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Hierarchical Game Approach for Optimization of Regional Integrated Energy System Clusters Considering Bounded Rationality

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CSEE Journal of power and Energy systems 2024年第1期10卷 302-313页

作者： Lei Dong Mengting Li Junjie Hu Sheng Chen Tao Zhang Xinying Wang Tianjiao Pu school of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China Department of artificial intelligence Application Research China Electric Power Research InstituteBeijing 100192China Key laboratory of Smart Grid of Ministry of Education Key Laboratory of Smart Energy&Information Technology of Tianjin MunicipalityTianjin UniversityTianjin 300072China

Regional integrated energy system(RIES)cluster,i.e.,multi-source integration and multi-region coordination,is an effective approach for increasing energy utilization *** hierarchical architecture and limited information sharing of RIES cluster make it difficult for traditional game theory to accurately describe their game ***,a hierarchical game approach considering bounded rationality is proposed in this paper to balance the interests of optimizing RIES cluster under privacy protection.A Stackelberg game with the cluster operator(CO)as the leader and multiple RIES as followers is developed to simultaneously optimize leader benefit and RIES utilization ***,a slight altruistic function is introduced to simulate the game behavior of each RIES agent on whether to cooperate or *** introducing an evolutionary game based on bounded rationality in the lower layer,the flaw of the assumption that participants are completely rational can be ***,for autonomous optimal dispatching,each RIES is treated as a prosumer,fexibly switching its market participation role to achieve cluster coordination *** studies on a RIES cluster verify effectiveness of the proposed approach.

关键词： Bounded rationality coordinated optimization evolutionary game regionaIilntegrated energysystem stackelberg game

Strategic Behavior Analysis and Modeling for Resilient Resource Allocation

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Journal of systems Science and Information 2024年

作者： Tong LIU Saike HE University of Chinese Academy of Sciences China People's Police University State Key laboratory of Multimodal artificial intelligence systems Institute of Automation Chines Academy of Sciences

In an era where power systems face increased cyber threats, social media data, especially public sentiment during outages, emerges as a crucial component for devising defense strategies. We present a methodology that integrates sentiment analysis of social media data with advanced reinforcement learning techniques to tackle uncertain load redistribution cyberattacks. This approach first employs VADER and Support Vector Machine(SVM) sentiment analysis on collected social media data, revealing insightful information about power outages and public sentiment. Proximal Policy Optimization(PPO), a state-of-the-art reinforcement learning method, is then applied in the second stage to leverage these insights, manage outage uncertainty, and optimize defense strategies. The efficacy of this methodology is demonstrated on a modified IEEE 6-bus system. The results underscore our approach’s effectiveness in utilizing social media data for a nuanced, targeted response to *** pioneering methodology offers a promising direction for enhancing power grid resilience against cyberattacks and natural disasters, highlighting the value of social media sentiment analysis in power systems security.

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Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing systems

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Phyton-International Journal of Experimental Botany 2024年第4期93卷 745-762页

作者： Jingcheng Zhang Xingjian Zhou Dong Shen Qimeng Yu Lin Yuan Yingying Dong College of artificial intelligence Hangzhou Dianzi UniversityHangzhou310018China School of Information Engineering Zhejiang University of Water Resources and Electric PowerHangzhou310018China Key laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing100094China

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae ***), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was *** capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple ***, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

关键词： Rice bacterial leaf blight analysis of spectral response multispectral data simulation vegetation indices cross-sensor disease monitoring

Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything Communications

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China Communications 2024年第1期21卷 242-259页

作者： Hao Jiang Hongming Zhang Ting Liu College of artificial intelligence Nanjing University of Information Science and Technology210044China National Mobile Communications Research laboratory Southeast UniversityNanjing 210096China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsChina

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)*** the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless *** statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and *** results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and *** proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

关键词： complex CIRs LoS and NLoS propagation components MIMO V2X communication environments SoS simulation channel model statistical properties

From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 1-14页

作者： Qiqi He Li Li Dai Li Tao Peng Xiangying Zhang Yincheng Cai Xujun Zhang Renzhong Tang State Key laboratory of Fluid power Components and Mechatronic systems Zhejiang UniversityHangzhou 310027China ZJU-Sunon joint Research Center for Smart Furniture Zhejiang UniversityHangzhou 310027China PICO-Lab Bytedance Inc.Shanghai 200233China Sunon Technology Co. Ltd.Hangzhou 311215China

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and *** human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic ***,it has limitations in real-time data usage,personalized services,and timely *** emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and ***,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors *** framework comprises the physical twin,the virtual twin,and the linkage between these *** virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled *** can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional ***,the future perspectives of HDT for industrial applications as well as technical and social challenges are *** general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

关键词： Human digital twin Digital human modeling Human factors Human-centric technology

Entanglement quantification via weak measurements assisted by deep learning

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Photonics Research 2024年第4期12卷 712-719页

作者： MU YANG YA XIAO ZE-YAN HAO YU-WEI LIAO JIA-HE CAO KAI SUN EN-HUI WANG ZHENG-HAO LIU YUTAKA SHIKANO JIN-SHI XU CHUAN-FENG LI GUANG-CAN GUO CAS Key laboratory of Quantum Information University of Science and Technology of ChinaHefei 230026China College of Physics and Optoelectronic Engineering Ocean University of ChinaQingdao 266100China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Hefei National laboratory University of Science and Technology of ChinaHefei 230088China Electric power Research Institute State Grid Anhui Electric Power Co.Ltd.Hefei 230601China Institute of systems and Information Engineering University of TsukubaIbaraki 305-8573Japan Center for artificial intelligence Research University of TsukubaIbaraki 305-8577Japan Graduate School of Science and Technology Gunma UniversityGunma 371-8510Japan Institute for Quantum Studies Chapman UniversityOrangeCalifornia 92866USA JST PRESTO Saitama 332-0012Japan

Entanglement has been recognized as being crucial when implementing various quantum information ***, quantifying entanglement for an unknown quantum state requires nonphysical operations or post-processing measurement data. For example, evaluation methods via quantum state tomography require vast amounts of measurement data and likely estimation.

关键词： estimation quantum angle

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

The study of lithographic variation in resistive random access memory

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Journal of Semiconductors 2024年第5期45卷 69-79页

作者： Yuhang Zhang Guanghui He Feng Zhang Yongfu Li Guoxing Wang Department of Micro-Nano Electronics Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Lab of artificial intelligence Shanghai Jiao Tong UniversityShanghai 200240China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China

Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM *** this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically *** results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography *** read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(power and area *** such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.

关键词： layout lithography process variation resistive random access memory

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area power systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the artificial intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems