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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

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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Cloud-assisted nonlinear model predictive control for finite-duration tasks

arXiv

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arXiv 2021年

作者： Li, Nan Zhang, Kaixiang Li, Zhaojian Srivastava, Vaibhav Yin, Xiang The Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States The Department of Mechanical Engineering Michigan State University East LansingMI48824 United States The Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States The Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback. Unlike traditional cloud-based control that treats the cloud as powerful, remote, and sole controller in a networked-system control setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks by explicitly considering model mismatches and errors due to request-response communication delays. We analyze stability-like properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-like conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

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Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Varying Nodes

Distributed State Estimation for Heterogeneous Mobile Sensor...

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第三十九届中国控制会议

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

On background of cooperative air combat, state estimation of aerial maneuvering target with multi-sensor cooperative reconnaissance is discussed in this paper. Multiple moving platforms equipped with different kinds of detectors construct heterogeneous mobile sensor networks. Graph theory is adopted to describe the communication topology of the networks. In consideration of detection range limit, the number of sensors participating in the detection changes over time. An interactive multiple model algorithm（IMM） is utilized for target motion modeling. The local observation information is processed by Cubature Information Filter（CIF）. A weighted average consensus-based state estimation method for heterogeneous mobile sensor networks with varying nodes is put forward to fuse information from different sensors and obtain a consistent and precise tracking result. Moreover, the stability analysis of proposed method is provided detailed. Finally, numerical simulation results are given to illustrate the effectiveness of the theoretical results.

关键词： Cubature Information Filter consensus theory distributed state estimation mobile sensor networks stochastic boundedness

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An Adaptive RBF Neural Network control Method for a Class of Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 457-462页

作者： Hongjun Yang Jinkun Liu State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Automation Science and Electrical Engineering Beihang University Beijing 100083 China

This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations *** uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the *** show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

关键词： Index TermsbAdaptive control neural network （NN） nonlin-ear system radial basis function.

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Corrigendum to ‘Distributional two-level synergetic unit commitment considering three scheduling states’

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Electric Power systems Research 2023年 216卷

作者： Yumin Zhang Xuan Zhang Xingquan Ji Ming Yang Xueshan Han Ziyang Yin College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao 266590 China Key Laboratory of Smart Grid of Ministry of Education Tianjin University 300072 Tianjin China Key Laboratory of Power System Intelligent Dispatch and Control (Shandong University) Ministry of Education Jinan 250061 China

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The Heterogeneous Deployment Tool for Hardware and Software Co-design

The Heterogeneous Deployment Tool for Hardware and Software ...

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International Conference on Computer, Information and Telecommunication systems (CITS)

作者： Bingjie Zhao Ziyang Li Tao Zhang Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Acsdemy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

In order to solve the shortcomings of manually writing code that cannot meet the requirements of rapid development and product verification, we designed a heterogeneous deployment tool based on Simulink model development. This heterogeneous deployment tool can deploy the algorithm designed by Simulink model to the CPU and FPGA platforms and can communicate between CPU and FPGA via PCI Express. Users do not need to concern the underlying hardware and drivers and but only need to build an algorithm, they can quickly deploy the algorithm on a heterogeneous hardware platform to verify the performance of the algorithm.

关键词： Field programmable gate arrays Tools Mathematical model Software packages Hardware design languages Matlab Load modeling

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Development of power drive technology for high lift system of commercial aircraft

Development of power drive technology for high lift system o...

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CSAA/IET International Conference on Aircraft Utility systems 2020, AUS 2020

作者： Liu, Tong Zhang, Yue Wang, Weida Ju, Jiachen Yu, Wenbin Department of Flight Control System Shanghai Aircraft Design and Research Institute 5188 Jinke Rd. Shanghai201210 China Department of Science and Technology Management Commercial Aircraft Corporation of China Ltd. 1919 Shibo Ave. Shanghai200126 China Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiaotong University 800 Dongchuan Rd. Shanghai200240 China

ISBN: (纸本)9781839535635

In this paper, the development of power driving technology for high lift system of modern commercial aircraft is presented. Three kinds of power driving technical schemes of high lift system are demonstrated together with their characteristics. Various applications of power drive technology on present mainstream aircrafts have been addressed and analyzed respectively. In addition to the analysis of existing power drive technology, this work presented that distributed electric power drive technology could be a potential future development trend. The paper can be read as a reference for selecting power driving technical scheme of high lift systems at conceptual design phase of a commercial aircraft. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Conceptual design

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Low-Cost INS/GNSS Integration Algorithm Aided by Multi-Constraints Applied to Vehicle Navigation

Low-Cost INS/GNSS Integration Algorithm Aided by Multi-Const...

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IEEE International Conference on control and Robotics engineering (ICCRE)

作者： Baoshuang Ge Hai Zhang Wenxing Fu Jieling Chen School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China China Ship Development and Design Center Wuhan China

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167923

The low-cost inertial navigation system/global navigation satellite system (INS/GNSS) integration has been widely used to vehicle navigation. However, the positioning performance often declines due to the weak satellite signals. This paper presents a new approach to improve the accuracy of the vehicle navigation. In contrast to the current methods, a new velocity constraint model is developed to aid the estimation of the navigation errors. The altitude accuracy of GNSS is challenged in comparison with its position in the horizonal plane. Thus, an auxiliary low-cost barometer is used to obtain the height as well. The experimental results demonstrate the benefit of the proposed multi-constraints aiding algorithm.

关键词： Global navigation satellite system Global Positioning system Kalman filters Receivers Sea measurements

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