pwc-archive/papers-with-abstracts huggingface dataset:
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation
Authors: Tao Yin, Chen Zhao, Xiaoyan Liu, Minglai Shao
Date: 2025-04-29 00:00:00
Tasks: Node Classification, Out-of-Distribution Detection

Graph neural networks (GNNs) are proven effective in extracting complex node and structural information from graph data. While current GNNs perform well in node classification tasks within in-distribution (ID) settings, real-world scenarios often present distribution shifts, leading to the presence of out-of-distribution (OOD) nodes. OOD detection in graphs is a crucial and challenging task. Most existing research focuses on homogeneous graphs, but real-world graphs are often heterogeneous, consisting of diverse node and edge types. This heterogeneity adds complexity and enriches the informational content. To the best of our knowledge, OOD detection in heterogeneous graphs remains an underexplored area. In this context, we propose a novel methodology for OOD detection in heterogeneous graphs (OODHG) that aims to achieve two main objectives: 1) detecting OOD nodes and 2) classifying all ID nodes based on the first task's results. Specifically, we learn representations for each node in the heterogeneous graph, calculate energy values to determine whether nodes are OOD, and then classify ID nodes. To leverage the structural information of heterogeneous graphs, we introduce a meta-path-based energy propagation mechanism and an energy constraint to enhance the distinction between ID and OOD nodes. Extensive experimental findings substantiate the simplicity and effectiveness of OODHG, demonstrating its superiority over baseline models in OOD detection tasks and its accuracy in ID node classification.
Approximation-free Control for Signal Temporal Logic Specifications using Spatiotemporal Tubes
Authors: Ratnangshu Das, Subhodeep Choudhury, Pushpak Jagtap
Date: 2025-05-08 00:00:00
Tasks: Computational Efficiency

This paper presents a spatiotemporal tube (STT)-based control framework for satisfying Signal Temporal Logic (STL) specifications in unknown control-affine systems. We formulate STL constraints as a robust optimization problem (ROP) and recast it as a scenario optimization program (SOP) to construct STTs with formal correctness guarantees. We also propose a closed-form control law that operates independently of the system dynamics, and ensures the system trajectory evolves within the STTs, thereby satisfying the STL specifications. The proposed approach is validated through case studies and comparisons with state-of-the-art methods, demonstrating superior computational efficiency, trajectory quality, and applicability to complex STL tasks.
Optimal Microgrid Sizing of Offshore Renewable Energy Sources for Offshore Platforms and Coastal Communities
Authors: Ann Mary Toms, Xingpeng Li, Kaushik Rajashekara
Date: 2025-05-08 00:00:00

The global energy landscape is undergoing a transformative shift towards renewable energy and advanced storage solutions, driven by the urgent need for sustainable and resilient power systems. Isolated offshore communities, such as islands and offshore platforms, which traditionally rely on mainland grids or diesel generators, stand to gain significantly from renewable energy integration. Promising offshore renewable technologies include wind turbines, wave and tidal energy converters, and floating photovoltaic systems, paired with a storage solution like battery energy storage systems. This paper introduces a renewable energy microgrid optimizer (REMO), a tool designed to identify the optimal sizes of renewable generation and storage resources for offshore microgrids. A key challenge in such models is accurately accounting for battery degradation costs. To address this, the REMO model integrates a deep neural network-based battery degradation (DNN-BD) module, which factors in variables like ambient temperature, charge/discharge rates, state of charge, depth of discharge and battery health. Simulations on six test regions demonstrate that the REMO-DNN-BD approach minimizes lifetime energy costs while maintaining high reliability and sustainability, making it a viable design solution for offshore microgrid systems.
CV-MP: Max-Pressure Control in Heterogeneously Distributed and Partially Connected Vehicle Environments
Authors: Chaopeng Tan, Dingshan Sun, Hao liu, Marco Rinaldi, Hans van Lint
Date: 2025-05-08 00:00:00
Tasks: Traffic Signal Control

Max-pressure (MP) control has emerged as a prominent real-time network traffic signal control strategy due to its simplicity, decentralized structure, and theoretical guarantees of network queue stability. Meanwhile, advances in connected vehicle (CV) technology have sparked extensive research into CV-based traffic signal control. Despite these developments, few studies have investigated MP control in heterogeneously distributed and partially CV environments while ensuring network queue stability. To address these research gaps, we propose a CV-based MP control (CV-MP) method that leverages real-time CV travel time information to compute the pressure, thereby incorporating both the spatial distribution and temporal delays of vehicles, unlike existing approaches that utilized only spatial distribution or temporal delays. In particular, we establish sufficient conditions for road network queue stability that are compatible with most existing MP control methods. Moreover, we pioneered the proof of network queue stability even if the vehicles are only partially connected and heterogeneously distributed, and gave a necessary condition of CV observation for maintaining the stability. Evaluation results on an Amsterdam corridor show that CV-MP significantly reduces vehicle delays compared to both actuated control and conventional MP control across various CV penetration rates. Moreover, in scenarios with dynamic traffic demand, CV-MP achieves lower spillover peaks even with low and heterogeneous CV penetration rates, further highlighting its effectiveness and robustness.
High Altitude Platform-Based Caching and Multicasting for Rural Connectivity
Authors: Yongqiang Zhang, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2025-05-08 00:00:00

Providing efficient and reliable content delivery in rural areas remains a significant challenge due to the lack of communication infrastructure. To bridge the digital divide, this paper investigates the potential of leveraging multiple high-altitude platforms (HAPs) for energy-efficient content delivery in wide rural regions. Each caching-enabled HAP is equipped with both Free-Space Optical (FSO) transceivers for backhaul links and Radio Frequency (RF) antenna arrays for access links. To further enhance network efficiency, we consider a network coding-based multicasting scheme, where different types of content are treated as distinct multicast sessions. With the objective of minimizing long-term power cost, we propose a hierarchical framework that integrates deep reinforcement learn-ing (DRL) and convex optimization to jointly optimize dynamic caching strategies and resource allocation across the network. Simulation results demonstrate that our approach significantly reduces power cost compared to several baseline approaches, providing a practical solution for improving rural connectivity.
Adaptive Biased User Scheduling for Heterogeneous Wireless Federate Learning Network
Authors: Changxiang Wu, Yijing Ren, Daniel K. C. So, Jie Tang
Date: 2025-05-08 00:00:00
Tasks: Deep Reinforcement Learning, Federated Learning, Scheduling

Federated Learning (FL) has revolutionized collaborative model training in distributed networks, prioritizing data privacy and communication efficiency. This paper investigates efficient deployment of FL in wireless heterogeneous networks, focusing on strategies to accelerate convergence despite stragglers. The primary objective is to minimize long-term convergence wall-clock time through optimized user scheduling and resource allocation. While stragglers may introduce delays in a single round, their inclusion can expedite subsequent rounds, particularly when they possess critical information. Moreover, balancing single-round duration with the number of cumulative rounds, compounded by dynamic training and transmission conditions, necessitates a novel approach beyond conventional optimization solutions. To tackle these challenges, convergence analysis with respect to adaptive and biased scheduling is derived. Then, by factoring in real-time system and statistical information, including diverse energy constraints and users' energy harvesting capabilities, a deep reinforcement learning approach, empowered by proximal policy optimization, is employed to adaptively select user sets. For the scheduled users, Lagrangian decomposition is applied to optimize local resource utilization, further enhancing system efficiency. Simulation results validate the effectiveness and robustness of the proposed framework for various FL tasks, demonstrating reduced task time compared to existing benchmarks under various settings.
Day-Ahead Bidding Strategies for Wind Farm Operators under a One-Price Balancing Scheme
Authors: Max Bruninx, Timothy Verstraeten, Jalal Kazempour, Jan Helsen
Date: 2025-05-08 00:00:00

We study day-ahead bidding strategies for wind farm operators under a one-price balancing scheme, prevalent in European electricity markets. In this setting, the profit-maximising strategy becomes an all-or-nothing strategy, aiming to take advantage of open positions in the balancing market. However, balancing prices are difficult, if not impossible, to forecast in the day-ahead stage and large open positions can affect the balancing price by changing the direction of the system imbalance. This paper addresses day-ahead bidding as a decision-making problem under uncertainty, with the objective of maximising the expected profit while reducing the imbalance risk related to the strategy. To this end, we develop a stochastic optimisation problem with explicit constraints on the positions in the balancing market, providing risk certificates, and derive an analytical solution to this problem. Moreover, we show how the price-impact of the trading strategy on the balancing market can be included in the ex-post evaluation. Using real data from the Belgian electricity market and an offshore wind farm in the North Sea, we demonstrate that the all-or-nothing strategy negatively impacts the balancing price, resulting in long-term losses for the wind farm. Our risk-constrained strategy, however, can still significantly enhance operational profit compared to traditional point-forecast bidding.
Predictive Control of EV Overnight Charging with Multi-Session Flexibility
Authors: Felix Wieberneit, Emanuele Crisostomi, Anthony Quinn, Robert Shorten
Date: 2025-05-08 00:00:00
Tasks: Model Predictive Control, Scheduling

The majority of electric vehicles (EVs) are charged domestically overnight, where the precise timing of power allocation is not important to the user, thus representing a source of flexibility that can be leveraged by charging control algorithms. In this paper, we relax the common assumption, that EVs require full charge every morning, enabling additional flexibility to defer charging of surplus energy to subsequent nights, which can enhance the performance of controlled charging. In particular, we consider a simple domestic smart plug, scheduling power delivery with the objective to minimize CO$_2$ emissions over prediction horizons of multiple sessions -- up to seven days ahead -- utilising model predictive control (MPC). Based on carbon intensity data from the UK National Grid, we demonstrate significant potential for emission reductions with multi-session planning of 40 to 46\% compared to uncontrolled charging and 19 to 26\% compared to single-session planning. Furthermore, we assess, how the driving and charging behaviour of EV users affects the available flexibility and consequentially the potential for emission reductions. Finally, using grid carbon intensity data from 14 different UK regions, we report significant variations in absolute emission reductions based on the local energy mix.
Enhanced Robust Tracking Control: An Online Learning Approach
Authors: Ao Jin, Weijian Zhao, Yifeng Ma, Panfeng Huang, Fan Zhang
Date: 2025-05-08 00:00:00
Tasks: continuous-control, Continuous Control

This work focuses the tracking control problem for nonlinear systems subjected to unknown external disturbances. Inspired by contraction theory, a neural network-dirven CCM synthesis is adopted to obtain a feedback controller that could track any feasible trajectory. Based on the observation that the system states under continuous control input inherently contain embedded information about unknown external disturbances, we propose an online learning scheme that captures the disturbances dyanmics from online historical data and embeds the compensation within the CCM controller. The proposed scheme operates as a plug-and-play module that intrinsically enhances the tracking performance of CCM synthesis. The numerical simulations on tethered space robot and PVTOL demonstrate the effectiveness of proposed scheme. The source code of the proposed online learning scheme can be found at https://github.com/NPU-RCIR/Online_CCM.git.
Learning Economic Model Predictive Control via Clustering and Kernel-Based Lipschitz Regression
Authors: Weiliang Xiong, Defeng He, Haiping Du
Date: 2025-05-08 00:00:00
Tasks: Model Predictive Control, regression

This paper presents a novel learning economic model predictive control scheme for uncertain nonlinear systems subject to input and state constraints and unknown dynamics. We design a fast and accurate Lipschitz regression method using input and output data that combines clustering and kernel regression to learn the unknown dynamics. In each cluster, the parallel convex optimization problems are solved to estimate the kernel weights and reduce the Lipschitz constant of the predictor, hence limiting the error propagation in the prediction horizon. We derive the two different bounds of learning errors in deterministic and probabilistic forms and customize a new robust constraint-tightening strategy for the discontinuous predictor. Then, the learning economic model predictive control algorithm is formulated by introducing a stabilized optimization problem to construct a Lyapunov function. Sufficient conditions are derived to ensure the recursive feasibility and input-to-state stability of the closed-loop system. The effectiveness of the proposed algorithm is verified by simulations of a numerical example and a continuously stirred tank reactor.
Consensus Seminorms and their Applications
Authors: Ron Ofir, Ji Liu, A. Stephen Morse, Brian D. O. Anderson
Date: 2025-05-07 00:00:00

Consensus is a well-studied problem in distributed sensing, computation and control, yet deriving useful and easily computable bounds on the rate of convergence to consensus remains a challenge. We study the applications of seminorms for this goal. We revisit a previously suggested family of seminorms and correct an error made in their original presentation where it was claimed that the a certain seminorm is equal to the well-known coefficient of ergodicity. We then propose a wider family of seminorms which guarantee convergence at an exponential rate of infinite products of matrices which generalizes known results on stochastic matrices to the class of matrices whose row sums are all equal one. Finally, we show that such seminorms cannot be used to bound the rate of convergence of classes larger than the well-known class of scrambling matrices, and pose several open questions for future research.
Integrated equilibrium model for electrified logistics and power systems
Authors: Rui Yao, Xuhang Liu, Anna Scaglione, Shlomo Bekhor, Kenan Zhang
Date: 2025-05-07 00:00:00
Tasks: Scheduling

This paper proposes an integrated equilibrium model to characterize the complex interactions between electrified logistics systems and electric power delivery systems. The model consists of two major players: an electrified logistics operator (ELO) and a power system operator (PSO). The ELO aims to maximize its profit by strategically scheduling and routing its electric delivery vehicles (e-trucks) for deliveries and charging, in response to the locational marginal price (LMP) set by the PSO. The routing, delivery, and charging behaviors of e-trucks are modeled by a perturbed utility Markov decision process (PU-MDP) while their collective operations are optimized to achieve the ELO's objective by designing rewards in the PU-MDP. On the other hand, PSO optimizes the energy price by considering both the spatiotemporal e-truck charging demand and the base electricity load. The equilibrium of the integrated system is formulated as a fixed point, proved to exist under mild assumptions, and solved for a case study on the Hawaii network via Anderson's fixed-point acceleration algorithm. Along with these numerical results, this paper provides both theoretical insights and practical guidelines to achieve sustainable and efficient operations in modern electrified logistics and power systems.
NN-Based Joint Mitigation of IQ Imbalance and PA Nonlinearity With Multiple States
Authors: Yundi Zhang, Wendong Cheng, Li Chen
Date: 2025-05-07 00:00:00
Tasks: Multi-Task Learning

Joint mitigation of IQ imbalance and PA nonlinearity is important for improving the performance of radio frequency (RF) transmitters. In this paper, we propose a new neural network (NN) model, which can be used for joint digital pre-distortion (DPD) of non-ideal IQ modulators and PAs in a transmitter with multiple operating states. The model is based on the methodology of multi-task learning (MTL). In this model, the hidden layers of the main NN are shared by all signal states, and the output layer's weights and biases are dynamically generated by another NN. The experimental results show that the proposed model can effectively perform joint DPD for IQ-PA systems, and it achieves better overall performance within multiple signal states than the existing methods.
Self-Calibrating Position Measurements: Applied to Imperfect Hall Sensors
Authors: Max van Meer, Marijn van Noije, Koen Tiels, Enzo Evers, Lennart Blanken, Gert Witvoet, Tom Oomen
Date: 2025-05-07 00:00:00
Tasks: Position

Linear Hall sensors are a cost-effective alternative to optical encoders for measuring the rotor positions of actuators, with the main challenge being that they exhibit position-dependent inaccuracies resulting from manufacturing tolerances. This paper develops a data-driven calibration procedure for linear analog Hall sensors that enables accurate online estimates of the rotor angle without requiring expensive external encoders. The approach combines closed-loop data collection with nonlinear identification to obtain an accurate model of the sensor inaccuracies, which is subsequently used for online compensation. Simulation results show that when the flux density model structure is known, measurement errors are reduced to the sensor noise floor, and experiments on an industrial setup demonstrate a factor of 2.6 reduction in the root-mean-square measurement error. These results confirm that Hall sensor inaccuracies can be calibrated even when no external encoder is available, improving their practical applicability.
Impact of Grid-Forming Inverters on Protective Relays: A Perspective for Current Limiting Control Design
Authors: Yifei Li, Heng Wu, Xiongfei Wang
Date: 2025-05-07 00:00:00

Grid-forming (GFM) inverters can significantly alter the fault characteristics of power systems, which challenges the proper function of protective relays. This paper gives a holistic analysis of the interaction between GFM inverter-based resources (IBRs) and the supervising elements in protective relays, including directional and phase selection elements. It is revealed that the current limiting control (CLC) that is based on the current reference saturation method, adversely affects the performance of supervising elements that rely on the negative-sequence quantities. In contrast, adopting highly inductive virtual impedance in the CLC enables a reliable operation of such elements. This finding provides insights into the design of CLC for GFM IBRs from a protection perspective. It is further found that even with a highly inductive virtual impedance, the altered virtual impedance dynamics introduced by the CLC can still lead to malfunctions of the incremental quantity-based supervising elements. These theoretical findings are corroborated by simulations and controller hardware-in-the-loop (CHIL) tests.
Energy Efficient RSMA-Based LEO Satellite Communications Assisted by UAV-Mounted BD-Active RIS: A DRL Approach
Authors: Rahman Saadat Yeganeh, Hamid Behroozi
Date: 2025-05-07 00:00:00
Tasks: Deep Reinforcement Learning

This paper proposes an advanced non-terrestrial communication architecture that integrates Rate-Splitting Multiple Access (RSMA) with a Beyond-Diagonal Active Reconfigurable Intelligent Surface (BD-ARIS) mounted on a UAV under the coverage of a Low Earth Orbit (LEO) satellite. The BD-ARIS adopts a group-connected structure to enhance signal amplification and adaptability, while RSMA enables efficient multi-user access by dividing messages into common and private components. The system jointly optimizes satellite beamforming, UAV positioning, power allocation, and rate-splitting ratios to maximize the overall energy efficiency (EE). To solve the resulting non-convex and high-dimensional problem, we employ three state-of-the-art deep reinforcement learning (DRL) algorithms: Trust Region Policy Optimization (TRPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Asynchronous Advantage Actor-Critic (A3C). Moreover, realistic models for the power consumption of both the UAV and the BD-ARIS are considered. Simulation results reveal that TRPO consistently achieves the best performance in terms of EE and sum rate, especially under high transmit powers and challenging deployment scenarios. TD3 converges faster and performs competitively in moderate settings, while A3C suffers from instability due to its high variance. Additionally, the robustness of each algorithm under channel state information (CSI) uncertainty is evaluated, confirming TRPO resilience to imperfect observations. Overall, the proposed RSMA-BD-ARIS framework significantly outperforms conventional RIS-assisted designs and provides a scalable, energy-efficient solution for 6G and massive IoT applications in non-terrestrial networks.
Scalable 49-Channel Neural Recorder with an Event-Driven Ramp ADC and PCA Compression in 28 nm CMOS
Authors: William Lemaire, Esmaeil Ranjbar Koleibi, Maher Benhouria, Konin Koua, Jérémy Ménard, Keven Gagnon, Charles Quesnel, Louis-Philippe Gauthier, Takwa Omrani, Montassar Dridi, Mahdi Majdoub, Marwan Besrour, Sébastien Roy, Réjean Fontaine
Date: 2025-05-07 00:00:00
Tasks: Spike Sorting

Neural interfaces advance neuroscience research and therapeutic innovations by accurately measuring neuronal activity. However, recording raw data from numerous neurons results in substantial amount of data and poses challenges for wireless transmission. While conventional neural recorders consume energy to digitize and process the full neural signal, only a fraction of this data carries essential spiking information. Leveraging on this signal sparsity, this paper introduces a neural recording integrated circuit in TSMC 28nm CMOS. It features an event-driven ramp analog-to-digital converter, and a spike compression module based on principal component analysis. The circuit consists of 49 channels, each occupying an on-chip area of 50 $\times$ 60 $\mu$m$^2$. The circuit measures 1370 $\times$ 1370 $\mu$m$^2$ and consumes 534 $\mu$W. Compression testing on a synthetic dataset demonstrated an 8.8-fold reduction compared to raw spikes and a 328-fold reduction relative to the raw signal. This compression approach maintained a spike sorting accuracy of 74.9%, compared to the 79.5% accuracy obtained with the raw signal. The paper details the architecture and performance outcomes of the neural recording circuit and its compression module.