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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Rahman, M.T. Jahid, M.A. Anha, S.Y. Rahman, Md. Shohanur Azmi Orpy, F. Bakibillah, A.S.M. Department of Computer Science and Engineering Bangladesh Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo152-8552 Japan

ISBN: (纸本)9798350380040

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems. © 2024 IEEE.

关键词： Cost effectiveness

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360-Degree Sensor Integration to Optimize Reinforcement Learning for Autonomous Vehicles 14

360-Degree Sensor Integration to Optimize Reinforcement Lear...

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14th Asian control Conference, ASCC 2024

作者： Mardhatillah, Irina Nazaruddin, Yul Yunazwin Widyotriatmo, Augie Institut Teknologi Bandung Instrumentation Control and Automation Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (纸本)9789887581598

The prevalent use of front-view and top-view cam-eras in reinforcement learning (RL)-based autonomous vehicle agents introduces limitations in observing the surroundings. To address these challenges, we propose the integration of 360-degree sensors on the agent, ensuring comprehensive observation cover-age. Our approach involves strategically placing four cameras on the vehicle's sides, supplemented by Light Detection and Ranging (LiDAR) technology to eliminate blind spots. Deep reinforcement learning (deep RL) is employed to develop a driving policy controlling the agent's velocity and steering based on the sensor information. Two sensor configurations are explored, namely the common top-view camera and our 360-degree sensor integration. Simulation results demonstrate excellent performance for the agent equipped with our proposed sensor integration, particu-larly in complex traffic scenarios. The evaluation matrix reveals improved route completion rates, reduced collisions, and lower average collision impulse, underscoring the effectiveness of our proposed sensor configuration. © 2024 Asian control Association.

关键词： Deep reinforcement learning

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Blockchain-Based Layered Secure Edge Content Delivery in UAV-Assisted Vehicular Networks

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7914-7927页

作者： Xu, Qichao Jin, Jie Su, Zhou Li, Ruidong Wang, Yuntao Fang, Dongfeng Wu, Yuan Shanghai University School of Mechatronic Engineering and Automation Shanghai China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China Kanazawa University Institute of Science and Engineering Kanazawa Japan California Polytechnic State University Department of Computer Science Obispo San Luis United States University of Macau Department of Computer and Information Science China

Recently, edge content delivery has been promoted for video applications in unmanned aerial vehicle (UAV)-assisted vehicular networks (UVNs). UAVs could proactively cache (video) contents and transmit them to nearby vehicular users, thereby significantly mitigating delivery latency. However, since UAVs are typically deployed by untrusted third parties, the contents may be illicitly accessed by some curious and even malicious UAVs, compromising users' privacy. Besides, due to the limited resources, UAVs may be unwilling to deliver contents without adequate compensations. To address these issues, in this paper, we propose a novel secure edge content delivery scheme in UVNs. Specifically, we first devise a blockchain-based layered secure edge content delivery framework. With the scalable video coding (SVC) that encodes each content into a base layer and multiple enhancement layers, only the enhancement layers of the content are delivered by UAVs, ensuring that untrusted UAVs without the base layer cannot recover the content to access explicit information. Meanwhile, a lightweight consortium blockchain is utilized to supervise the enhancement layer delivery services of UAVs in a distributed fashion. The delegated proof of stake (DPoS)-based practical Byzantine fault tolerance (PBFT) consensus algorithm is designed to immutably and traceably record content delivery transactions between UAVs and vehicular users. Then, we formulate the content delivery incentive problem as a Stackelberg game, where UAVs act as game leaders to determine content delivery prices and vehicular users act as game followers to determine the number of required enhancement layers. Afterwards, through game analysis using the backward induction approach, the Stackelberg equilibrium is attained as the solution to the formulated problem, where the optimal strategies of both UAVs and vehicular users are derived by the Q-learning algorithm. Finally, extensive simulations are conducted to demonstrate that

关键词： Scalable video coding

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A Data-Driven Adaptive control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Exploring the Capabilities and Limitations of Recommender System Models in the Electric Power Sector

Exploring the Capabilities and Limitations of Recommender Sy...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Li, Yifu Dehghanian, Payman Zhang, Xiaohua Su, Jinshun Zhang, Ruotan The George Washington University Department of Electrical and Computer Engineering WashingtonDC United States Changzhou University Department of Electrical Engineering and Automation Jiangsu Changzhou China

ISBN: (纸本)9798331541125

As the modern bulk power system (BPS) expands and electricity users increase, an ever-growing amount of power data needs to be processed. The recommender system (RS), as an artificial intelligence (AI) algorithm, leverage big data to suggest additional products to consumers, making them naturally suited for BPS analysis. This paper summarizes published works on RS applications in the electric power sector, explores the goals and objectives of each proposed use case, and analyzes the advantages and limitations of their capabilities. Furthermore, this paper evaluates the performance of three large language models (LLMs) on power system domain challenges to compare the differences between the models. Finally, the paper summarizes the current applications of RS models in the electric power sector and explores prospects for future development. © 2025 IEEE.

关键词： Power systems computer aided design

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Cooperative nonlinear distributed model predictive control with dissimilar control horizons 63

Cooperative nonlinear distributed model predictive control w...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Chanfreut, Paula Maestre, Jose M. Zhu, Quanyan Heemels, W.P.M.H. Maurice Eindhoven University of Technology Department of Mechanical Engineering Netherlands University of Seville Department of Systems and Automation Engineering Spain New York University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

In this paper, we introduce a nonlinear distributed model predictive control (DMPC) algorithm, which allows for dissimilar and time-varying control horizons among agents, thereby addressing a common limitation in current DMPC schemes. We consider cooperative agents with varying computational capabilities and operational objectives, each willing to manage varying numbers of optimization variables at each time step. Recursive feasibility and a non-increasing evolution of the optimal cost are proven for the proposed algorithm. Through numerical simulations on systems with three agents, we show that our approach effectively approximates the performance of traditional DMPC, while reducing the number of variables to be optimized. This advancement paves the way for a more decentralized yet coordinated control strategy in various applications, including power systems and traffic management. © 2024 IEEE.

关键词： Predictive control systems

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Towards Virtual Commissioning Environment for Smart Mechatronic Systems

Towards Virtual Commissioning Environment for Smart Mechatro...

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18th IEEE International Conference on Advanced Motion control, AMC 2024

作者： Lyu, Tuojian Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350342819

This paper presents progress in the development of a virtual commissioning environment for the validation of intelligent mechatronic systems with the following properties: 1) decentralised control architecture without a master controller, 2) open programmability based on the IEC 61499 standard, and 3) heterogeneous wired/wireless communication architecture. The presented environment is capable of instantiating in the cloud images of distributed control nodes connected to each other and with a plant simulation model. The environment aims to facilitate the verification of functional and non-functional requirements for complex automation systems composed of autonomous collaborating units. © 2024 IEEE.

关键词： Virtual reality

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & Sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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A Deep Echo State Network with Scaling Factor Activation Functions for Fault Diagnosis of Electrical Drive Systems

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7874-7884页

作者： Gong, Yuanpeng Jiang, Yulian Cheng, Chao Chen, Hongtian Wang, Shenquan Changchun University of Technology College of Electrical and Electronic Engineering Changchun130012 China Changchun University of Technology School of Computer Science and Engineering Changchun130012 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

Deep echo state networks (Deep-ESNs) play an important role in fault diagnosis. However, due to its limitation in the iterative process of dealing with nonlinear data, the accuracy of fault diagnosis is relatively low. In order to improve the fault diagnosis accuracy of electrical drive systems, this paper proposes a novel Deep-ESN based on the synergistic effect of golden jackal optimization (GJO), variational mode decomposition (VMD) and scaling factor activation function, called GVSD-ESN. The main work of this study contains: 1) the novel scaling factor activation function is proposed to solve the gradient vanishing problem in the Deep-ESN model;2) GJO is used to solve high-dimensional optimization problems of VMD;3) the power spiral curve is proposed to optimize the position update equation of GJO, which solves the problem of falling into the local optimal;4) adding a sparse regularization layer between reservoirs can enhance the class definition of GVSD-ESN. Finally, the effectiveness of the proposed method is verified in an electrical drive systems. © 2015 IEEE.

关键词： Variational mode decomposition

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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