductance detectors, and emerging quantum processors and sensors.
Authors: Paolo Combes, Stefan Weinzierl, Klaus Obermayer
Deep learning appears as an appealing solution for Automatic Synthesizer Programming (ASP), which aims to assist musicians and sound designers in programming sound synthesizers. However, integrating software synthesizers into training pipelines is challenging due to their potential non-differentiability. This work tackles this challenge by introducing a method to approximate arbitrary synthesizers. Specifically, we train a neural network to map synthesizer presets onto an audio embedding space derived from a pretrained model. This facilitates the definition of a neural proxy that produces compact yet effective representations, thereby enabling the integration of audio embedding loss into neural-based ASP systems for black-box synthesizers. We evaluate the representations derived by various pretrained audio models in the context of neural-based nASP and assess the effectiveness of several neural network architectures, including feedforward, recurrent, and transformer-based models, in defining neural proxies. We evaluate the proposed method using both synthetic and hand-crafted presets from three popular software synthesizers and assess its performance in a synthesizer sound matching downstream task. While the benefits of the learned representation are nuanced by resource requirements, encouraging results were obtained for all synthesizers, paving the way for future research into the application of synthesizer proxies for neural-based ASP systems.
Authors: Gavin Tao, Yinuo Wang, Jinzhao Zhou
End-to-end reinforcement learning for motion control promises unified perception-action policies that scale across embodiments and tasks, yet most deployed controllers are either blind (proprioception-only) or rely on fusion backbones with unfavorable compute-memory trade-offs. Recurrent controllers struggle with long-horizon credit assignment, and Transformer-based fusion incurs quadratic cost in token length, limiting temporal and spatial context. We present a vision-driven cross-modal RL framework built on SSD-Mamba2, a selective state-space backbone that applies state-space duality (SSD) to enable both recurrent and convolutional scanning with hardware-aware streaming and near-linear scaling. Proprioceptive states and exteroceptive observations (e.g., depth tokens) are encoded into compact tokens and fused by stacked SSD-Mamba2 layers. The selective state-space updates retain long-range dependencies with markedly lower latency and memory use than quadratic self-attention, enabling longer look-ahead, higher token resolution, and stable training under limited compute. Policies are trained end-to-end under curricula that randomize terrain and appearance and progressively increase scene complexity. A compact, state-centric reward balances task progress, energy efficiency, and safety. Across diverse motion-control scenarios, our approach consistently surpasses strong state-of-the-art baselines in return, safety (collisions and falls), and sample efficiency, while converging faster at the same compute budget. These results suggest that SSD-Mamba2 provides a practical fusion backbone for scalable, foresightful, and efficient end-to-end motion control.
Authors: Sungjun Eom, Gyunghoon Park
This work addresses an extended class of optimal control problems where a target for a system state has the form of an ellipsoid rather than a fixed, single point. As a computationally affordable method for resolving the extended problem, we present a revised version of the differential dynamic programming (DDP), termed the differential dynamic programming with ellipsoidal target set (ETS-DDP). To this end, the problem with an ellipsoidal target set is reformulated into an equivalent form with the orthogonal projection operator, yielding that the resulting cost functions turn out to be discontinuous at some points. As the DDP usually requires the differentiability of cost functions, in the ETS-DDP formulation we locally approximate the (nonsmooth) cost functions to smoothed ones near the path generated at the previous iteration, by utilizing the explicit form of the orthogonal projection operator. Moreover, a statistical inference method is also presented for designing the ellipsoidal target set, based on data on admissible target points collected by expert demonstrations. Via a simulation on autonomous parking of a vehicle, it is seen that the proposed ETS-DDP efficiently derives an admissible state trajectory while running much faster than the point-targeted DDP, at the expense of optimality.
Authors: Rui Yang, Lei Zheng, Shuzhi Sam Ge, Jun Ma
Autonomous vehicles must navigate dynamically uncertain environments while balancing the safety and driving efficiency. This challenge is exacerbated by the unpredictable nature of surrounding human-driven vehicles (HVs) and perception inaccuracies, which require planners to adapt to evolving uncertainties while maintaining safe trajectories. Overly conservative planners degrade driving efficiency, while deterministic approaches may encounter serious issues and risks of failure when faced with sudden and unexpected maneuvers. To address these issues, we propose a real-time contingency trajectory optimization framework in this paper. By employing event-triggered online learning of HV control-intent sets, our method dynamically quantifies multi-modal HV uncertainties and refines the forward reachable set (FRS) incrementally. Crucially, we enforce invariant safety through FRS-based barrier constraints that ensure safety without reliance on accurate trajectory prediction of HVs. These constraints are embedded in contingency trajectory optimization and solved efficiently through consensus alternative direction method of multipliers (ADMM). The system continuously adapts to the uncertainties in HV behaviors, preserving feasibility and safety without resorting to excessive conservatism. High-fidelity simulations on highway and urban scenarios, as well as a series of real-world experiments demonstrate significant improvements in driving efficiency and passenger comfort while maintaining safety under uncertainty. The project page is available at this https URL.
Authors: Arslan Majal, Aamir Hussain Chughtai, Muhammad Tahir
We present a learning-based outlier-robust filter for a general setup where the measurement noise can be correlated. Since it is an enhanced version of EM-based outlier robust filter (EMORF), we call it as EMORF-II. As it is equipped with an additional powerful feature to learn the outlier characteristics during inference along with outlier-detection, EMORF-II has improved outlier-mitigation capability. Numerical experiments confirm performance gains as compared to the state-of-the-art methods in terms of accuracy with an increased computational overhead. However, thankfully the computational complexity order remains at par with other practical methods making it a useful choice for diverse applications.
Authors: Kweku Enninful, Fardeen Ahmed, Bradley Girod, Richard Laforest, Daniel L. J. Thorek, Vikas Prasad, Abhinav K. Jha
The theranostic paradigm enables personalization of treatment by selecting patients with a diagnostic radiopharmaceutical and monitoring therapy using a matched therapeutic isotope. This strategy relies on accurate image reconstruction of both pre-therapy and post-therapy images for patient selection and monitoring treatment. However, traditional reconstruction methods are hindered by challenges such as crosstalk in multi-isotope imaging and extremely low-count measurements when imaging of alpha- ({\alpha}-) emitting therapies. Additionally, to fully realize the benefits of new imaging systems being developed for theranostic applications, advanced reconstruction techniques are needed. These needs, alongside the growing clinical adoption of theranostics, have spurred the development of novel PET and SPECT reconstruction algorithms. This review highlights recent progress and addresses critical challenges and unmet needs in theranostic image reconstruction.
Authors: Nida Alyas, Jonathan Horsley, Peter N. Taylor, Yujiang Wang, Karoline Leiberg
Normative modelling is an increasingly common statistical technique in neuroimaging that estimates population-level benchmarks in brain structure. It enables the quantification of individual deviations from expected distributions whilst accounting for biological and technical covariates without requiring large, matched control groups. This makes it a powerful alternative to traditional case-control studies for identifying brain structural alterations associated with pathology. Despite the availability of numerous modelling approaches and several toolboxes with pretrained models, the distinct strengths and limitations of normative modelling make it difficult to determine how and when to implement them appropriately. This review offers practical guidance and outlines statistical considerations for clinical researchers using normative modelling in neuroimaging. We compare several open-source normative modelling tools through a worked example using clinical epilepsy data; outlining decision points, common pitfalls, and considerations for responsible implementation, to support broader and more rigorous adoption of normative modelling in neuroimaging research.
Authors: William Chen, Chutong Meng, Jiatong Shi, Martijn Bartelds, Shih-Heng Wang, Hsiu-Hsuan Wang, Rafael Mosquera, Sara Hincapie, Dan Jurafsky, Antonis Anastasopoulos, Hung-yi Lee, Karen Livescu, Shinji Watanabe
Recent improvements in multilingual ASR have not been equally distributed across languages and language varieties. To advance state-of-the-art (SOTA) ASR models, we present the Interspeech 2025 ML-SUPERB 2.0 Challenge. We construct a new test suite that consists of data from 200+ languages, accents, and dialects to evaluate SOTA multilingual speech models. The challenge also introduces an online evaluation server based on DynaBench, allowing for flexibility in model design and architecture for participants. The challenge received 5 submissions from 3 teams, all of which outperformed our baselines. The best-performing submission achieved an absolute improvement in LID accuracy of 23% and a reduction in CER of 18% when compared to the best baseline on a general multilingual test set. On accented and dialectal data, the best submission obtained 30.2% lower CER and 15.7% higher LID accuracy, showing the importance of community challenges in making speech technologies more inclusive.
Authors: Jingyi Yin, Jingke Zhang, Lijie Huang, U-Wai Lok, Ryan M DeRuiter, Kaipeng Ji, Yanzhe Zhao, Kate M. Knoll, Kendra E. Petersen, Tao Wu, Xiang-yang Zhu, James D Krier, Kathryn A. Robinson, Lilach O Lerman, Andrew J. Bentall, Shigao Chen, Chengwu Huang
Microvascular imaging has advanced significantly with ultrafast data acquisition and improved clutter filtering, enhancing the sensitivity of power Doppler imaging to small vessels. However, the image quality remains limited by spatial resolution and elevated background noise, both of which impede visualization and accurate quantification. To address these limitations, this study proposes a high-resolution cross-correlation Power Doppler (HR-XPD) method that integrates spatial radiality weighting with Doppler signal coherence analysis, thereby enhancing spatial resolution while suppressing artifacts and background noise. Quantitative evaluations in simulation and in vivo experiments on healthy human liver, transplanted human kidney, and pig kidney demonstrated that HR-XPD significantly improves microvascular resolvability and contrast compared to conventional PD. In vivo results showed up to a 2 to 3-fold enhancement in spatial resolution and an increase in contrast by up to 20 dB. High-resolution vascular details were clearly depicted within a short acquisition time of only 0.3 s-1.2 s without the use of contrast agents. These findings indicate that HR-XPD provides an effective, contrast-free, and high-resolution microvascular imaging approach with broad applicability in both preclinical and clinical research.
Authors: Yerin Ryu, Inseop Shin, Chanwoo Kim
Controllable Singing Voice Synthesis (SVS) aims to generate expressive singing voices reflecting user intent. While recent SVS systems achieve high audio quality, most rely on probabilistic modeling, limiting precise control over attributes such as dynamics. We address this by focusing on dynamic control--temporal loudness variation essential for musical expressiveness--and explicitly condition the SVS model on energy sequences extracted from ground-truth spectrograms, reducing annotation costs and improving controllability. We also propose a phoneme-level energy sequence for user-friendly control. To the best of our knowledge, this is the first attempt enabling user-driven dynamics control in SVS. Experiments show our method achieves over 50% reduction in mean absolute error of energy sequences for phoneme-level inputs compared to baseline and energy-predictor models, without compromising synthesis quality.
Authors: Anton Kolonin, Vladimir Kryukov
The article provides an overview of approaches to modeling the human psyche in the perspective of building an artificial one. Based on the review, a concept of cognitive architecture is proposed, where the psyche is considered as an operating system of a living or artificial subject, including a space of needs that determines its life meanings in connection with stimuli from the external world, and intelligence as a decision-making system for actions in relation to this world in order to satisfy these needs. Based on the concept, a computational formalization is proposed for creating artificial intelligence systems through learning from experience in the space of a space of needs, taking into account their biological or existential significance for an intelligent agent. Thus, the problem of building general artificial intelligence as a system for making optimal decisions in the space of agent-specific needs under conditions of uncertainty is formalized, with maximization of success in achieving goals, minimization of existential risks and maximization of energy efficiency. A minimal experimental implementation of the model is also provided.
Authors: Jimmy Joseph
In this paper, we introduce the Protocol Genome, a self-supervised learning system that learns correlations from DICOM headers and achieves AUROC 0.901 (vs 0.847 baseline) and ECE 0.036 (vs 0.058) on fully held-out external validation. Our method also improves calibration and robustness across modalities (CT, MRI, CXR) and vendors. Clinical imaging is funneled through PACS/DICOM, where procedure choices (scanner make/model, sequence, kernel, kVp, TR/TE, and slice thickness) have consequences for contrast, noise, and artifact. These latent confounders impede the generalization of image-only networks across sites. We consider structured DICOM headers as a label and learn protocol-aware but clinically robust image representations. Protocol Genome obtains tokenized embeddings of de-identified header fields and models them along with image features using: (1) protocol-image contrastive learning, (2) masked protocol prediction, and (3) protocol-protocol translation. With 1.26M studies (7 health systems, 31 scanners, 3 vendors; CT, MR, CR/DR), we experiment on: (A) chest CT triage for PE, (B) brain MRI glioma grading, and (C) chest radiograph cardiomegaly detection. Relative to strong SSL baselines (SimCLR, MAE) as well as ImageNet transfer, Protocol Genome (+0.046: PE, +0.058: glioma, +0.041: cardiomegaly) is associated with higher external AUROC; 25-37% calibration improvements are obtained (p < 0.01, DeLong tests). While the gains may be task-dependent, they are preserved with 10-20% of labeled data. From a clinical point of view, the technique reduces false positives at protocol borders and is applicable in a PACS (DICOM C-FIND/C-MOVE, DICOMweb QIDO/WADO). We publish a model card and deployment guide, complete with both de-identification and bias audits.
Authors: Huihong Liang, Dongxuan Jia, Youquan Wang, Longtao Huang, Shida Zhong, Luping Xiang, Lei Huang, Tao Yuan
In this demo, we present a compact intelligent audio system-on-chip (SoC) integrated with a keyword spotting accelerator, enabling ultra-low latency, low-power, and low-cost voice interaction in Internet of Things (IoT) devices. Through algorithm-hardware co-design, the system's energy efficiency is maximized. We demonstrate the system's capabilities through a live FPGA-based prototype, showcasing stable performance and real-time voice interaction for edge intelligence applications.
Authors: Patrick Kreidl
We formulate and analyze a simplest Markov decision process model for intrusion tolerance problems, assuming that (i) each attack proceeds through one or more steps before the system's security fails, (ii) defensive responses that target these intermediate steps may only sometimes thwart the attack and (iii) reset responses that are sensible upon discovering an attack's completion may not always recover from the security failure. The analysis shows that, even in the ideal case of perfect detectors, it can be sub-optimal in the long run to employ defensive responses while under attack; that is, depending on attack dynamics and response effectiveness, the total overhead of ongoing defensive countermeasures can exceed the total risk of intermittent security failures. The analysis similarly examines the availability loss versus the risk reduction of employing preemptive resets, isolating key factors that determine whether system recovery is best initiated reactively or proactively. We also discuss model extensions and related work looking towards intrusion tolerance applications with (i) imperfect or controllable detectors, (ii) multiple types of attacks, (iii) continuous-time dynamics or (iv) strategic attackers.
Authors: Prashil Wankhede, Nirabhra Mandal, Sonia Martínez, Pavankumar Tallapragada
We propose a model of opinion formation on resource allocation among multiple topics by multiple agents, who are subject to hard budget constraints. We define a utility function for each agent and then derive a projected dynamical system model of opinion evolution assuming that each agent myopically seeks to maximize its utility subject to its constraints. Inter-agent coupling arises from an undirected social network, while inter-topic coupling arises from resource constraints. We show that opinions always converge to the equilibrium set. For special networks with very weak antagonistic relations, the opinions converge to a unique equilibrium point. We further show that the underlying opinion formation game is a potential game. We relate the equilibria of the dynamics and the Nash equilibria of the game and characterize the unique Nash equilibrium for networks with no antagonistic relations. Finally, simulations illustrate our findings.
Authors: Alexander Dorsey, Ankit Goel
This paper presents an input-output feedback linearization (IOL)-based guidance law to ensure interception in a pursuer-evader engagement scenario. A point-mass dynamic model for both the pursuer and the evader is considered. An IOL guidance law is derived using range and line-of-sight (LOS) rate measurements. It is found that the range-based IOL guidance law exhibits a singularity under certain conditions. To address this issue, a fuzzy logic system is employed to smoothly blend the IOL guidance with the classical proportional guidance law, thereby avoiding the singularity. In contrast, the LOS-based IOL guidance law is free of singularities but suffers from divergence issues due to angle-related complications. To resolve this, a simple correction function is introduced to ensure consistent interception behavior. Results from Monte Carlo simulations indicate that both modifications of the IOL guidance laws cause interception with control limits applied.
Authors: Patrick Kreidl
Technological advancements in miniaturization and wireless communications are yielding more affordable and versatile sensors and, in turn, more applications in which a network of sensors can be actively managed to best support overall decision-making objectives. We propose modeling the opportunity for sensor management within multi-stage stochastic control problems with imperfect state information. Such formulations inherently assume the state of the modeled environment cannot be accessed directly but instead the controller can observe only noisy measurements of the state and, therefore, at each decision stage some form of state estimation is required before a control is actuated. The notion of sensor management arises when the modeled controls not only affect the subsequent evolution of the state but can also affect the nature of future measurements and, hence, the quality of state estimates that drive future control decisions. In principle, the optimal strategy for any appropriately modeled multi-stage stochastic control problem with imperfect state information (with or without opportunity for sensor management) is the solution to a dynamic program; in practice, the computational requirements are typically prohibitive yet dynamic programming methods are still useful to guide the development of effective suboptimal strategies. In this spirit, we model the opportunity for sensor management within small-scale examples of two well-studied dynamic programming formulations, namely (1) the finite-state/finite-action Partially-Observable Markov Decision Process (PO-MDP) and (2) the Linear-Quadratic-Gaussian Regulator (LQGR). These examples admit solvable dynamic programs and confirm how the interplay between sensing and acting is a natural by-product of a dynamic programming solution.
Authors: Florian Strasser, Marion Bäro, Wolfgang Utschick
We propose enhancements to score-based generative modeling techniques for low-latency pilot-based channel estimation in a point-to-point single-carrier multiple-input multiple-output (MIMO) wireless system. Building on recent advances in score-based models, we investigate a specific noise schedule design and sampling acceleration by step-skipping to reduce the number of denoising steps during inference. We additionally propose a single-step signal-to-noise ratio informed denoiser as an extreme case of the step-skipping approach. Our methods achieve significant latency reductions without performance degradation, as demonstrated on a synthetic channel dataset representing an urban macrocell MIMO communications scenario.
Authors: Angel L. Cedeño, Rodrigo A. González, Boris I. Godoy, Juan C. Agüero
This work addresses the problem of state estimation in multivariable dynamic systems with quantized outputs, a common scenario in applications involving low-resolution sensors or communication constraints. A novel method is proposed to explicitly construct the probability mass function associated with the quantized measurements by approximating the indicator function of each region defined by the quantizer using Gaussian mixture models. Unlike previous approaches, this technique generalizes to any number of quantized outputs without requiring case-specific numerical solutions, making it a scalable and efficient solution. Simulation results demonstrate that the proposed filter achieves high accuracy in state estimation, both in terms of fidelity of the filtering distributions and mean squared error, while maintaining significantly reduced computational cost.
Authors: S M Asiful Islam Saky, Ugyen Tshering
Optical Coherence Tomography (OCT) is essential for diagnosing conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration. Accurate retinal layer segmentation enables quantitative biomarkers critical for clinical decision-making, but manual segmentation is time-consuming and variable, while conventional deep learning models often lack interpretability. This work proposes an improved SegNet-based deep learning framework for automated and interpretable retinal layer segmentation. Architectural innovations, including modified pooling strategies, enhance feature extraction from noisy OCT images, while a hybrid loss function combining categorical cross-entropy and Dice loss improves performance for thin and imbalanced retinal layers. Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to provide visual explanations, allowing clinical validation of model decisions. Trained and validated on the Duke OCT dataset, the framework achieved 95.77% validation accuracy, a Dice coefficient of 0.9446, and a Jaccard Index (IoU) of 0.8951. Class-wise results confirmed robust performance across most layers, with challenges remaining for thinner boundaries. Grad-CAM visualizations highlighted anatomically relevant regions, aligning segmentation with clinical biomarkers and improving transparency. By combining architectural improvements, a customized hybrid loss, and explainable AI, this study delivers a high-performing SegNet-based framework that bridges the gap between accuracy and interpretability. The approach offers strong potential for standardizing OCT analysis, enhancing diagnostic efficiency, and fostering clinical trust in AI-driven ophthalmic tools.
Authors: Yu Ge, Ossi Kaltiokallio, Elizaveta Rastorgueva-Foi, Musa Furkan Keskin, Hui Chen, Guillaume Jornod, Jukka Talvitie, Mikko Valkama, Frank Hofmann, Henk Wymeersch
The integration of sensing and communication (ISAC) is a cornerstone of 6G, enabling simultaneous environmental awareness and communication. This paper explores radio SLAM (simultaneous localization and mapping) as a key ISAC approach, using radio signals for mapping and localization. We analyze radio SLAM across different frequency bands, discussing trade-offs in coverage, resolution, and hardware requirements. We also highlight opportunities for integration with sensing, positioning, and cooperative networks. The findings pave the way for standardized solutions in 6G applications such as autonomous systems and industrial robotics.
Authors: Pietro Savazzi, Anna Vizziello, Sherif Badran, Josep M. Jornet
This paper proposes and experimentally evaluates a joint clock recovery (CR) and equalization architecture tailored for high-speed sub-terahertz (sub-THz) wireless communication links. Specifically, a Baud-spaced digital receiver architecture is investigated that combines a constant modulus algorithm (CMA) equalizer with a blind timing error detector (TED), enabling robust symbol timing synchronization without decision-directed (DD) feedback or pilot symbols. The proposed TED leverages the CMA filter coefficients to estimate timing errors, which are then used to drive a Farrow interpolator operating at twice the symbol rate. The system is validated experimentally using a 140~GHz wireless testbed with 16-QAM modulation over a 10~GHz bandwidth. Results show that the proposed TED schemes outperform conventional blind TEDs, such as Gardner and blind implementations of Mueller \& Müller, in terms of bit error rate (BER), error vector magnitude (EVM), and intersymbol interference (ISI) suppression. These capabilities are especially relevant to next-generation spaceborne communication systems, where wideband sub-THz links are expected to play a key role in enabling ultra-high-data-rate inter-satellite and deep-space communications under challenging synchronization constraints.
Authors: Felix Artmann, Daniel Gil Gaviria, Benedikt Geiger, Laurent Schmalen
Integrated sensing and communication will be a key feature of future mobile networks, enabling highly efficient systems and numerous new applications by leveraging communication signals for sensing. In this paper, we analyze the impact of arbitrary modulation alphabets on the sensing performance of communication-centric OFDM systems as expected in the next-generation 6G networks. We evaluate existing interference mitigation techniques, such as coherent successive target cancellation, and propose an enhanced version of this algorithm. A systematic performance evaluation in multi-target scenarios, including the effects of scattering, demonstrates that our proposed interference mitigation methods achieve performance comparable to sensing-optimal constant modulus signals while utilizing higher order constellations for more efficient communications.
Authors: Alexander Dorsey, Parham Oveissi, Jeffrey D. Barton, Ankit Goel
This paper considers the problem of optimizing a missile autopilot. In particular, the paper investigates the application of an online learning technique to learn and optimize the gains of a three-loop topology autopilot for a planar missile modeled with nonlinear dynamics and nonlinear aerodynamics forces and moments. The classical autopilot for a missile is based on a three-loop topology, where each loop consists of tunable proportional gains. An adaptive three-loop autopilot is constructed by augmenting the classical autopilot's fixed-gain controllers with a learning-based controller, which is recursively optimized using retrospective cost optimization. Numerical simulations show that online learning improves the tracking performance of the classical autopilot in both nominal and off-nominal interception scenarios.
Authors: Sunny K P, Rakesh R Warier
This article proposes a distributed control method for matrix-scaled multi-agent networks aimed at achieving convergence within a user-defined time frame. The control law of each individual agent relies only on information from neighboring agents and is updated at discrete intervals determined by state-dependent triggering functions, reducing the frequency of agent interactions. To this end, first, the controller is augmented with a time-varying gain. Then, the dynamics of the closed-loop system over the finite-time interval is transformed into an infinite-time frame using time scaling. Lyapunov-based analysis is employed to derive suitable triggering conditions that guarantee the asymptotic convergence of the time-transformed system, thereby ensuring the prescribed-time convergence of the original system.
Authors: Cesare Donati, Martina Mammarella, Giuseppe C. Calafiore, Fabrizio Dabbene, Constantino Lagoa, Carlo Novara
This paper presents a kernel-based framework for physics-informed nonlinear system identification. The key contribution is a structured methodology that extends kernel-based techniques to seamlessly integrate partially known physics-based models, improving parameter estimation and overall model accuracy. The proposed method enhances traditional modeling approaches by integrating a parametric model, which provides physical interpretability, with a kernel-based function, which accounts for unmodelled dynamics. The two model's components are identified from data simultaneously, minimizing a suitable cost that balances the relative importance of the physical and the black-box parts of the model. Additionally, nonlinear state smoothing is employed to address scenarios involving state-space models with not fully measurable states. Numerical simulations on an experimental benchmark system demonstrate the effectiveness of the proposed approach, with performance comparisons against state-of-the-art identification techniques.
Authors: Ahsan Mehmood, Ioannis Krikidis, Ghassan M. Kraidy
In this work, we propose the design of modulation schemes that improve the rate-energy region of fluid antenna-assisted simultaneous wireless information and power transfer (SWIPT) systems. By considering the nonlinear characteristics of practical energy harvesting circuits, we formulate a dual-objective rate-energy (RE) region optimization problem to jointly maximize the discrete-input mutual information (DIMI) and harvested current. The problem is solved using the epsilon-constraint method and optimized constellations are designed for various energy harvesting thresholds. We then evaluate the performance of the optimized constellations under three different fluid antenna (FA) port selection strategies: (i) Best Port, (ii) Fixed Port, and (iii) Random Port. Our simulation results demonstrate significant performance gains of optimized constellations over conventional constellations in both information rate and energy harvesting.
Authors: Patricia Hu, Silvan David Peter, Jan Schlüter, Gerhard Widmer
Advances in neural network design and the availability of large-scale labeled datasets have driven major improvements in piano transcription. Existing approaches target either offline applications, with no restrictions on computational demands, or online transcription, with delays of 128-320 ms. However, most real-time musical applications require latencies below 30 ms. In this work, we investigate whether and how the current state-of-the-art online transcription model can be adapted for real-time piano transcription. Specifically, we eliminate all non-causal processing, and reduce computational load through shared computations across core model components and variations in model size. Additionally, we explore different pre- and postprocessing strategies, and related label encoding schemes, and discuss their suitability for real-time transcription. Evaluating the adaptions on the MAESTRO dataset, we find a drop in transcription accuracy due to strictly causal processing as well as a tradeoff between the preprocessing latency and prediction accuracy. We release our system as a baseline to support researchers in designing models towards minimum latency real-time transcription.
Authors: Jinming Wang, Lipeng Zhu, Shuai Han, He Sun, Rui Zhang
This paper proposes a new architecture for the low-earth orbit (LEO) satellite ground station aided by movable antenna (MA) array. Unlike conventional fixed-position antenna (FPA), the MA array can flexibly adjust antenna positions to reconfigure array geometry, for more effectively mitigating interference and improving communication performance in ultra-dense LEO satellite networks. To reduce movement overhead, we configure antenna positions at the antenna initialization stage, which remain unchanged during the whole communication period of the ground station. To this end, an optimization problem is formulated to maximize the average achievable rate of the ground station by jointly optimizing its antenna position vector (APV) and time-varying beamforming weights, i.e., antenna weight vectors (AWVs). To solve the resulting non-convex optimization problem, we adopt the Lagrangian dual transformation and quadratic transformation to reformulate the objective function into a more tractable form. Then, we develop an efficient block coordinate descent-based iterative algorithm that alternately optimizes the APV and AWVs until convergence is reached. Simulation results demonstrate that our proposed MA scheme significantly outperforms traditional FPA by increasing the achievable rate at ground stations under various system setups, thus providing an efficient solution for interference mitigation in future ultra-dense LEO satellite communication networks.
Authors: Ivan Iudice, Domenico Pascarella, Sonia Zappia, Giovanni Cuciniello, Hernan M. R. Giannetta, Marta Albano, Enrico Cavallini
In wireless sensor networks for reusable launchers, the electromagnetic characterization and electromagnetic compatibility analyses are relevant due to the reference operational scenario, which implies a complex, and sometimes dynamic, electromagnetic environment. This work proposes a methodological framework for the design of the network and for the analysis of the related electromagnetic environment within the stages of a given launcher. Based on the preliminary positioning of the network nodes, the framework prescribes a workflow and the related toolset for determining the optimal network topology focusing on the weights, the operation of the transceivers, and the overall radiated power. The optimal network configuration is simulated by using computational electromagnetics strategies in order to assess the electromagnetic environment induced by the sensor network itself. The paper provides some results concerning a case study for a specific launcher.
Authors: Joan Çollaku, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Takumi Takahashi
We propose a novel framework for integrated communication and computing (ICC) transceiver design in time-varying millimeter-wave (mmWave) channels. In particular, in order to cope with the dynamics of time-varying mmWave channels, the detection of communication symbols and the execution of an over-the-air computing (AirComp) operation are performed in parallel with channel tracking, as opposed to existing state-of-the-art (SotA) on ICC where perfect knowledge of the channel at all time instances is typically assumed. For clarity of exposition, we consider a single-input multiple-output (SIMO) uplink scenario where multiple single-antenna user equipment (UE) transmit to a base station (BS) equipped with multiple antennas, such that each UE, or edge device (ED), precodes its own transmit signal, while the BS, or access points (APs), also performs receive beamforming. The proposed transceiver framework then estimates channel state information (CSI) and data symbols in parallel, using a bilinear Gaussian belief propagation (BiGaBP) algorithm for joint channel and data detection (JCDE), aided by a channel prediction (CP) algorithm executed before each estimation window at the BS. The AirComp operation is then executed by means of an optimal combination of the residual signal. Simulation results demonstrate the effectiveness of the proposed scheme in performing ICC in challenging time-varying mmWave channels, with minimal degradation to both communication and computing performance.
Authors: Ashish Patwari, Andrés Alayón Glazunov
As the field of sparse arrays progressed, numerous array designs have been introduced with a focus on larger apertures and higher degrees of freedom (DOFs), resulting in maximally economic sparse arrays (MESAs) that operate with the least number of sensors required to provide a given aperture while ensuring a hole-free difference coarray (DCA). Consequently, MESAs are least robust to sensor failures and cannot afford the failure of even a single sensor. Multifold redundant sparse arrays (MFRSAs) provide a practical solution to the problem of sensor failures in sparse arrays by making sure that the array contains enough sensor pairs necessary to produce each spatial lag multiple times. Owing to this property, a \b{eta}-fold redundant array can withstand simultaneous failure of at least \b{eta}-1 sensors without losing the hole-free DCA property. Nevertheless, MFRSAs are also prone to hidden dependencies that prevent them from being fully robust. In this work, we present a systematic framework to evaluate the robustness of triple redundant sparse linear arrays (TRSLAs) against all possible two-sensor failures. After detailing the proposed approach, we present the failure analysis of representative TRSLAs available in existing literature. It is found that existing TRSLAs have some hidden vulnerabilities against the failure of some peculiar sensor pairs. Corresponding MATLAB programs and numerical simulations are provided for evaluation and use by the array processing community. The proposed approach has a great archival value as it can evaluate the robustness of any present or future TRSLAs through objective means.
Authors: Sangjun Hwang, Chan-Byoung Chae
This paper proposes a method for accurately estimating the relative position between two nodes with unknown locations in a diffusion-based molecular communication environment. A specialized node structure is designed, combining a central absorbing receiver with multiple transmitters placed at predefined spherical coordinates. Pilot molecules are released, and their absorption time and concentration are measured. By partitioning the spherical coordinate space, these spatially distinct measurements serve as input to a multilayer perceptron (MLP)-based model. The proposed method significantly improves the precision of distance and direction estimation. Simulation results demonstrate localization accuracy, confirming the effectiveness of the neural network model in capturing the underlying physical characteristics.
Authors: Feifan Zhang, Yuyang Du, Yifan Xiang, Xiaoyan Liu, Soung Chang Liew
This paper introduces SA-OOSC, a multimodal large language models (MLLM)-distilled semantic communication framework that achieves efficient semantic coding with scenario-aware importance allocations. This approach addresses a critical limitation of existing object-oriented semantic communication (OOSC) systems - assigning static importance values to specific classes of objects regardless of their contextual relevance. Our framework utilizes MLLMs to identify the scenario-augmented (SA) semantic importance for objects within the image. Through knowledge distillation with the MLLM-annotated data, our vectorization/de-vectorization networks and JSCC encoder/decoder learn to dynamically allocate coding resources based on contextual significance, i.e., distinguishing between high-importance objects and low-importance according to the SA scenario information of the task. The framework features three core innovations: a MLLM-guided knowledge distillation pipeline, an importance-weighted variable-length JSCC framework, and novel loss function designs that facilitate the knowledge distillation within the JSCC framework. Experimental validation demonstrates our framework's superior coding efficiency over conventional semantic communication systems, with open-sourced MLLM-annotated and human-verified datasets established as new benchmarks for future research in semantic communications.
Authors: Senith Jayakody, Kalana Jayasooriya, Sashini Liyanage, Roshan Godaliyadda, Parakrama Ekanayake, Chathura Rathnayake
Preterm birth (PTB), defined as delivery before 37 weeks of gestation, is a leading cause of neonatal mortality and long term health complications. Early detection is essential for enabling timely medical interventions. Electrohysterography (EHG) and tocography (TOCO) are promising non invasive tools for PTB prediction, but prior studies often suffer from class imbalance, improper oversampling, and reliance on features with limited physiological relevance. This work presents a machine learning pipeline incorporating robust preprocessing, physiologically grounded feature extraction, and rigorous evaluation. Features were extracted from EHG (and TOCO) signals using Mel frequency cepstral coefficients, statistical descriptors of wavelet coefficients, and peaks of the normalized power spectrum. Signal quality was enhanced via Karhunen Loève Transform (KLT) denoising through eigenvalue based subspace decomposition. Multiple classifiers, including Logistic Regression, Support Vector Machines, Random Forest, Gradient Boosting, Multilayer Perceptron, and CatBoost, were evaluated on the TPEHGT dataset. The CatBoost classifier with KLT denoising achieved the highest performance on fixed interval segments of the TPEHGT dataset, reaching 97.28% accuracy and an AUC of 0.9988. Ablation studies confirmed the critical role of both KLT denoising and physiologically informed features. Comparative analysis showed that including TOCO signals did not substantially improve prediction over EHG alone, highlighting the sufficiency of EHG for PTB detection. These results demonstrate that combining denoising with domain relevant features can yield highly accurate, robust, and clinically interpretable models, supporting the development of cost effective and accessible PTB prediction tools, particularly in low resource healthcare settings.
Authors: Lu Bai, Zengrui Han, Xuesong Cai, Xiang Cheng
The design and technology development of 6G-enabled networked intelligent systems needs an accurate real-time channel model as the cornerstone. However, with the new requirements of 6G-enabled networked intelligent systems, the conventional channel modeling methods face many limitations. Fortunately, the multi-modal sensors equipped on the intelligent agents bring timely opportunities, i.e., the intelligent integration and mutually beneficial mechanism between communications and multi-modal sensing could be investigated based on the artificial intelligence (AI) technologies. In this case, the mapping relationship between physical environment and electromagnetic channel could be explored via Synesthesia of Machines (SoM). This article presents a novel multi-modal intelligent channel modeling (MMICM) framework for 6G-enabled networked intelligent systems, which establishes a nonlinear model between multi-modal sensing and channel characteristics, including large-scale and small-scale channel characteristics. The architecture and features of proposed intelligent modeling framework are expounded and the key technologies involved are also analyzed. Finally, the system-engaged applications and potential research directions of MMICM framework are outlined.
Authors: Lianming Hu, Xiaotong Zhang, Kamal Youcef-Toumi
Electrooculography (EOG) is widely used for gaze tracking in Human-Robot Collaboration (HRC). However, baseline drift caused by low-frequency noise significantly impacts the accuracy of EOG signals, creating challenges for further sensor fusion. This paper presents an Eye Movement Feature-Guided De-drift (FGD) method for mitigating drift artifacts in EOG signals. The proposed approach leverages active eye-movement feature recognition to reconstruct the feature-extracted EOG baseline and adaptively correct signal drift while preserving the morphological integrity of the EOG waveform. The FGD is evaluated using both simulation data and real-world data, achieving a significant reduction in mean error. The average error is reduced to 0.896° in simulation, representing a 36.29% decrease, and to 1.033° in real-world data, corresponding to a 26.53% reduction. Despite additional and unpredictable noise in real-world data, the proposed method consistently outperforms conventional de-drifting techniques, demonstrating its effectiveness in practical applications such as enhancing human performance augmentation.
Authors: Tran Trung Duc, Vu Duc Minh, Nguyen Ngoc Doanh, Pham Gia Nguyen, Laurent El Ghaoui, Ha Minh Hoang
The Electric Vehicle Routing Problem with Time Windows and Station-based or Route-based Charging Options addresses fleet optimization incorporating both conventional charging stations and continuous wireless charging infrastructure. This paper extends Schneider et al.'s foundational EVRP-TW model with arc-based dynamic wireless charging representation, partial coverage modeling, and hierarchical multi-objective optimization prioritizing fleet minimization. Computational experiments on Schneider benchmark instances demonstrate substantial operational benefits, with distance and time improvements ranging from 0.7% to 35.9% in secondary objective components. Analysis reveals that 20% wireless coverage achieves immediate benefits, while 60% coverage delivers optimal performance across all test instances for infrastructure investment decisions.
Authors: Khue Nong Thuc, Khoa Tran Nguyen Anh, Tai Nguyen Huy, Du Nguyen Hao Hong, Khanh Dinh Ba
The Internet of Things (IoT) plays a crucial role in enabling seamless connectivity and intelligent home automation, particularly in food management. By integrating IoT with computer vision, the smart fridge employs an ESP32-CAM to establish a monitoring subsystem that enhances food management efficiency through real-time food detection, inventory tracking, and temperature monitoring. This benefits waste reduction, grocery planning improvement, and household consumption optimization. In high-density inventory conditions, capturing partial or layered images complicates object detection, as overlapping items and occluded views hinder accurate identification and counting. Besides, varied angles and obscured details in multi-layered setups reduce algorithm reliability, often resulting in miscounts or misclassifications. Our proposed system is structured into three core modules: data pre-processing, object detection and management, and a web-based visualization. To address the challenge of poor model calibration caused by overconfident predictions, we implement a variant of focal loss that mitigates over-confidence and under-confidence in multi-category classification. This approach incorporates adaptive, class-wise error calibration via temperature scaling and evaluates the distribution of predicted probabilities across methods. Our results demonstrate that robust functional calibration significantly improves detection reliability under varying lighting conditions and scalability challenges. Further analysis demonstrates a practical, user-focused approach to modern food management, advancing sustainable living goals through reduced waste and more informed consumption.
Authors: Aiping Zhong, Wanlin Lu, Langwen Zhang, Ziyang Bao
This paper proposes a unified adaptive event-triggered model predictive control (ETMPC) scheme for linear parameter-varying (LPV) systems subject to state delays, actuator saturation, and external disturbances. In existing studies, only a limited number of ETMPC methods have attempted to address either state delays or actuator saturation, and even these few methods typically lack co-design optimization between adaptive event-triggering mechanisms and the control law. To overcome these limitations, this paper presents a Lyapunov-Krasovskii-based adaptive ETMPC strategy that enables the co-design optimization of both the triggering mechanism and the controller. Specifically, the event-triggering parameter matrix is adaptively optimized by embedding an internal adaptive variable within the Lyapunov-Krasovskii-like function. Furthermore, the actuator saturation nonlinearity is transformed into a convex hull representation. The infinite-horizon robust optimization problem is reformulated as a convex optimization problem with linear matrix inequality (LMI) constraints. Invariant set constraints are introduced to ensure recursive feasibility, and mean-square input-to-state stability (ISS) under multiple uncertainties is rigorously established. Simulations on an industrial electric heating system validate the proposed method's effectiveness in reducing communication load.
Authors: Qian Zuo, Shujie Wu, Yuzhe Qian
To address non-linear disturbances and uncertainties in complex marine environments, this paper proposes a disturbance-resistant controller for deep-sea cranes. The controller integrates hierarchical sliding mode control, adaptive control, and neural network compensation techniques. By designing a global sliding mode surface, the dynamic coordination between the driving and non-driving subsystems is achieved, ensuring overall system stability. The subsystem surfaces reduce oscillations and enhance tracking accuracy. Adaptive control dynamically adjusts system parameters, enhancing robustness against external uncertainties, while the neural network compensates for time-varying disturbances through real-time learning. The stability of the control scheme is verified on the basis of Lyapunov theory. The simulation results demonstrate that, compared to traditional PID control, the proposed controller exhibits significant advantages in trajectory tracking accuracy, response speed, and disturbance rejection.
Authors: Xiemin Mo, Tao Liu
High penetration of renewable energy sources intensifies frequency fluctuations in multi-area power systems, challenging both stability and operational safety. This paper proposes a novel distributed frequency control method that ensures transient frequency safety and enforces generation capacity constraints, while achieving steady-state frequency restoration and optimal economic operation. The method integrates a feedback optimization (FO)-based controller and a safety corrector. The FO-based controller generates reference setpoints by solving an optimization problem, driving the system to the steady state corresponding to the optimal solution of this problem. The safety corrector then modifies these references using control barrier functions to maintain frequencies within prescribed safe bounds during transients while respecting capacity constraints. The proposed method combines low computational burden with improved regulation performance and enhanced practical applicability. Theoretical analysis establishes optimality, asymptotic stability, and transient frequency safety for the closed-loop system. Simulation studies show that, compared with conventional FO-based schemes, the method consistently enforces frequency safety and capacity limits, achieves smaller frequency deviations and faster recovery, thereby demonstrating its practical effectiveness and advantages.
Authors: Ye Ni, Ruiyu Liang, Xiaoshuai Hao, Jiaming Cheng, Qingyun Wang, Chengwei Huang, Cairong Zou, Wei Zhou, Weiping Ding, Björn W. Schuller
Hearing aids (HAs) are widely used to provide personalized speech enhancement (PSE) services, improving the quality of life for individuals with hearing loss. However, HA performance significantly declines in noisy environments as it treats noise reduction (NR) and hearing loss compensation (HLC) as separate tasks. This separation leads to a lack of systematic optimization, overlooking the interactions between these two critical tasks, and increases the system complexity. To address these challenges, we propose a novel audiogram fusion network, named AFN-HearNet, which simultaneously tackles the NR and HLC tasks by fusing cross-domain audiogram and spectrum features. We propose an audiogram-specific encoder that transforms the sparse audiogram profile into a deep representation, addressing the alignment problem of cross-domain features prior to fusion. To incorporate the interactions between NR and HLC tasks, we propose the affine modulation-based audiogram fusion frequency-temporal Conformer that adaptively fuses these two features into a unified deep representation for speech reconstruction. Furthermore, we introduce a voice activity detection auxiliary training task to embed speech and non-speech patterns into the unified deep representation implicitly. We conduct comprehensive experiments across multiple datasets to validate the effectiveness of each proposed module. The results indicate that the AFN-HearNet significantly outperforms state-of-the-art in-context fusion joint models regarding key metrics such as HASQI and PESQ, achieving a considerable trade-off between performance and efficiency. The source code and data will be released at this https URL.
Authors: Yurun Zhang, Wei Yao, Yutian Lan, Hang Shuai, Shanyang Wei, Wei Gan, Chao Duan, Jinyu Wen, Shijie Cheng
Frequency security is critical for power grids, as deviations can trigger widespread outages and result in substantial economic losses. However, modern renewable-dominated power grids face an increased risk of insecurity due to low inertia and nonlinear frequency responses. To mitigate these risks, robust pre-fault frequency security assessment (FSA) is critical, which enables grid operators to implement preventive control strategies. We propose a data-knowledge fusion framework to achieve intelligent FSA in actual power grids. First, we classify FSA domain knowledge into two distinct categories: (1) physics-guided knowledge directs the neural network pre-training process, ensuring that the fusion model's predictions consistent with frequency response mechanisms, and (2) physics-constrained knowledge establishes quantitative relationship on predictions, which forces them within theoretical ranges defined by domain knowledge. Furthermore, we develop a dual-channel neural network architecture to simultaneously capture both local and global characteristics of the power system. Finally, we introduce a data-knowledge fusion training algorithm that integrates guided learning with constrained network architecture to enhance model reliability and generalization. Case studies on China's Yunnan Provincial Power Grid validate the superior performance of our framework: it reduces average prediction error to 1.26% (a 49.2% reduction over data-driven methods), and maintains 97.60% accuracy in untrained scenarios (3.85% higher than data-driven methods), therefore satisfies the accuracy, reliability, and generalization requirements for actual power grids. The proposed methodology establishes a new paradigm for enhancing robustness of FSA in power grids, with potential application to cross-domain security assessment.
Authors: Armel Koulong, Ali Pakniyat
A distributed adaptive control strategy is developed for heterogeneous multiagent systems in nonlinear Brunovsky form with \({\pd}\)-dimensional $n^{\text{th}}$-order dynamics, operating under time-triggered switching communication topologies. The approach uses repulsive potential functions to ensure agent-agent and obstacle safety, while neural network estimators compensate for system uncertainties and disturbances. A high-order control barrier function framework is then employed to certify the positive invariance of the safe sets and the boundedness of the proposed control inputs. The resulting distributed control and adaptive laws, together with dwell-time requirements for topology transitions, achieve leader-following consensus. This integrated design provides synchronized formation and robust disturbance rejection in evolving network configurations, and its effectiveness is demonstrated through numerical simulations.
Authors: Yuezhu Xu, S. Sivaranjani, Vijay Gupta
We address the problem of learning a neural Koopman operator model that provides dissipativity guarantees for an unknown nonlinear dynamical system that is known to be dissipative. We propose a two-stage approach. First, we learn an unconstrained neural Koopman model that closely approximates the system dynamics. Then, we minimally perturb the parameters to enforce strict dissipativity. Crucially, we establish theoretical guarantees that extend the dissipativity properties of the learned model back to the original nonlinear system. We realize this by deriving an exact relationship between the dissipativity of the learned model and the true system through careful characterization of the identification errors from the noisy data, Koopman operator truncation, and generalization to unseen data. We demonstrate our approach through simulation on a Duffing oscillator model.
Authors: Miguel Saavedra-Melo, Benjamin Bradshaw, Vanessa Yao, Ender Ayanoglu, Lee Swindlehurst, Filippo Capolino
Reconfigurable intelligent surfaces (RISs), an emerging technology proposed for inclusion in next generation wireless communication systems, are programmable surfaces that can adaptively reflect incident electromagnetic radiation in different desired directions. To reduce the complexity and physical profile of conventional RIS designs, a novel concept known as Wave-Controlled RIS has been proposed, in which standing waves along a transmission line are used to generate the required dc bias for reflective control. This paper shows the design of such a Wave-Controlled RIS and its biasing transmission line. The effectiveness of this approach in generating the correct dc bias from a single standing wave frequency is analyzed through both theoretical modeling and experimental validation, which uncovered a dependence on impedance matching not accounted for by the theory. Additionally, the potential for reflective control using only a single standing wave frequency on the biasing transmission line is explored, demonstrating the ability of single-beam steering toward angles near broadside.
Authors: Navid Reyhanian, Reza Ghaderi Zefreh, Parisa Ramezani, Emil Björnson
The deployment of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems cannot rely solely on digital precoding due to hardware constraints. Instead, hybrid precoding, which combines digital and radio frequency (RF) techniques, has emerged as a potential alternative. This approach strikes a balance between performance and cost, addressing the limitations of signal mixers and analog-to-digital converters in mmWave systems. mmWave systems are designed to function in wideband channels with frequency selectivity, necessitating the use of orthogonal frequency-division multiplexing (OFDM) to mitigate dispersive channels. However, OFDM faces several challenges. First, it suffers from a high peak-to-average power ratio (PAPR) due to the linear combination of subcarriers. Second, it suffers from out-of-band (OOB) emissions due to the sharp spectral transitions of OFDM subcarriers and windowing-induced spectral leakage. Furthermore, phase shifter (PS) impairments at the RF transmitter precoder and the user combiner represent a limitation in practical mmWave systems, leading to phase errors. This work addresses these challenges. We study the problem of robust digital-RF precoding optimization for the downlink sum-rate maximization in hybrid multi-user (MU) MIMO-OFDM systems under maximum transmit power, PAPR, and OOB emission constraints. The formulated maximization problem is non-convex and difficult to solve. We propose a weighted minimum mean squared error (WMMSE) based block coordinate descent (BCD) method to iteratively optimize digital-RF precoders at the transmitter and digital-RF combiners at the users. Low-cost and scalable optimization approaches are proposed to efficiently solve the BCD subproblems. Extensive simulation results are conducted to demonstrate the efficiency of the proposed approaches and exhibit their superiority relative to well-known benchmarks.
Authors: Xin Chen, Xiaoyang Wang, Ana Colacelli, Matt Lee, Le Xie
The rapid growth of artificial intelligence (AI) is driving an unprecedented increase in the electricity demand of AI data centers, raising emerging challenges for electric power grids. Understanding the characteristics of AI data center loads and their interactions with the grid is therefore critical for ensuring both reliable power system operation and sustainable AI development. This paper provides a comprehensive review and vision of this evolving landscape. Specifically, this paper (i) presents an overview of AI data center infrastructure and its key components, (ii) examines the key characteristics and patterns of electricity demand across the stages of model preparation, training, fine-tuning, and inference, (iii) analyzes the critical challenges that AI data center loads pose to power systems across three interrelated timescales, including long-term planning and interconnection, short-term operation and electricity markets, and real-time dynamics and stability, and (iv) discusses potential solutions from the perspectives of the grid, AI data centers, and AI end-users to address these challenges. By synthesizing current knowledge and outlining future directions, this review aims to guide research and development in support of the joint advancement of AI data centers and power systems toward reliable, efficient, and sustainable operation.
Authors: Mehdi Davoudi, Junjie Qin, Xiaojun Lin
This study investigates market-driven long-term investment decisions in distributed solar panels by individual investors. We consider a setting where investment decisions are driven by expected revenue from participating in short-term electricity markets over the panel's lifespan. These revenues depend on short-term markets equilibria, i.e., prices and allocations, which are influenced by aggregate invested panel capacity participating in the markets. We model the interactions among investors by a non-atomic game and develop a framework that links short-term markets equilibria to the resulting long-term investment equilibrium. Then, within this framework, we analyze three market mechanisms: (a) a single-product real-time energy market, (b) a product-differentiated real-time energy market that treats solar energy and grid energy as different products, and (c) a contract-based panel market that trades claims or rights to the production of certain panel capacity ex-ante, rather than the realized solar production ex-post. For each, we derive expressions for short-term equilibria and the associated expected revenues, and analytically characterize the corresponding long-term Nash equilibrium aggregate capacity. We compare the solutions of these characterizing equations under different conditions and theoretically establish that the product-differentiated market always supports socially optimal investment, while the single-product market consistently results in under-investment. We also establish that the contract-based market leads to over-investment when the extra valuations of users for solar energy are small. Finally, we validate our theoretical findings through numerical experiments.
Authors: Timothy Everett Adams, James Richard Forbes
This paper proposes a linear input-output observer design methodology for a population of systems in which each observer uses knowledge of the linear time-invariant dynamics of the particular device. Observers are typically composed of a known model of the system and a correction mechanism to produce an estimate of the state. The proposed design procedure characterizes the variation within the population in the frequency domain and synthesizes a single robust correction filter. The correction filter is compatible with all system models that satisfy the variation characterization such that a given level of estimation performance is guaranteed. This is accomplished by posing a robust performance problem using the observer error dynamics and solving it using $DK$-iteration. The design procedure is experimentally demonstrated on a flexible joint robotic manipulator with varied joint stiffnesses. It is shown that the proposed method that uses a single correction filter achieves comparable estimation performance to a method that uses a correction gain tailored toward each joint stiffness configuration.
Authors: Mingyue Huo, Yuheng Zhang, Yan Tang
Modern end-to-end automatic speech recognition (ASR) models like Whisper not only suffer from reduced recognition accuracy in noise, but also exhibit overconfidence - assigning high confidence to wrong predictions. We conduct a systematic analysis of Whisper's behavior in additive noise conditions and find that overconfident errors increase dramatically at low signal-to-noise ratios, with 10-20% of tokens incorrectly predicted with confidence above 0.7. To mitigate this, we propose a lightweight, post-hoc calibration framework that detects potential overconfidence and applies temperature scaling selectively to those tokens, without altering the underlying ASR model. Evaluations on the R-SPIN dataset demonstrate that, in the low signal-to-noise ratio range (-18 to -5 dB), our method reduces the expected calibration error (ECE) by 58% and triples the normalized cross entropy (NCE), yielding more reliable confidence estimates under severe noise conditions.
Authors: Jonathan I. Mandel, Shivaprakash Hiremath, Hedyeh Keshtgar, Timothy Scholl, Sadegh Raeisi
This retrospective-prospective study evaluated whether a deep learning-based MRI reconstruction algorithm can preserve diagnostic quality in brain MRI scans accelerated up to fourfold, using both public and prospective clinical data. The study included 18 healthy volunteers (scans acquired at 3T, January 2024-March 2025), as well as selected fastMRI public datasets with diverse pathologies. Phase-encoding-undersampled 2D/3D T1, T2, and FLAIR sequences were reconstructed with DeepFoqus-Accelerate and compared with standard-of-care (SOC). Three board-certified neuroradiologists and two MRI technologists independently reviewed 36 paired SOC/AI reconstructions from both datasets using a 5-point Likert scale, while quantitative similarity was assessed for 408 scans and 1224 datasets using Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Haar wavelet-based Perceptual Similarity Index (HaarPSI). No AI-reconstructed scan scored below 3 (minimally acceptable), and 95% scored $\geq 4$. Mean SSIM was 0.95 $\pm$ 0.03 (90% cases >0.90), PSNR >41.0 dB, and HaarPSI >0.94. Inter-rater agreement was slight to moderate. Rare artifacts did not affect diagnostic interpretation. These findings demonstrate that DeepFoqus-Accelerate enables robust fourfold brain MRI acceleration with 75% reduced scan time, while preserving diagnostic image quality and supporting improved workflow efficiency.
Authors: Paula Isabel Tilleria Lucero (1), Bryan Fernando Sarango Rodríguez (1), Fernando Darío Almeida García (2), José Cândido Silveira Santos Filho (1) ((1) Wireless Technology Laboratory, Department of Communications, School of Electrical and Computer Engineering, State University of Campinas (UNICAMP), Campinas, SP 13083-852, Brazil, (2) Wireless and Artificial Intelligence (WAI) laboratory, National Institute of Telecommunications (INATEL), Santa Rita do Sapucaí, MG, 37540-000, Brazil)
This paper addresses three critical limitations in previous analyses of RIS-aided wireless systems: propagation environments with fixed diversity gain, restricted spatial correlation profiles, and approximation methods that fail to capture the system behavior in the high signal-to-noise ratio (SNR) regime. To overcome these challenges, we conduct an exact asymptotic analysis focused on the left tail of the SNR distribution, which plays a critical role in high-SNR system performance. Additionally, to account for general correlation profiles and fading environments with variable diversity and coding gains, we consider arbitrarily correlated Nakagami-m fading channels. The analytical results show that fading correlation induces a horizontal shift in the asymptotic behavior -- represented as a straight line in the log-dB scale -- of the PDF and CDF, displacing these curves to the left. The asymptotic linear coefficient quantifies this shift, while the angular coefficient remains unaffected. Moreover, the results reveal that the high sensitivity of the linear coefficient to correlation arises from the aggregated contribution of all marginal asymptotic terms, effectively capturing each channel's correlation characteristics.
Authors: Baris Donmez, Sebastien Loranger, Gunes Karabulut Kurt
This study investigates the RF-based Doppler shift distribution characterization of the Lunar South Pole (LSP) based inter-satellite link (ISL) in varying inclination. Doppler shift in parts per million (ppm) is determined and analyzed, as it provides an independence from the carrier frequency. Due to unknown relative velocity states duration, the Gaussian Mixture Model (GMM) is found to be the best fitting distribution for ISLs with $1^\circ$ inclination interval Doppler shift with respect to a predetermined satellite. Goodness-of-fit is investigated and quantified with Kullback-Leibler (KL) divergence and weighted mean relative difference (WMRD) error metrics. Simulation results show that ISL Doppler shifts reach up to $\pm1.89$ ppm as the inclination of the other orbit deviates higher from the reference orbit, inclining $80^\circ$. Regarding the error measurements of GMM fitting, the WMRD and KL divergence metrics for ISL take values up to 0.6575 and 2.2963, respectively.
Authors: Diego Fajardo-Rojas, Levente Baljer, Jordina Aviles Verdera, Megan Hall, Daniel Cromb, Mary A. Rutherford, Lisa Story, Emma C. Robinson, Jana Hutter
Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. Its performance was benchmarked against linear regression using cervical length measurements obtained by experienced clinicians from anatomical MRI and other Deep Learning architectures. The GA at birth predictions were assessed using mean absolute error. Accuracy, sensitivity, and specificity were used to assess preterm classification. Both the fully automated MRI-based pipeline and the cervical length regression achieved comparable mean absolute errors (3 weeks) and good sensitivity (0.67) for detecting preterm birth, despite pronounced class imbalance in the dataset. These results provide a proof of concept for automated prediction of GA at birth from functional MRI, and underscore the value of whole-uterus functional imaging in identifying at-risk pregnancies. Additionally, we demonstrate that manual, high-definition cervical length measurements derived from MRI, not currently routine in clinical practice, offer valuable predictive information. Future work will focus on expanding the cohort size and incorporating additional organ-specific imaging to improve generalisability and predictive performance.
Authors: Mu Nan, Taohui Xiao, Ruoyou Wu, Shoujun Yu, Ye Li, Hairong Zheng, Shanshan Wang
Diffusion MRI (dMRI) angular super-resolution (ASR) aims to reconstruct high-angular-resolution (HAR) signals from limited low-angular-resolution (LAR) data without prolonging scan time. However, existing methods are limited in recovering fine-grained angular details or preserving high fidelity due to inadequate modeling of q-space geometry and insufficient incorporation of physical constraints. In this paper, we introduce a Physics-Guided Diffusion Transformer (PGDiT) designed to explore physical priors throughout both training and inference stages. During training, a Q-space Geometry-Aware Module (QGAM) with b-vector modulation and random angular masking facilitates direction-aware representation learning, enabling the network to generate directionally consistent reconstructions with fine angular details from sparse and noisy data. In inference, a two-stage Spherical Harmonics-Guided Posterior Sampling (SHPS) enforces alignment with the acquired data, followed by heat-diffusion-based SH regularization to ensure physically plausible reconstructions. This coarse-to-fine refinement strategy mitigates oversmoothing and artifacts commonly observed in purely data-driven or generative models. Extensive experiments on general ASR tasks and two downstream applications, Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI), demonstrate that PGDiT outperforms existing deep learning models in detail recovery and data fidelity. Our approach presents a novel generative ASR framework that offers high-fidelity HAR dMRI reconstructions, with potential applications in neuroscience and clinical research.
Authors: Murat Temiz, Yongwei Zhang, Yanwei Fu, Chi Zhang, Chenfeng Meng, Orhan Kaplan, Christos Masouros
This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems. ISAC, which combines sensing and communication functionalities, is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities for effective operation. A unified platform that provides both functions can reduce hardware complexity, alleviate frequency spectrum congestion, and improve energy efficiency. However, integrating these functionalities on the same hardware requires highly optimized signal processing and system design, introducing significant computational complexity when relying on conventional iterative or optimization-based techniques. As an alternative to conventional techniques, DL-based techniques offer efficient and near-optimal solutions with reduced computational complexity. Hence, such techniques are well-suited for operating under limited computational resources and low latency requirements in real-time systems. DL-based techniques can swiftly and effectively yield near-optimal solutions for a wide range of sophisticated ISAC-related tasks, including waveform design, channel estimation, sensing signal processing, data demodulation, and interference mitigation. Therefore, motivated by these advantages, recent studies have proposed various DL-based approaches for ISAC system design. After briefly introducing DL architectures and ISAC fundamentals, this survey presents a comprehensive and categorized review of state-of-the-art DL-based techniques for ISAC, highlights their key advantages and major challenges, and outlines potential directions for future research.
Authors: Tianyu Huo, Jian Xiong, Yiyan Wu, Songjie Yang, Bo Liu, Wenjun Zhang
Extremely large antenna array (ELAA) is key to enhancing spectral efficiency in 6G networks. Leveraging the distributed nature of multi-unmanned aerial vehicle (UAV) systems enables the formation of distributed ELAA, which often operate in the near-field region with spatial sparsity, rendering the conventional far-field plane wave assumption invalid. This paper investigates channel estimation for distributed near-field multi-UAV communication systems. We first derive closed-form signal-to-noise ratio (SNR) expressions under the plane wave model (PWM), spherical wave model (SWM), and a hybrid spherical-plane wave model (HSPWM), also referred to as the cross-field model, within a distributed uniform planar array (UPA) scenario. The analysis shows that HSPWM achieves a good balance between modeling accuracy and analytical tractability. Based on this, we propose two channel estimation algorithms: the spherical-domain orthogonal matching pursuit (SD-OMP) and the tensor-OMP. The SD-OMP generalizes the polar domain to jointly consider elevation, azimuth, and range. Under the HSPWM, the channel is naturally formulated as a tensor, enabling the use of tensor-OMP. Simulation results demonstrate that tensor-OMP achieves normalized mean square error (NMSE) performance comparable to SD-OMP, while offering reduced computational complexity and improved scalability.
Authors: Neal G. Ravindra, Arijit Sehanobish
High-quality, medically validated labels exist for clinical actigraphy data but not for ubiquitous consumer wearables like the Apple Watch. Manually labeling wearables data is expensive and doesn't scale. This paper offers a novel framework that transfers valuable labels from a source domain (e.g., actigraphy) to a target domain (e.g., Apple Watch) without requiring paired data. Instead of working with raw time-series signals, we project both domains into a shared latent embedding space using time-series foundation models (TSFMs) and develop a new framework to align the cross-device representations. Our method, Adversarial Alignment of TSFM Embeddings forces the distributions of source and target embeddings to align within this space, facilitating label transfer across device type.
Authors: Simone Pirrera, Lorenzo Calogero, Francesco Gabriele, Diego Regruto, Alessandro Rizzo, Gianluca Setti
This paper proposes a novel approach to design analog electronic circuits that implement Model Predictive Control (MPC) policies for plants described by affine models. The combination of state-of-the-art approaches to define reduced-complexity Explicit MPC (EMPC) is employed to realize an analog circuit characterized by a limited amount of low-latency and commercially available components. The practical feasibility and effectiveness of the proposed approach are demonstrated through its application in the design of an advanced controller for DC-DC Buck converters. We formally analyze the stability of the obtained system and conduct extensive numerical simulations to demonstrate that it is capable of achieving outstanding load disturbance rejection performance, outclassing standard approaches.
Authors: Yijie Liu, Weidong Mei, He Sun, Dong Wang, Peilan Wang
Beyond diagonal reconfigurable intelligent surface (BD-RIS), with its enhanced degrees of freedom compared to conventional RIS, has demonstrated notable potential for enhancing wireless communication performance. However, a key challenge in employing BD-RIS lies in accurately acquiring its channel state information (CSI) with both the base station (BS) and users. Existing BD-RIS channel estimation methods rely mainly on dedicated pilot signals, which increase system overhead and may be incompatible with current communication protocols. To overcome these limitations, this letter proposes a new single-layer neural network (NN)-enabled channel estimation method utilizing only the easily accessible received power measurements at user terminals. In particular, we show that the received signal power can be expressed in a form similar to a single-layer NN, where the weights represent the BD-RIS's CSI. This structure enables the recovery of CSI using the backward propagation, based on power measurements collected under varying training reflection coefficients. Numerical results show that our proposed method can achieve a small normalized mean square error (NMSE), particularly when the number of training reflections is large.
Authors: Simone Pirrera, Nicolas Faedo, Sophie M. Fosson, Diego Regruto
This paper proposes a novel real-time algorithm for controlling wave energy converters (WECs). We begin with the economic model predictive control (MPC) problem formulation and apply a novel, first-order optimization algorithm inspired by recently developed control-based algorithms for constrained optimization to define the controller dynamics according to the single-iteration MPC approach. We theoretically analyse the convergence of the employed algorithm and the computational complexity of the obtained controller. Results from simulations using a benchmark WEC system indicate that the proposed approach significantly outperforms standard MPC, thanks to the inherent ability to handle faster sampling rates.
Authors: Mohammad Rasoul Narimani, Katherine R. Davis, Daniel K. Molzahn
By providing the optimal operating point that satisfies both the power flow equations and engineering limits, the optimal power flow (OPF) problem is central to the operation of electric power systems. While extensive research efforts have focused on reliably computing high-quality OPF solutions, assessing the feasibility of transitioning between operating points remains challenging since the feasible spaces of OPF problems may consist of multiple disconnected components. It is not possible to transition between operating points in different disconnected components without violating OPF constraints. To identify such situations, this paper introduces an algorithm for certifying the infeasibility of transitioning between two operating points within an OPF feasible space. As an indication of potential disconnectedness, the algorithm first seeks an infeasible point on the line connecting a pair of feasible points. The algorithm then certifies disconnectedness by using convex relaxation and bound tightening techniques to show that all points on the plane that is normal to this line are infeasible. Using this algorithm, we provide the first certifications of disconnected feasible spaces for a variety of OPF test cases.
Authors: Tahmin Mahmud, Euzeli Cipriano Dos Santos Jr
Component ageing is a critical concern in power electronic converter systems (PECSs). It directly impacts the reliability, performance, and operational lifespan of converters used across diverse applications, including electric vehicles (EVs), renewable energy systems (RESs) and industrial automation. Therefore, understanding and monitoring component ageing is crucial for developing robust converters and achieving long-term system reliability. This paper proposes a data-driven digital twin (DT) framework for DC-DC buck converters, integrating deep neural network (DNN) with the spider monkey optimization (SMO) algorithm to monitor and predict component degradation. Utilizing a low-power prototype testbed along with empirical and synthetic datasets, the SMO+DNN approach achieves the global optimum in 95% of trials, requires 33% fewer iterations, and results in 80% fewer parameter constraint violations compared to traditional methods. The DNN model achieves $R^2$ scores above 0.998 for all key degradation parameters and accurately forecasts time to failure ($t_{failure}$). In addition, SMO-tuned degradation profile improves the converter's performance by reducing voltage ripple by 20-25% and inductor current ripple by 15-20%.
Authors: Marwan Mostafa, Daniel Wenser, Payam Teimourzadeh Baboli, Christian Becker
The increasing complexity of energy systems due to sector coupling and decarbonization calls for unified modeling frameworks that capture the physical and structural interactions between electricity, gas, and heat networks. This paper presents a graph-based modeling approach for multi-energy systems, where each domain is represented as a layer in a multi-layer graph, and coupling technologies are modeled as inter-layer edges via a dedicated coupling layer. A steady-state solver based on a block-structured Newton-Raphson method is developed to jointly compute flows and state variables across all carriers. The proposed model is tested and validated on a realistic case study based on data from a German distribution network. The results demonstrate convergence, numerical accuracy, and consistent domain interaction, and demonstrate the method's applicability for system-wide analysis and its potential as a foundation for future optimizations in integrated energy systems.
Authors: Mohammadreza Doostmohammadian, Hamid R. Rabiee
This paper considers automatic generation control over an information-sharing network of communicating generators as a multi-agent system. The optimization solution is distributed among the agents based on information consensus algorithms, while addressing the generators' ramp-rate-limits (RRL). This is typically ignored in the existing linear/nonlinear optimization solutions but they exist in real-time power generation scenarios. Without addressing the RRL, the generators cannot follow the assigned rate of generating power by the optimization algorithm; therefore, the existing solutions may not necessarily converge to the exact optimal cost or may lose feasibility in practice. The proposed solution in this work addresses the ramp-rate-limit constraint along with the box constraint (limits on the generated powers) and the coupling-constraint (generation-demand balance) at all iteration times of the algorithm. The latter is referred to as the anytime feasibility and implies that at every termination point of the algorithm, the balance between the demand and generated power holds. To improve the convergence rate of the algorithm we further consider internal signum-based nonlinearity. We also show that our solution can tolerate communication link removal. This follows from the uniform-connectivity assumption on the communication network.
Authors: Ao Huang, Xidong Mu, Li Guo, Guangyu Zhu
This paper investigates the problem of resource efficiency maximization in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted communication system under practical transceiver hardware impairments (HWIs). We aim to obtain an optimal tradeoff between system spectral efficiency (SE) and energy efficiency (EE), by jointly optimizing the base station (BS) transmit beamforming and the active STAR-RIS beamforming. To tackle the challenges in the fractional objective function, we begin by applying the quadratic transformation method to simplify it into a manageable form. An alternating optimization-based algorithm is then developed to iteratively update the BS and STAR-RIS beamforming coefficients. Simulation results demonstrate that the proposed scheme performs better than other baseline schemes in the presence of HWIs. Moreover, the variation of the achievable SE-EE region with different transmit power budgets is analyzed.
Authors: Yu-Hsiang Huang, Po-Heng Chou, Wan-Jen Huang, Walid Saad, C.-C. Jay Kuo
In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy- and hardware-constrained broadcasting scenarios.
Authors: Yanis Labrak, Richard Dufour, Mickaël Rouvier
This paper investigates discrete unit representations in Speech Language Models (SLMs), focusing on optimizing speech modeling during continual pre-training. In this paper, we systematically examine how model architecture, data representation, and training robustness influence the pre-training stage in which we adapt existing pre-trained language models to the speech modality. Our experiments highlight the role of speech encoders and clustering granularity across different model scales, showing how optimal discretization strategies vary with model capacity. By examining cluster distribution and phonemic alignments, we investigate the effective use of discrete vocabulary, uncovering both linguistic and paralinguistic patterns. Additionally, we explore the impact of clustering data selection on model robustness, highlighting the importance of domain matching between discretization training and target applications.
Authors: Jorge E. León, Miguel Carrasco
The increasing use of machine learning models has amplified the demand for high-quality, large-scale multimodal datasets. However, the availability of such datasets, especially those combining acoustic, visual and textual data, remains limited. This paper addresses this gap by proposing a method to extract related audio-image-text observations from videos. We detail the process of selecting suitable videos, extracting relevant data pairs, and generating descriptive texts using image-to-text models. Our approach ensures a robust semantic connection between modalities, enhancing the utility of the created datasets for various applications. We also discuss the challenges encountered and propose solutions to improve data quality. The resulting datasets, publicly available, aim to support and advance research in multimodal data analysis and machine learning.
Authors: Vedant Karia, Abdullah Zyarah, Dhireesha Kudithipudi
Continual learning, the ability to acquire and transfer knowledge through a models lifetime, is critical for artificial agents that interact in real-world environments. Biological brains inherently demonstrate these capabilities while operating within limited energy and resource budgets. Achieving continual learning capability in artificial systems considerably increases memory and computational demands, and even more so when deploying on platforms with limited resources. In this work, Genesis, a spiking continual learning accelerator, is proposed to address this gap. The architecture supports neurally inspired mechanisms, such as activity-dependent metaplasticity, to alleviate catastrophic forgetting. It integrates low-precision continual learning parametersand employs a custom data movement strategy to accommodate the sparsely distributed spikes. Furthermore, the architecture features a memory mapping technique that places metaplasticity parameters and synaptic weights in a single address location for faster memory access. Results show that the mean classification accuracy for Genesis is 74.6% on a task-agnostic split-MNIST benchmark with power consumption of 17.08mW in a 65nm technology node.
Authors: Yue Gu, Zhihao Du, Ying Shi, Shiliang Zhang, Qian Chen, Jiqing Han
Recently, cross-attention-based contextual automatic speech recognition (ASR) models have made notable advancements in recognizing personalized biasing phrases. However, the effectiveness of cross-attention is affected by variations in biasing information volume, especially when the length of the biasing list increases significantly. We find that, regardless of the length of the biasing list, only a limited amount of biasing information is most relevant to a specific ASR intermediate representation. Therefore, by identifying and integrating the most relevant biasing information rather than the entire biasing list, we can alleviate the effects of variations in biasing information volume for contextual ASR. To this end, we propose a purified semantic correlation joint modeling (PSC-Joint) approach. In PSC-Joint, we define and calculate three semantic correlations between the ASR intermediate representations and biasing information from coarse to fine: list-level, phrase-level, and token-level. Then, the three correlations are jointly modeled to produce their intersection, so that the most relevant biasing information across various granularities is highlighted and integrated for contextual recognition. In addition, to reduce the computational cost introduced by the joint modeling of three semantic correlations, we also propose a purification mechanism based on a grouped-and-competitive strategy to filter out irrelevant biasing phrases. Compared with baselines, our PSC-Joint approach achieves average relative F1 score improvements of up to 21.34% on AISHELL-1 and 28.46% on KeSpeech, across biasing lists of varying lengths.
Authors: Simran Kumari, Ashish R. Hota, Siddhartha Mukhopadhyay
In this paper, we simultaneously address the problems of energy optimal and safe motion planning of electric vehicles (EVs) in a data-driven robust optimization framework. Safe maneuvers, especially in urban traffic, are characterized by frequent lateral motions, such as lane changes, overtakes and turning along curved roads. Motivated by our previous work which shows a 3-10 % increase in energy consumption due to lateral motion when an electric vehicle changes its lane once every kilometer while following standard drive cycles, we incorporate vehicle lateral dynamics in the modeling and control synthesis, which is in contrast with most prior works. In the context of safety, we leverage past data of obstacle motion to construct a future occupancy set with probabilistic guarantees, and formulate robust collision avoidance constraints with respect to such an occupancy set using convex programming duality. Consequently, we formulate a finite-horizon optimal control problem subject to robust collision avoidance constraints while penalizing resulting energy consumption, and solve it in a receding horizon fashion. Finally, we show the effectiveness of the proposed approach in reducing energy consumption and collision avoidance via numerical simulations involving curved roads and multiple obstacles. A detailed analysis of energy consumption along different components of EV motion highlights appreciable improvement under the proposed approach.
Authors: Yuqing Shen, Yuanyuan Shi, Daniel Kirschen, Yize Chen
Power systems decarbonization are at the focal point of the clean energy transition. While system operators and utility companies increasingly publicize system-level carbon emission information, it remains unclear how emissions from individual generators are transported through the grid and how they impact electricity users at specific locations. This paper presents a novel and computationally efficient approach for exact quantification of nodal average and marginal carbon emission rates, applicable to both AC and DC optimal power flow problems. The approach leverages graph-based topological sorting and directed cycle removal techniques, applied to directed graphs formed by generation dispatch and optimal power flow solutions. Our proposed algorithm efficiently identifies each generator's contribution to each node, capturing how emissions are spatially distributed under varying system conditions. To validate its effectiveness and reveal locational and temporal emission patterns in the real world, we simulate the 8,870-bus realistic California grid using actual CAISO data and the CATS model. Based on year long hourly data on nodal loads and renewable generation, obtained or estimated from CAISO public data, our method accurately estimates power flow conditions, generation mixes, and systemwide emissions, and delivers fine grained spatiotemporal emission analysis for every California county. Both our algorithm and the California study are open-sourced, providing a foundation for future research on grid emissions, planning, operations, and energy policy.
Authors: Fenghao Zhu, Xinquan Wang, Siming Jiang, Xinyi Li, Maojun Zhang, Yixuan Chen, Chongwen Huang, Zhaohui Yang, Xiaoming Chen, Zhaoyang Zhang, Richeng Jin, Yongming Huang, Wei Feng, Tingting Yang, Baoming Bai, Feifei Gao, Kun Yang, Yuanwei Liu, Sami Muhaidat, Chau Yuen, Kaibin Huang, Kai-Kit Wong, Dusit Niyato, Ying-Chang Liang, Mérouane Debbah
The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions.
Authors: Hai Wang, Baoshen Guo, Xiaolei Zhou, Shuai Wang, Zhiqing Hong, Tian He
Driven by growing concerns over air quality and energy security, electric vehicles (EVs) has experienced rapid development and are reshaping global transportation systems and lifestyle patterns. Compared to traditional gasoline-powered vehicles, EVs offer significant advantages in terms of lower energy consumption, reduced emissions, and decreased operating costs. However, there are still some core challenges to be addressed: (i) Charging station congestion and operational inefficiencies during peak hours, (ii) High charging cost under dynamic electricity pricing schemes, and (iii) Conflicts between charging needs and passenger service this http URL, in this paper, we present a comprehensive review of data-driven models and approaches proposed in the literature to address the above challenges. These studies cover the entire lifecycle of EV systems, including charging station deployment, charging scheduling strategies, and large-scale fleet management. Moreover, we discuss the broader implications of EV integration across multiple domains, such as human mobility, smart grid infrastructure, and environmental sustainability, and identify key opportunities and directions for future research.
Authors: Jianhua Zhang, Jiaxin Lin, Pan Tang, Yuxiang Zhang, Huixin Xu, Tianyang Gao, Haiyang Miao, Huiwen Gong, Changsheng Zhao, Yameng Liu, Yichen Cai, Zhiqiang Yuan, Lei Tian, Shaoshi Yang, Liang Xia, Guangyi Liu, Ping Zhang
Sixth-generation (6G) mobile communications have attracted substantial attention in the global research community of information and communication technologies (ICTs). 6G systems are expected to support not only extended 5G usage scenarios but also new usage scenarios, such as integrated sensing and communication (ISAC), integrated artificial intelligence (AI) and communication, and communication and ubiquitous connectivity. To achieve this goal, channel characteristics must be comprehensively studied and properly exploited to promote the design, standardization, and optimization of 6G systems. In this paper, we first summarize the requirements and challenges in 6G channel research. Our focus is on channels for six promising technologies enabling 6G, including ISAC, extremely large-scale MIMO (XL-MIMO), mid-band and terahertz (THz) technologies, reconfigurable intelligent surfaces (RISs), and space-air-ground integrated networks (SAGINs). A survey of the progress in 6G channel research regarding the above six promising technologies is presented in terms of the latest measurement campaigns, new characteristics, modeling methods, and research prospects. To support testing, optimization and evaluation, existing 6G channel simulators are summarized. Then, BUPTCMCCCMG-IMT2030 is introduced as an example of a simulator that was developed on the basis of the ITU/3GPP 3D geometry-based stochastic model (GBSM) methodology. We also address open issues covering standardization activities, AI-enabled methods, and system performance analysis in the context of 6G channel research. This paper offers in-depth, hands-on insights into the best practices of channel measurements, modeling, and simulations for the evaluation of 6G technologies, the development of 6G standards, and the implementation and optimization of 6G systems.
Authors: Hyeonsu Lyu, Hyeonho Noh, Hyun Jong Yang, Kaushik Chowdhury
As a key enabler of borderless and ubiquitous connectivity, space-air-ground-sea integrated networks (SAGSINs) are expected to be a cornerstone of 6G wireless communications. However, the multi-tiered and global-scale nature of SAGSINs also amplifies the security vulnerabilities, particularly due to the hidden, passive eavesdroppers distributed throughout the network. In this paper, we introduce a joint optimization framework for multi-hop relaying in SAGSINs that maximizes the minimum user throughput while ensuring a minimum strictly positive secure connection (SPSC) probability. We first derive a closed-form expression for the SPSC probability and incorporate this into a cross-layer optimization framework that jointly optimizes radio resources and relay routes. Specifically, we propose an $\mathcal{O}(1)$ optimal frequency allocation and power splitting strategy-dividing power levels of data transmission and cooperative jamming. We then introduce a Monte-Carlo relay routing algorithm that closely approaches the performance of the numerical upper-bound method. We validate our framework on testbeds built with real-world dataset.
Authors: Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sastry Kompella, Gustave Anderson, Jonathan Ashdown
This paper presents a complete signal-processing chain for multistatic integrated sensing and communications (ISAC) using 5G Positioning Reference Signal (PRS). We consider a distributed architecture in which one gNB transmits a periodic OFDM-PRS waveform while multiple spatially separated receivers exploit the same signal for target detection, parameter estimation and tracking. A coherent cross-ambiguity function (CAF) is evaluated to form a range-Doppler map from which the bistatic delay and radial velocity are extracted for every target. For a single target, bistatic delays are fused through nonlinear least-squares trilateration, yielding a geometric position estimate, and a regularized linear inversion of the radial-speed equations yields a two-dimensional velocity vector, where speed and heading are obtained. The approach is applied to 2D and 3D settings, extended to account for receiver clock synchronization bias, and generalized to multiple targets by resolving target association. The sequence of position-velocity estimates is then fed to standard and extended Kalman filters to obtain smoothed tracks. Our results show high-fidelity moving-target detection, positioning, and tracking using 5G PRS signals for multistatic ISAC.
Authors: Jehad Jilan, Niranjana Naveen Nambiar, Ahmad Mohammad Saber, Alok Paranjape, Amr Youssef, Deepa Kundur
Automatic Generation Control (AGC) is essential for power grid stability but remains vulnerable to stealthy cyberattacks, such as False Data Injection Attacks (FDIAs), which can disturb the system's stability while evading traditional detection methods. Unlike previous works that relied on blackbox approaches, this work proposes Kolmogorov-Arnold Networks (KAN) as an interpretable and accurate method for FDIA detection in AGC systems, considering the system nonlinearities. KAN models include a method for extracting symbolic equations, and are thus able to provide more interpretability than the majority of machine learning models. The proposed KAN is trained offline to learn the complex nonlinear relationships between the AGC measurements under different operating scenarios. After training, symbolic formulas that describe the trained model's behavior can be extracted and leveraged, greatly enhancing interpretability. Our findings confirm that the proposed KAN model achieves FDIA detection rates of up to 95.97% and 95.9% for the initial model and the symbolic formula, respectively, with a low false alarm rate, offering a reliable approach to enhancing AGC cybersecurity.
Authors: Muratkhan Abdirash, Xiaofan Cui
The increasing penetration of distributed energy resources and power-electronics interfaces in DC microgrids, coupled with rising cyber threats, necessitates primary controllers that are provably safe, cyber-resilient, and practical. The increasing penetration of distributed energy resources and power-electronics interfaces in DC microgrids, coupled with rising cyber threats, necessitates primary controllers that are provably safe, cyber-resilient, and practical. Conventional droop-based methods remain prevalent due to their simplicity, yet their design is largely empirical and conservative, lacking rigorous guarantees. Advanced strategies improve certain aspects, but often sacrifice scalability, robustness, or formal safety. In this work, we propose a Distributed Safety-Critical Controller (DSCC) that systematically integrates global stabilization with formal safety guarantees in a fully decentralized manner. Leveraging control barrier functions and the port-Hamiltonian system theory, the DSCC achieves scalable safe stabilization while preserving real-time implementability. High-fidelity switched-circuit simulations validate the controller's advantages under various contingencies. This framework paves the way for resilient, safety-critical, and scalable control in next-generation DC microgrids.
Authors: Farshad Amani, Faezeh Ardali, Amin Kargarian
Post-disaster crew dispatch is a critical but computationally intensive task. Traditional mixed-integer linear programming methods often require minutes to several hours to compute solutions, leading to delays that hinder timely decision-making in highly dynamic restoration environments. To address this challenge, we propose a novel learning-based framework that integrates transformer architectures with deep reinforcement learning (DRL) to deliver near real-time decision support without compromising solution quality. Crew dispatch is formulated as a sequential decision-making problem under uncertainty, where transformers capture high-dimensional system states and temporal dependencies, while DRL enables adaptive and scalable decision-making. Earthquake-induced distribution network damage is first characterized using established seismic standards, followed by a scenario generation and reduction pipeline that aggregates probable outcomes into a single geospatial impact map. Conditioned on this map, the proposed framework generates second-level dispatch strategies, trained offline on simulated and historical events and deployed online for rapid response. In addition to substantial runtime improvements, the proposed method enhances system resilience by enabling faster and more effective recovery and restoration. Case studies, particularly on the 2869-bus European gas and power network, demonstrate that the method substantially accelerates restoration while maintaining high-quality solutions, underscoring its potential for practical deployment in large-scale disaster response.
Authors: Regulo E. Avila-Martinez, Xavier Guillaud, Javier Renedo, Luis Rouco, Aurelio Garcia-Cerrada, Lukas Sigrist
Grid-forming voltage source converters (GFM-VSCs) are emerging as a solution for integrating renewable energy resources (RERs) into power systems. GFM-VSCs need a self-synchronisation strategy to ensure that all converters and generators in the power system are in synchronism and they reach the same frequency in steady state. The self-synchronisation strategy in GFM-VSCs that has received most attention in previous research is virtual synchronous machine (VSM) control. However, no systematic study of the effects on transient stability of different variants of this strategy has been carried out in previous work. This paper analyses and compares transient stability of four self-synchronisation strategies for GFM-VSCs: VSM without phase-locked loop (PLL), VSM with PLL, VSM without PLL using wash-out filter and integral-proportional (IP) controller. The paper also analyses two different methods that can \color{black} be applied to GFM-VSC self-synchronisation strategies to improve transient stability: the concept of virtual unsaturated active-power controller (VAPC), proposed in previous work, and an algorithm for frequency limitation in the GFM-VSC (FLC), which is proposed in this paper.
Authors: Andrea Zanelli, Dirk Schmidt, Matthias Resch, Marco Giovanelli, Martin Geidl, Walter Sattinger
On 21 June 2024, a severe incident happened in the South-Eastern part of the Continental European power system. After a voltage collapse, large parts of Albania, Montenegro, Bosnia and Herzegovina as well as Croatia suffered from a blackout [1]. The initial tripping of two transmission lines resulted in a voltage collapse in these countries. Investigations have shown that a) transformers with on-load tap changers (OLTC) and b) nonlinear loads, in particular air conditioning systems, played a significant role in this event. Motivated by this, we carry out an assessment of the effect of OLTC on voltage stability in the presence of nonlinear loads. By doing this we hope to further shed some light on the potential instability mechanisms that can be triggered in scenarios like the above-mentioned blackout.
Authors: Sagnik Bhattacharya, Abhiram Rao Gorle, John M. Cioffi
6G envisions massive cell-free networks with spatially nested multiple access (MAC) and broadcast (BC) channels without centralized coordination. This makes optimal resource allocation across power, subcarriers, and decoding orders crucial for interference channels (ICs), where neither transmitters nor receivers can cooperate. Current orthogonal multiple access (OMA) methods, as well as non-orthogonal (NOMA) and rate-splitting (RSMA) schemes, rely on fixed heuristics for interference management, leading to suboptimal rates, power inefficiency, and scalability issues. This paper proposes a novel minPIC framework for optimal power, subcarrier, and decoding order allocation in general multi-user ICs. Unlike existing methods, minPIC eliminates heuristic SIC order assumptions. Despite the convexity of the IC capacity region, fixing an SIC order induces non-convexity in resource allocation, traditionally requiring heuristic approximations. We instead introduce a dual-variable-guided sorting criterion to identify globally optimal SIC orders, followed by convex optimization with auxiliary log-det constraints, efficiently solved via binary search. We also demonstrate that minPIC could potentially meet the stringent high-rate, low-power targets of immersive XR and other 6G applications. To the best of our knowledge, minPIC is the first algorithmic realisation of the Pareto boundary of the SIC-achievable rate region for Gaussian ICs, opening the door to scalable interference management in cell-free networks.
Authors: B.G. Odunlami, M. Netto, Y. Susuki
The increasing integration of renewable energy sources has introduced complex dynamic behavior in power systems that challenge the adequacy of traditional continuous-time modeling approaches. These developments call for modeling frameworks that can capture the intricate interplay between continuous dynamics and discrete events characterizing modern grid operations. Hybrid dynamical systems offer a rigorous foundation for representing such mixed dynamics and have emerged as a valuable tool in power system analysis. Despite their potential, existing studies remain focused on isolated applications or case-specific implementations, offering limited generalizability and guidance for model selection. This paper addresses that gap by providing a comprehensive overview of hybrid modeling approaches relevant to power systems. It critically examines key formalisms, including hybrid automata, switched systems, and piecewise affine models, evaluating their respective strengths, limitations, and suitability across control, stability, and system design tasks. In doing so, the paper identifies open challenges and outlines future research directions to support the systematic application of hybrid methods in renewable-rich, converter-dominated power systems
Authors: Xin Li, Li Ding, Qiao Lin, Zhen-Wei Yu
Demand response providers (DRPs) are intermediaries between the upper-level distribution system operator and the lower-level participants in demand response (DR) programs. Usually, DRPs act as leaders and determine electricity pricing strategies to maximize their economic revenue, while end-users adjust their power consumption following the pricing signals. However, this profit-seeking bi-level optimization model often neglects the satisfaction of end-users participating in DR programs. In addition, the detailed mathematical models underlying user decision-making strategy and satisfaction evaluation mechanism are typically unavailable to DRPs, posing significant challenges to conventional model-based solution methods. To address these issues, this paper designs a user-side satisfaction evaluation mechanism and proposes a multi-branch temporal fusion twin-delayed deep deterministic policy gradient (MBTF-TD3) reinforcement learning algorithm. User satisfaction feedback is incorporated into the reward function via a dynamically adjusted penalty term. The proposed MBTF structure effectively extracts temporal feature dependencies in the time-series observation data, and the dynamically adjusted penalty function successfully enhances the overall satisfaction level of users. Several experiments are conducted to validate the performance and the effectiveness of our proposed solution algorithm.
Authors: Christian Doh Dinga, Sander van Rijn, Laurens de Vries, Milos Cvetkovic
Coordinating the interactions between flexibility assets in multi-carrier integrated energy systems (MIES) can lead to an efficient integration of variable renewable energy resources, and a cost-efficient energy transition. However, the proliferation of flexibility assets and their participation in active demand response increases the complexity of coordinating these interactions. This paper introduces different approaches to model the coordination of flexibility scheduling in MIES. We propose a market auction-inspired model coupling approach to address the challenges of preserving the autonomy and privacy of flexibility providers, and the issue of scalability. We benchmark our approach against co-optimization and an iterative price-response method by conducting experiments with varying problem sizes and computing infrastructure. We show that our approach scales well and is suitable for modeling flexibility in large-scale energy systems in a more realistic way. From an optimality standpoint, the flexibility dispatch schedules and electricity prices are ``near-optimal". Our methodology is implemented as a new open-source software, which offers several practical applications. For example, flexibility providers and network operators can couple their models to simulate the interaction between their systems without disclosing confidential information; policy regulators can use it to investigate new market design and regulations to optimize the utilization of flexibility in MIES.
Authors: Eli Brock, Jingqi Li, Javad Lavaei, Somayeh Sojoudi
We propose a real-time nodal pricing mechanism for cost minimization and voltage control in a distribution network with autonomous distributed energy resources and analyze the resulting market using stochastic game theory. Unlike existing methods, the proposed pricing scheme does not require device-aware centralized coordination or communication between prosumers. By developing new sufficient conditions under which a stochastic game is a Markov potential game, we show that the problem of computing an equilibrium for the proposed model is equivalent to solving a single-agent Markov Decision Process. These new conditions are general and may apply to other applications. We compute the equilibrium for an IEEE test system to empirically demonstrate the effectiveness of the pricing policy.
Authors: Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
Power allocation is an important task in wireless communication networks. Classical optimization algorithms and deep learning methods, while effective in small and static scenarios, become either computationally demanding or unsuitable for large and dynamic networks with varying user loads. This letter explores the potential of transformer-based deep learning models to address these challenges. We propose a transformer neural network to jointly predict optimal uplink and downlink power using only user and access point positions. The max-min fairness problem in cell-free massive multiple input multiple output systems is considered. Numerical results show that the trained model provides near-optimal performance and adapts to varying numbers of users and access points without retraining, additional processing, or updating its neural network architecture. This demonstrates the effectiveness of the proposed model in achieving robust and flexible power allocation for dynamic networks.
Authors: Thinh Viet Le, Md Obaidur Rahman, Vassilis Kekatos
Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy transition. To expedite such studies, this work leverages recent advances in quantum computing to find or predict AC PF solutions using a variational quantum circuit (VQC). VQCs are trainable models that run on modern-day noisy intermediate-scale quantum (NISQ) hardware to accomplish elaborate optimization and machine learning (ML) tasks. Our first contribution is to pose a single instance of the AC PF as a nonlinear least-squares fit over the VQC trainable parameters (weights) and solve it using a hybrid classical/quantum computing approach. The second contribution is to feed PF specifications as features into a data-embedded VQC and train the resultant quantum ML (QML) model to predict general PF solutions. The third contribution is to develop a novel protocol to efficiently measure AC-PF quantum observables by exploiting the graph structure of a power network. Preliminary numerical tests indicate that the proposed VQC models attain enhanced prediction performance over a deep neural network despite using much fewer weights. The proposed quantum AC-PF framework sets the foundations for addressing more elaborate grid tasks via quantum computing.
Authors: Eli Brock, Jingqi Li, Javad Lavaei, Somayeh Sojoudi
We propose a real-time nodal pricing mechanism for cost minimization and voltage control in a distribution network with autonomous distributed energy resources and analyze the resulting market using stochastic game theory. Unlike existing methods, the proposed pricing scheme does not require device-aware centralized coordination or communication between prosumers. By developing new sufficient conditions under which a stochastic game is a Markov potential game, we show that the problem of computing an equilibrium for the proposed model is equivalent to solving a single-agent Markov Decision Process. These new conditions are general and may apply to other applications. We compute the equilibrium for an IEEE test system to empirically demonstrate the effectiveness of the pricing policy.
Authors: Djamila Rekioua (LTII), Saloua Belaid (LTII), Pierre-Olivier Logerais (CERTES EA 3481), Toufik Rekioua (LTII), Zahra Mokrani (LTII), Khoudir Kakouche (LTII), Adel Oubelaid (LTII), Faika Zaouche (LTII)
Our paper is focused on optimal and control of an isolated photovoltaic system with batteries. The control is made by the application of a power management control (PMC). Batteries are kept safe from deep discharges and overloads by the PMC, maintaining a continuous supply to the load. The ease, with which this method can be implemented, as well as its effectiveness without imposing a large computing strain on the user, is noteworthy. The batteries and PV panels in the system under study are connected to a bidirectional converter enabling the batteries to be charged and drained in accordance with weather conditions. The simulation results, clearly highlight good performance of the proposed control across two different profiles.
Authors: Riya Kinnarkar, Mansur Arief
Traditional power grid infrastructure presents significant barriers to renewable energy integration and perpetuates energy access inequities, with low-income communities experiencing disproportionately longer power outages. This study develops a Markov Decision Process (MDP) framework to optimize renewable energy allocation while explicitly addressing social equity concerns in electricity distribution. The model incorporates budget constraints, energy demand variability, and social vulnerability indicators across eight major U.S. cities to evaluate policy alternatives for equitable clean energy transitions. Numerical experiments compare the MDP-based approach against baseline policies including random allocation, greedy renewable expansion, and expert heuristics. Results demonstrate that equity-focused optimization can achieve 32.9% renewable energy penetration while reducing underserved low-income populations by 55% compared to conventional approaches. The expert policy achieved the highest reward, while the Monte Carlo Tree Search baseline provided competitive performance with significantly lower budget utilization, demonstrating that fair distribution of clean energy resources is achievable without sacrificing overall system performance and providing ways for integrating social equity considerations with climate goals and inclusive access to clean power infrastructure.
Authors: Merlinda Andoni, Benoit Couraud, Valentin Robu, Jamie Blanche, Sonam Norbu, Si Chen, Satria Putra Kanugrahan, David Flynn
Amid global interest in resilient energy systems, green hydrogen is considered vital to the net-zero transition, yet its deployment remains limited by high production cost. The cost is determined by the its production pathway, system configuration, asset location, and interplay with electricity markets and regulatory frameworks. To compare different deployment strategies in the UK, we develop a comprehensive techno-economic framework based on the Levelised Cost of Hydrogen (LCOH) assessment. We apply this framework to 5 configurations of wind-electrolyser systems, identify the most cost-effective business cases, and conduct a sensitivity analysis of key economic parameters. Our results reveal that electricity cost is the dominant contributor to LCOH, followed by the electrolyser cost. Our work highlights the crucial role that location, market arrangements and control strategies among RES and hydrogen investors play in the economic feasibility of deploying green hydrogen systems. Policies that subsidise low-cost electricity access and optimise deployment can lower LCOH, enhancing the economic competitiveness of green hydrogen.
Authors: Pan-Yang Su, Yi Ju, Scott Moura, Shankar Sastry
We propose a general two-period model where electrical vehicles (EVs) can reserve charging sessions in the day-ahead market and swap them in the real-time market. Under the model, we explore several candidate mechanisms for running the two markets, compared using several normative properties such as incentive compatibility, efficiency, reservation awareness, and budget balance. Specifically, reservation awareness is the only property coupling the two markets and dictates that an EV will not get a lower utility by joining the real-time market. Focusing on the real-time market, we show that two variants of the classical Vickrey-Clarke-Groves (VCG) mechanism do not satisfy all the proposed properties; specifically, one is not reservation-aware, while the other is not budget-balanced. Moreover, we show that no mechanism satisfies some combinations of the properties. Then, we propose to use a posted-price mechanism to resolve the issue, which turns out to be the dynamic pricing mechanism adopted in many real-world systems. The proposed mechanism has no efficiency guarantee but satisfies all the other properties. To improve efficiency, we propose to use a VCG auction in the day-ahead market that guides the reserve prices in the real-time market. When EVs' valuations in the two markets are highly correlated, the proposed approach results in highly efficient outcomes.
Authors: Thinh Viet Le, Mark M. Wilde, Vassilis Kekatos
The optimal power flow (OPF) is a large-scale optimization problem that is central in the operation of electric power systems. Although it can be posed as a nonconvex quadratically constrained quadratic program, the complexity of modern-day power grids raises scalability and optimality challenges. In this context, this work proposes a variational quantum paradigm for solving the OPF. We encode primal variables through the state of a parameterized quantum circuit (PQC), and dual variables through the probability mass function associated with a second PQC. The Lagrangian function can thus be expressed as scaled expectations of quantum observables. An OPF solution can be found by minimizing/maximizing the Lagrangian over the parameters of the first/second PQC. We pursue saddle points of the Lagrangian in a hybrid fashion. Gradients of the Lagrangian are estimated using the two PQCs, while PQC parameters are updated classically using a primal-dual method. We propose permuting primal variables so that OPF observables are expressed in a banded form, allowing them to be measured efficiently. Numerical tests on the IEEE 57-node power system using Pennylane's simulator corroborate that the proposed doubly variational quantum framework can find high-quality OPF solutions. Although showcased for the OPF, this framework features a broader scope, including conic programs with numerous variables and constraints, problems defined over sparse graphs, and training quantum machine learning models to satisfy constraints.
Authors: Fangyuan Sun, Ruisheng Diao, Ruiyuan Zeng, Jing Zhang, Jianguo Qian
With the fast-increasing penetration of inverter-based resources (IBRs), the voltage support capability of the grid following (GFL) IBRs under low voltage ride through (LVRT) control significantly influences the transient voltage stability of the power system. The existing LVRT adjusts the q-axis current to regulate reactive power injection. However, under a large disturbance, the phase-locked loop (PLL) error invalidates the proportional relationship between the q-axis current and reactive power, consequently causing deviation in the actual reactive power injection of the IBR. Besides, the variation of IBR current, determined by the PLL phase and LVRT, also directly influences the transient voltage. To address this issue, the specific influence of PLL error on active and reactive power injection is first analyzed under LVRT control. In addition, by combining the LVRT and PLL dynamics, the mechanisms of three voltage problems caused by voltage angle coupling are revealed. overvoltage, low voltage, and DC-side overvoltage. The specific scenarios in which these voltage stability problems occur are also obtained by the voltage-vector-triangle graphic. Furthermore, a power angle decoupled LVRT control is proposed to eliminate the influence of voltage angle coupling. Finally, the mechanism analysis and effectiveness of the decoupled LVRT are verified in the case study.
Authors: Taulant Kerci, Federico Milano
This paper introduces the concept of "frequency control strength" as a novel approach to understand how different real-world power systems compare to each other in terms of effectiveness and performance of system-wide frequency control. It presents a comprehensive comparison, based on measurement data, of the frequency control strength of four real-world, renewable-based, synchronous islands power systems, namely Great Britain (GB), All-Island power system (AIPS) of Ireland, and Australia (AUS) mainland and Tasmania (TAS). The strength is evaluated by means of different frequency quality metrics. The common understanding is that the bigger the capacity of a power system, the bigger its robustness with respect to events and contingencies. Here we show that this is not always the case in the context of frequency control. In fact, our study shows that mainland AUS shows the highest frequency control strength during normal operating conditions, whereas the AIPS shows the highest relative frequency control strength for abnormal system conditions. The strength is, in particular, greatly influenced by different regulatory requirements and different system/ancillary services arrangements in each jurisdiction. The paper also provides possible mitigations to improve frequency control strength through grid codes and market rules.
Authors: Chao Duan, Adilson E. Motter
Averting catastrophic global warming requires decisive action to decarbonize key sectors. Vehicle electrification, alongside renewable energy integration, is a long-term strategy toward zero carbon emissions. However, transitioning to fully renewable electricity may take decades -- during which electric vehicles may still rely on carbon-intensive electricity. We analyze the critical role of the transmission network in enabling or constraining emissions reduction from U.S. vehicle electrification. Our models reveal that the available transmission capacity severely limits potential CO2 emissions reduction. With adequate transmission, full electrification could nearly eliminate vehicle operational CO2 emissions once renewable generation reaches the existing nonrenewable capacity. In contrast, the current grid would support only a fraction of that benefit. Achieving the full emissions reduction potential of vehicle electrification during this transition will require a moderate but targeted increase in transmission capacity. Our findings underscore the pressing need to enhance transmission infrastructure to unlock the climate benefits of large-scale electrification and renewable integration.
Authors: Chao Duan, Adilson E. Motter
Averting catastrophic global warming requires decisive action to decarbonize key sectors. Vehicle electrification, alongside renewable energy integration, is a long-term strategy toward zero carbon emissions. However, transitioning to fully renewable electricity may take decades -- during which electric vehicles may still rely on carbon-intensive electricity. We analyze the critical role of the transmission network in enabling or constraining emissions reduction from U.S. vehicle electrification. Our models reveal that the available transmission capacity severely limits potential CO2 emissions reduction. With adequate transmission, full electrification could nearly eliminate vehicle operational CO2 emissions once renewable generation reaches the existing nonrenewable capacity. In contrast, the current grid would support only a fraction of that benefit. Achieving the full emissions reduction potential of vehicle electrification during this transition will require a moderate but targeted increase in transmission capacity. Our findings underscore the pressing need to enhance transmission infrastructure to unlock the climate benefits of large-scale electrification and renewable integration.
Authors: Yangyadatta Tripathy, Barjeev Tyagi
This paper demonstrates the key features of a control system applicable to inverter-based resources (IBR), which is based on grid-forming technology. Such resources are classified as grid-forming or grid-following converters based on the type of output with or without grid connection. With rapid growth in the energy sector to adopt carbon-free generation, Grid Forming Converter (GFC) seems suitable for power provision to remote or islanded operation of converters. A fully-fledged bulk power grid based on GFC requires complex control implementation with suitable tuning of its parameters. In this article a broader analysis of synchronous machine and such type of converter is discussed and designed in the MATLAB 2024 environment with its control technique is studied for a closed-loop system under contingencies. A proposed control scheme is developed to understand the frequency minimization problem and the minimization problem is solved using GAMS programming tool. The primary objective function is found to be suitable for minimization of frequency deviation using a mixed control approach. An artificial neural network-based controller is also proposed with Levenberg-Marquardt training algorithm which augments the research by finding suitable optimal reference for GFM converter in the presence of a grid. A long-short-term memory (LSTM) based network is also proposed for the above control and the performance is found to be efficacious.
Authors: Yangyadatta Tripathy, Barjeev Tyagi
This paper presents the development of a mathematical model of a converter state space model for a hybrid microgrid. The hybrid model combines the models of components such as DC-Converters, DC-AC converters, and their individual controllers, as well as loads. The input to the converter is considered a constant DC voltage, assumed to originate from distributed generations like solar, battery storage, or fuel-cells. The converter output is connected to a DC line through an LCL filter. The controller circuitry is designed to regulate the voltage, current, and power from the converter. Sensors are strategically placed to measure the currents, voltages, and power, and calculate the reference pulse signal using PWM for the switch. Similarly, the DC-AC converter is modeled. In the state space domain the converter models is used to design overall microgrid system. A single DC converter has six states and two inputs, with all states as outputs. A single DC-AC converter has thirteen states and three inputs, with all states as outputs. Three such converters of each type are considered to develop the DC microgrid and AC microgrid, which are then combined using mathematical analysis to model a hybrid microgrid. For the hybrid microgrid development, network and load models were also included. Eigenvalue analysis has been conducted to study the small signal stability of the considered system. The complete state space model of the hybrid microgrid has been programmed, and a pole-placement controller has been designed to enhance the stability of the system.
Authors: Maedeh Izadi, A.T.J.R. Cobbenhagen, R.L. Sommer, A.R.P. Andrien, E. Lefeber, W.P.M.H. Heemels
In this paper, we present a cascade control structure to address the trajectory tracking problem for quadcopters, ensuring uniform global asymptotic stability of the state tracking error dynamics. An MPC strategy based on a 12-dimensional prediction model is proposed for the outer loop, explicitly accounting for time-varying coupled constraints, where multiple variables are interdependent and need to be handled together. The outer-loop controller generates an acceleration reference, which is then converted into attitude and angular velocity references, later tracked by a nonlinear inner-loop controller. Numerical simulations validate the approach, demonstrating enhanced performance in precise and fast tracking by imposing less conservative constraints than existing approaches, while still guaranteeing stability.
Authors: J. L. González, R. L. Moreno, D. Vázquez
This paper investigates the impact of technological constraints on passive elements in the design of inductively degenerated CMOS low-noise amplifiers (LNAs). A theoretical analysis is combined with circuit simulations in a 130-nm CMOS process at 2.45~GHz to explore how the available inductance and capacitance values limit key design objectives such as maximum gain, minimum power consumption, and transistor sizing. Results show that these limits significantly restrict the achievable design space, particularly for low-power implementations, and highlight the need to incorporate detailed passive-element models into RF integrated circuit design flows.
Authors: Youssef Ait Si, Ratnangshu Das, Negar Monir, Sadegh Soudjani, Pushpak Jagtap, Adnane Saoud
In this paper, we consider the notion of resilience of a dynamical system, defined by the maximum disturbance a controlled dynamical system can withstand while satisfying given temporal logic specifications. Given a dynamical system and a specification, the objective is to synthesize the controller such that the closed-loop system satisfies this specification while maximizing its resilience. The problem is formulated as a robust optimization program where the objective is to compute the maximum resilience while simultaneously synthesizing the corresponding controller parameters. For linear systems and linear controllers, exact solutions are provided for the class of time-varying polytopic specifications. For the case of nonlinear systems, nonlinear controllers and more general specifications, we leverage tools from the scenario optimization approach, offering a probabilistic guarantee of the solution as well as computational feasibility. Different case studies are presented to illustrate the theoretical results.