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Annual Meeting Industrial and Commercial Power systems Technical Conference

作者： Yixiao Yu Xueshan Han Ming Yang Yumin Zhang Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728171951

ISBN: (纸本)9781728171968

Different from individual wind power forecast (IWPF), regional wind power forecast (RWPF), one of the most significative researches for alleviating negative influence on power systems, aims to estimate the output of multiple wind farms in the specific region, which is a valuable complement of the wind power forecast. This paper proposes a nonparametric probabilistic method for regional wind power forecast, a quantile regression neural network (QRNN), incorporating nonlinear mapping and massive data dealing abilities of the neural network (NN) and nonparametric probabilistic forecast ability of quantile regression (QR). On this basis the deep quantile regression (DQR) is proposed to improve the performance of the QRNN. In this approach, local connection is applied to the input layer of NN for tackling the challenge of the massive data. A ramp function is designed to avoid quantile plane crossing problem. To improve the model's generalization capability, a smoothing method is used to loss function for achieving differentiable everywhere. By properly constructing model, the approach provides a specific solution for regional wind power forecast with massive input information. Test results on a region with 10 wind farms demonstrate the effectiveness of the proposed approach.

关键词： Wind power generation Wind forecasting Wind farms Artificial neural networks Probabilistic logic Power systems

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Hexagonally ordered microbowl arrays decorated with ultrathin CuInS_(2) nanosheets for enhanced photoelectrochemical performance

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Journal of Energy Chemistry 2020年第12期29卷 134-142页

作者： Ming Li Le Chen Yanjie Su Huan Yin Kexiang Hu State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyKey Laboratory of Optoelectronic Information Processing of Guangxi Colleges and Uniy/ersitiesGuilin 541004GuangxiChina Key Laboratory for Thin Film and Microfabrication of the Ministry of Education Department of Micro/Nano ElectronicsSchool of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China National and Local Joint Engineering Laboratory of Internet Technology on Mine School of Information and Control EngineeringChina University of Mining and TechnologyXuzhou 221008JiangsuChina School of Physics and Telecommunication Engineering Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulin 537400GuangxiChina

This paper demonstrates the design and fabrication of three-dimensional(3 D) hexagonally ordered microbowl arrays(MBAs) decorated with Cu In S2 nanosheets for enhanced photoelectrochemical(PEC) performance. The 3 D MBAs are fabricated by a micro-fabrication technique. The ultrathin CuInS2 nanosheets are grown on the 3 D electrodes by solvothermal transformation of Cu film. The photocurrent density of 3 D photocathode(CuInS2@MBAs) is about two times higher than that of the planar counterpart(CuInS2@Planar). The improved PEC performance can be ascribed to the elevated light trapping ability and the increased surface area for loading photocatalysts. In addition, CdS quantum dots as cocatalysts are modified onto the Cu In S2 nanosheets to further enhance the PEC activity because the formed p-n heterojunction can accelerate the separation of photogenerated carriers. As a result, the 3 D photocathode of CuInS2/CdS@MBAs shows an optimal incident photon to current efficiency of 10% at the wavelength of400 nm. It is believed that this work can be generalized to design other hierarchical 3 D photoelectrodes for improved solar water splitting.

关键词： Microbowl array CuInS_(2) Nanosheet Photocathode Photoelectrochemical performance

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Communication and control Co-Design for Heterogeneous Industrial IoT: A Logic-Based Stochastic Switched system Approach

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wang, Shuling Zhu, Shanying Chen, Cailian Shen, Fei Zhang, Weihai Guan, Xinping Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Chinese Academy of Sciences Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Shanghai200050 China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao266590 China

With the development of Industry 4.0, mobile agents are deployed to coordinate with multi-loop control systems to perform manufacturing tasks, leading to heterogeneous Internet of Things (IoT). Due to shadow fading induced by the movement of mobile agents, the wireless channel closing the control loops is inherently unreliable, which can compromise the control system performance. This paper addresses the co-design problem of transmission scheduling and agents' movement to ensure both control performance and energy efficiency. A generalized cyber-physical-agent framework is proposed to capture the coupling between IoT systems and a mobile agent through a state-dependent fading channel. Moreover, the movement of the mobile agent among areas exhibiting different levels of shadow effects is modeled by Markov decision process. To address such heterogeneous dynamics, the co-design problem is formulated into the optimization of a logic-based stochastic switched system by utilizing the semi-tensor product technique. Based on state mergence and H-representation methods, a tractable and effective algorithm is then proposed to design co-design policies, minimizing the average joint cost of communication and control. A parallel scheme is further developed to alleviate the computation burden of the algorithm. Theoretical guarantees are provided to ensure control system performance. Finally, simulation results are given to demonstrate effectiveness of the proposed method. © 1983-2012 IEEE.

关键词： Fading channels

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Research on the Strategy of Energy Storage system Participating in the Secondary Frequency Regulation of Power Grid Based on Fuzzy control

Research on the Strategy of Energy Storage System Participat...

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第33届中国控制与决策会议

作者： Ya Zeng Yan Zhao Tieyan Zhang Yuri Wang Qianzhi Shao Yuanzhuo Du School of Electric Power Shenyang Institute of Engineering School of Renewable Energy Shenyang Institute of Engineering Key Laboratory of Regional Multi-energy System Integration and Control Liaoning Province School of Automation and Electrical Engineering Shenyang Ligong University Industrial Branch State Grid Liaoning Electric Power Supply Co.Ltd. Fengman Training Center State Grid Xinyuan Company

The continuous development of science and technology has led to great changes in the demand for loads in the power system,placing higher demands on the grid for frequency *** disadvantages of traditional FM generators such as slow response speed and dead zone in frequency modulation are becoming more and more ***,the energy storage system has timely tracking,reversible charging and discharging,and rapid *** auxiliary grid frequency modulation through the energy storage system helps to maintain the active power balance of the power system,improve and increase *** frequency adjustment capability of the system provides reliable support for the safe and efficient operation of the power *** paper first analyses the mechanism of secondary frequency regulation of energy storage assisted power grid and establishes an equivalent model of secondary frequency regulation of thermal power units assisted by energy storage *** addition,an energy storage system-assisted grid secondary frequency regulation control strategy based on double-layer fuzzy control is designed to address the shortcomings of the existing energy storage assisted grid frequency regulation control ***,the simulation analysis of the frequency regulation effect is carried out to verify the rationality and effectiveness of the proposed control strategy.

关键词： Fuzzy control Energy Storage system Secondary Frequency Modulation

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Knowledge-based prediction of network controllability robustness

arXiv

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arXiv 2020年

作者： Lou, Yang He, Yaodong Wang, Lin Tsang, Kim Fung Chen, Guanrong The Department of Electrical Engineering City University of Hong Kong Hong Kong The Department of Automation Shanghai Jiao Tong University China The Key Laboratory of System Control and Information Processing

Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the network controllability robustness is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor;2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity. © 2020, CC BY.

关键词： Complex networks

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Distributed cooperative guidance for multi-missile system with time-varying delays and switching topologies

Distributed cooperative guidance for multi-missile system wi...

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第三十九届中国控制会议

作者： Siquan Zhou Jie Ren Yong Heng Xiwang Dong Qingdong Li School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Aerospace System Engineering Research Institute Beijing Institute of Electronic System Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Distributed cooperative guidance problems for multi-missile system with time-varying delays and switching topologies are investigated. Firstly, considering the complex communication environment, a two-step cooperative guidance strategy is proposed to achieve the simultaneous attack under time-varying delays and switching topologies. In the first step, a distributed cooperative guidance law using neighboring information is designed to coordinate range-to-go and leading angle based on feedback linearization and consensus theory. The second step starts while multi missiles disconnect from each other and hit the target using PNG（Proportional Navigation Guidance）. Then, the algorithm with three steps is presented to select the parameters in the cooperative guidance law. And the stability of the multi-missile system is proven using Lyapunov-Krasovskii theory. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed cooperative guidance strategy.

关键词： Cooperative guidance Distributed strategy Consensus theory Time-varying delays Switching topologies

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Pinning boundary sampled-data synchronization of coupled reaction–diffusion neural networks

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Neurocomputing 2025年 648卷

作者： Zi-Peng Wang Bo-Ming Chen Junfei Qiao Biao Luo Huai-Ning Wu Tingwen Huang Guangwei Chen School of Information Science and Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System and Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing 100124 China School of Automation Central South University Changsha 410083 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518107 China

In this paper, the pinning boundary sampled-data (SD) synchronization problem of coupled reaction–diffusion neural networks (CRDNNs) with SD-based couplings is investigated under boundary and distributed SD measurements. First, a pinning boundary SD controller is designed to effectively address the synchronization problem of CRDNNs, utilizing boundary and distributed SD measurements and requiring only a small number of sensors and actuators. Then, linear matrix inequalities provide adequate conditions that ensure exponential stability of the synchronization error system by using the designed pinning boundary SD controller, derived through inequality techniques and Lyapunov functionals. Finally, we present a numerical example to showcase the practical applicability and effectiveness of the proposed pinning boundary SD synchronization method.

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PCB Defect Recognition and Elimination Based on Secondary Error and Statistical Histogram

PCB Defect Recognition and Elimination Based on Secondary Er...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Rui Li Bin Xue Kun Zhao Huihui Chu Benshuang Jiao School of Mechanical and Electrical Engineering Qingdao Binhai University Shandong PR China Qingdao Zhongzhuang Vision Automation System Control Co. Ltd. Shandong PR China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Aiming at the difficulty of missing, wrong and reverse detection in PCB component detection, the paper proposes a new PCB defect detection and elimination based on visual recognition technology and computer-aided system, it can find out target more efficiently by visual inspection technology in the non-stop line. The target region was marked through secondary error matching algorithm, the threshold segmentation graph of original images were investigated in the marked region. The recognition capability for missing and wrong detection was enhanced. The component matching compatibility was analyzed by mask operation and statistical histogram algorithm. The recognition capability for polarity detection was enhanced. The results were judged by computer aided evaluation system that was carried out under the environment of Vision Pro and Visual Studio. The dynamic position of defective component was calculated by PLC. The operation results showed that through the proposed method, the recognition rate was 100%, and the false alarm rate was about 3% in the detection of anti logic, color components, pins and capacitance, and meet the needs of real-time detection and elimination.

关键词： Image color analysis Histograms Image segmentation Pins Visualization Capacitors Capacitance

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