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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

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. © 2025, CC BY-NC-SA.

关键词： Mean square error

Transactive energy strategy for energy trading of proactive distribution company with renewable systems: A robust/stochastic hybrid technique

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e-Prime - Advances in electrical engineering, Electronics and Energy 2022年 2卷

作者： Daneshvar, Mohammadreza Mohammadi-Ivatloo, Behnam Zare, Kazem Amjad Anvari-Moghaddam Smart Energy Systems Laboratory Faculty of Electrical and Computer Engineering University of Tabriz 29 Bahman Blvd Tabriz Iran Department of Energy Aalborg University Denmark

Given the unsuitability of the previous strategies in procuring affordable conditions for the proactive distribution company (PDISCO) participation in the power market interactions, this article benefits transactive energy advantages to manipulate an innovative model for addressing this challenge. The proposed model enables the PDISCO to undertake optimal energy exchanges for maximizing its profit while creating equality between power supply and demand in the renewable-based system. A robust/stochastic hybrid technique is developed considering the uneven changes’ pattern to properly model uncertainties in the studied system. In this process, probabilistic scrutinizing of whole elements of the sample space is done by applying the Latin Hyperbolic Sampling method, while the selection process of the elements with a high existence probability is completed using the fast forward selection method. Moreover, the system robustness is intended by exerting robust optimization. The demand response program is advanced by using the elasticity properties of the shiftable loads. The modified version of the IEEE 33-bus test system is intended for verifying the effectiveness of the developed model. The results demonstrated a 23.197% diminution in the profit while procuring an acceptable degree of robustness for the system as well as guaranteeing the reach of a certain profit by operating the PDISCO under the suggested model rather than the base one. © 2022

关键词： Demand response program Hybrid stochastic and robust approach Optimal operation of the PDISCO Renewable energy sources Transactive energy Uncertainty modeling

Ambient Infrared Solar Energy Harvesting Using Log-Periodic Optical Rectenna at 28.3 THz

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Ambient Infrared Solar Energy Harvesting Using Log-Periodic ...

Mediterrannean Microwave Symposium (MMS)

作者： Ali Yahyaoui Ahmad Elsharabasy Fahad Al-Qahtani Mohammed Aseeri Hatem Rmili Taoufik Aguili Communications Systems Laboratory (SysCom) University of Tunis El Manar (UTM) National Engineering School of Tunis (ENIT) Tunis Tunisia Electrical and Computer Engineering Department McMaster University Hamilton ON Canada Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Next Generation Connectivity and Wireless Sensors Institute King Abdulaziz City of Science and Technology (KACST) Riyadh Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

This work presents the conception and design of log-periodic nano-antenna for the infrared IR solar energy harvesting applications. The suggested nano-antenna contain three layers. The first layer contains two arms of the log-periodic nano-antenna made of gold (Au), the second is a quartz substrate and finally, the ground layer is a gold (Au) plane reflector. The analysis of the proposed nano-antenna is performed using FDTD technique in the range of frequencies [28 to 29 THz]. A huge parametric study was performed to optimize the different parameters (thickness of the arms, teeth angle, number of teeth, substrate thickness, solid angles, and inner radius) of the nano-antenna. With the optimized design parameters, the structure captures a maximum electric field E at the hotspot point of around 800 V/m. The optimized geometry and shape of the presented structure make it a suitable choice to design a metal-insulator-metal (MIM) rectifier system in order to harvest solar infrared IR energy with good performance in terms of E-field, current-voltage I/V, resistance, and responsivity.

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Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

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Journal of Modern Power systems and Clean Energy 2020年第5期8卷 906-918页

作者： Jaber Valinejad Mousa Marzband Mert Korkali Yijun Xu Ameena Saad Al-Sumaiti IEEE Bradley Department of Electrical and Computer Engineering Virginia TechNorthern Virginia CenterGreater Washington D.C.VA 22043USA Department of Computer Science Virginia TechNorthern Virginia CenterGreater Washington D.C.VA 22043USA Department of Mathematics Physicsand Electrical EngineeringNorthumbria UniversityNewcastleUK Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21589Saudi Arabia Computational Engineering Division Lawrence Livermore National LaboratoryLivermoreCA 94550USA Advanced Power and Energy Center Electrical Engineering and Computer ScienceKhalifa UniversityAbu DhabiP.O.Box 127788UAE

Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other ***,there is the need for a smart structure to handle the new *** paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other *** upper-level problem is decomposed into a set of low-level market clearing *** electricity and heat markets are simultaneously modeled in this ***,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity *** addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat *** show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.

关键词： Microgrids distributed energy resource(DER) electricity market heat market demand response(DR) coalition formation energy storage

A Lightweight Ultrasound Image Denoiser Using parallel Attention Modules and Capsule Generative Adversarial Network

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Informatics in Medicine Unlocked 2024年 50卷

作者： Sivaanpu, Anparasy Punithakumar, Kumaradevan Thanikasalam, Kokul Noga, Michelle Zheng, Rui Ta, Dean Lou, Edmond H.M. Le, Lawrence H. Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Alberta Canada Department of Computer Science University of Jaffna Jaffna Sri Lanka School of Information Science and Technology ShanghaiTech University Shanghai 201210 China Department of Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China

The quality of ultrasound (US) imaging has been constrained by its limited contrast and resolution, inherent speckle noise, and the presence of other artifacts. Existing traditional and deep learning-based US denoising approaches have many limitations, such as reliance on manual parameter configurations, poor performance for unknown noise levels, the requirement for a large number of training data, and high computational expense. To address these challenges, we propose a novel Generative Adversarial Network (GAN) based denoiser. Capsule networks are utilized in both the generator and discriminator of the proposed GAN to capture intricate sparse features with less complexity. In addition, bias components are removed from all neurons of the generator to handle the unknown noise levels. A parallel attention module is also included in the proposed model to further enhance denoising performance. The proposed approach is trained in a semi-supervised manner and can thus be trained with fewer labeled images. Experimental evaluation on publicly available HC18 and BUSI datasets showed that the proposed approach achieved state-of-the-art denoising performance, with PSNR values of 33.86 and 34.16, and SSIM indices of 0.91 and 0.90, respectively. Moreover, experiments showed that the proposed approach is lightweight and more than twice as fast as similar denoisers. © 2024

关键词： Capsule Network Generative Adversarial Network Image denoising Semi-supervision Ultrasound image

Discrete-Time Uncertain Event-Triggered H∞ Control for Slow Sampling Markov Jump systems With Strictly Dissipativity

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Mathematical Methods in the Applied Sciences 2025年

作者： Kchaou, Mourad Syed Ali, M. Vigneshwar, B. Sanober, Sumaya Ibrahim, Tarek F. D Alanazi, Faizah Electrical Department College of Engineering University of Hail Kingdom Saudi Arabia Complex Systems and Networked Science Research Laboratory Department of Mathematics Thiruvalluvar University Tamil Nadu Vellore India Department of Computer Science Old Dominion University NorfolkVA United States Department of Mathematics Sciences College King Khalid University Abha Saudi Arabia Department of Mathematics Faculty of Sciences Mansoura University Mansoura Egypt Department of Mathematics College of Science Northern Border University Arar Saudi Arabia

This paper investigates the (Formula presented.) control problem for discrete-time uncertain slow sampling Markov jump systems under the event-triggered scheme. Discrete-time Markov jumps are controlled using an event-triggered scheme. An event-based strategy is addressed to reduce the data communication and improve the control efficiency. By applying a switched Lyapunov functional and linear matrix inequalities (LMIs), we derive sufficient conditions for (Formula presented.) -dissipativity of the resulting system. An event-triggered scheme is introduced into LMIs approaches in order to solve the dissipative control problem. The control strategy is implemented by combining the disturbance output with state feedback control law in order to achieve asymptotically stable closed-loop systems. Convex combination approach is utilized to handle slow sampling input, while parameter uncertainty and external disturbances are both included, and the robustness (Formula presented.) performance is analyzed. The developed methods are illustrated with two examples that illustrate their flexibility and applicability. © 2025 John Wiley & Sons Ltd.

关键词： Markov processes

Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs

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arXiv 2022年

作者： Sasahara, Hampei Dán, György Amin, Saurabh Sandberg, Henrik The Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Kth Royal Institute of Technology StockholmSE-100 44 Sweden The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology StockholmSE-100 44 Sweden

This paper introduces a "green" routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement;2) shared nature of charging facilities;3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can dramatically reduce inefficiency of equilibria. © 2022, CC BY-NC-ND.

关键词： Economic and social effects