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Blackout Mitigation via Physics-Guided RL

IEEE Transactions on Power systems 2025年第3期40卷 2363-2375页

作者： Dwivedi, Anmol Paternain, Santiago Tajer, Ali Rensselaer Polytechnic Institute Electrical Computer and Systems Engineering Department TroyNY12180 United States

This paper considers the sequential design of remedial control actions in response to system anomalies to prevent blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions involving both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black-box counterparts. One important observation is that strategically removing transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts. © 1969-2012 IEEE.

关键词： Electric load flow

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Advanced Scalable Multi-Beam Focusing for Indoor Optical Wireless Networks With IR Radiative Clusters

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3624-3643页

作者： Gunathilake, Sharadhi Nirmalathas, Ampalavanapillai Herath, Kosala Premaratne, Malin Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia The University of Melbourne Department of Electrical and Electronic Engineering ParkvilleVIC3010 Australia

Optical wireless networks emerge as a promising solution to the ever-growing data demand for user-centric indoor applications. This work demonstrates a novel approach to advance multi-beam radiation patterns in indoor optical wireless networks by utilizing a cluster-based optical aperture comprising IR radiative elements. Spatially distributed IR clusters permit a non-uniform spherical wave model to focus the radiation in the near-field regime. By executing sub-clusters within main clusters and assigning them to groups for phase delay compensation, we ensure the generation of independent narrow beams focused on each receiver simultaneously. To mitigate the grating lobe formation, we incorporate a dual-carrier framework that introduces an effective wavelength for the system. Based on this theoretical model, we examine multi-beam focusing with a systematic arrangement of clusters on a planar ceiling. It follows a phased array within a phased array structure and incorporates a sub-cluster segmentation algorithm. We suggest optimizing cluster excitation based on receiver positions to enhance power efficiency and safety. This involves selecting the optimal clusters from a uniform array by solving a multiobjective non-convex binary optimization problem, aiming to maximize receiver intensity, minimize intensity variations, and reduce side lobes level. Instead of stochastic algorithms, we adopt a sparse relaxation-based weighted sum method that convexifies the binary space with L1 norm regularization compensating for convexity. The Transformed problem is solved deterministically via Nelder-Mead simplex without gradients. Simulated results confirm a better multi-beam focusing pattern, effectively balancing three objectives. Our findings pave the way for sustainable indoor optical wireless networks in next-generation communication. © 2020 IEEE.

关键词： Multiobjective optimization

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Distributed Attention-Enabled Multi-Agent Reinforcement Learning Based Frequency Regulation of Power systems

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IEEE Transactions on Power systems 2025年第3期40卷 2427-2437页

作者： Zhao, Yunzheng Liu, Tao Hill, David J. University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia

This paper develops a new distributed attention-enabled multi-agent reinforcement learning method for frequency regulation of power systems. Specifically, the controller of each generator is modelled as an agent, and the reward and observation are designed based on the characteristics of power systems. All the agents learn their own control policies in the offline training phase and generate frequency control signals in the online execution phase. The target of the proposed algorithm is to conduct both offline training and online frequency control in a distributed way. To achieve this goal, two distributed information-sharing mechanisms are proposed based on the different global information to be discovered. First, a consensus-based reward-sharing mechanism is designed to estimate the globally averaged reward. Second, a distributed observation-sharing scheme is developed to discover the global observation information. Furthermore, the attention strategy is embedded in the observation-sharing scheme to help agents adaptively adjust the importance of observations from different neighbors. With these two mechanisms, a new distributed attention-enabled proximal policy optimization (DAPPO) based method is proposed to achieve model-free frequency control. Simulation results on the IEEE 39-bus system and the NPCC 140-bus system demonstrate that the proposed DAPPO achieves stable offline training and effective online frequency control. © 1969-2012 IEEE.

关键词： Reinforcement learning

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A Mathematical and Dynamical Model for Analyzing H-Shaped PM Vernier Motor for Electric Motorcycle Mid-Drive Applications

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IEEE Transactions on Energy Conversion 2025年第1期40卷 529-543页

作者： Ghods, Mehrage Faiz, Jawad Bazrafshan, Mohammad Amin Gorginpour, Hamed Toulabi, Mohammad Sedigh University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran*** Iran Persian Gulf University Department of Intelligent Systems Engineering and Data Science Bushehr*** Iran Oakland University Department of Electrical and Computer Engineering RochesterMI48309 United States

The permanent magnet (PM) Vernier machines enhance torque density and decrease cogging torque compared to conventional permanent magnet synchronous motor. This paper presents a novel fractional-slot H-shaped PM Vernier machines, which incorporates unique design for the PM housing and rotor core construction. These designs are specifically optimized for the small-scale electric vehicles application. The choice of the PM arrangement is based on the combination of V-shaped and spoke-array PM topologies. The aim is to achieve a higher torque density than V-shaped PM machines, while reducing cogging torque compared to spoke-array PM machines. By using this methodology, leakage fluxes in the terminal region of the PMs reduce, resulting in an augmentation of both flux-linkage and power factor. A prototype 680 W motor was fabricated with 12 slots and 8 poles to verify the accuracy and effectiveness of the proposed model. Mathematical model and dynamical analysis are proposed for the analytical prediction of machine performance. Due to the unique shape feature of the machine, the conformal mapping approach is applied to design the rotor core. To validate the model, the predicted performance of the motor was compared with those obtained by finite element analysis and experiments. © 1986-2012 IEEE.

关键词： Permanent magnets

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Generalized Regenerating Codes and Node Repair on Graphs

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IEEE Transactions on Information Theory 2025年第3期71卷 1613-1630页

作者： Patra, Adway Barg, Alexander University of Maryland Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD20742 United States

We consider regenerating codes in distributed storage systems where connections between the nodes are constrained by a graph. In this problem, the failed node downloads the information stored at a subset of vertices of the graph for the purpose of recovering the lost data. Compared to the standard setting, regenerating codes on graphs address two additional features. The repair information is moved across the network, and the cost of node repair is determined by the graphical distance from the helper nodes to the failed node. Accordingly, the helpers far away from the failed node may be expected to contribute less data for repair than the nodes in the neighborhood of that node. We analyze regenerating codes with nonuniform download for repair on graphs. Moreover, in the process of repair, the information moved from the helpers to the failed node may be combined through intermediate processing, reducing the repair bandwidth. We derive lower bounds for communication complexity of node repair on graphs, including repair schemes with nonuniform download and intermediate processing, and construct codes that attain these bounds. Additionally, some of the nodes may act as adversaries, introducing errors into the data moved in the network. For repair on graphs in the presence of adversarial nodes, we construct codes that support node repair and error correction in systematic nodes. © 1963-2012 IEEE.

关键词： Maintenance engineering Codes Costs Network coding IP networks Bandwidth Symbols Encoding Vectors Standards

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Latency Bounds for TSN Scheduling in the Presence of Clock Synchronization

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 41-45页

作者： Ghosh, Aviroop Yousefi, Saleh Kunz, Thomas Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Urmia University Department of Electrical and Computer Engineering Urmia57561-51818 Iran

The IEEE 802.1Qbv (80.21Qbv) standard is designed for traffic requiring deterministic and bounded latencies through strict periodic time synchronization, as specified by IEEE 802.1AS standard. However, internal clock drift in devices causes timing misalignment, introducing further challenges to 802.1Qbv scheduling. Existing solutions, using either complex optimization approaches or non-trivial scheduling heuristics, address this by scheduling frame transmissions only once they are guaranteed to have been fully received, even in the presence of clock drifts. However, this approach introduces additional delays that can impact deadline requirements. This letter analytically derives tight end-to-end latency bounds, allowing us to determine if stream deadlines for a given network will be violated without the need to solve for any scheduling algorithms. It also proposes an approach that results in tighter bounds based on information collected from the synchronization process. The analytical results are compared with simulation results, confirming their validity. © 2019 IEEE.

关键词： Synchronization Clocks Delays Standards Schedules Logic gates Scheduling algorithms Payloads Jitter Data models

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

引用

36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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