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Completely Stealthy FDI Attack Against state Estimation in Networked control systems

IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 2023年第3期70卷 1114-1118页

作者： Liu, Yifa Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518055 Peoples R China

An effective false data injection attack should cause enough damage to the system while avoiding being spotted by detection methods. This brief proposes an extremely stealthy attack strategy, which can make the residual almost always unchanged to invalidate both the widely used chi(2) detector and the advanced summation detector and can eventually cause unbound state deviations. Compared with the existing attack methods, the proposed attack strategy can reduce the sum of cumulative residual increments by 85.39% while causing 9817001 times of the state deviations in the numerical simulations.

关键词： Detectors Sensors Networked control systems Symbols Observers Mathematical models Estimation error False data injection attack estimation complete stealthiness detection networked control systems

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MRO Configuration management for complex Products

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Journal of systems Science and systems Engineering 2022年第3期31卷 359-380页

作者： Chunliu Zhou Lian Qi Xiaobing Liu Hongguang Bo Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of TechnologyMaanshan 243002China School of Management Science and Engineering Anhui University of TechnologyMaanshan 243032China Rutgers Business School RutgersThe State University of New JerseyPiscatawayNJ 08854United State School of Economics and Management Dalian University of TechnologyDalian 116023China

Configuration management(CM)is an effective method for data modeling and change management in product development,but the study of how it can be adapted for complex product maintenance,repair and overhaul(MRO)has thus far been *** propositions are put forward on the basis of a literature review and verified by a case of a high-speed train MRO *** findings show that CM can be used in MRO data management with adjustments in its ***,MRO-CM planning considers both multistage service and single-stage service to keep data consistent and ***,MRO data integration with the design/production phase should be considered during configuration ***,baselines and change rules are developed for change ***,status accounting and auditing can be repeatedly used in MRO data management.A systematic MRO-CM framework is generated to assist MRO practitioners in understanding the relationships among different MRO-CM *** paper contributes to expanding the theory of CM in MRO for complex products and provides an effective method for managing MRO data from systematic engineering insights.

关键词： Configuration management MRO Service data management case study

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Event-triggered near-optimal tracking control based on adaptive dynamic programming for discrete-time systems

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NEUROCOMPUTING 2023年第1期537卷 187-197页

作者： Wang, Ziyang Lee, Joonhyup Wei, Qinglai Zhang, Anting Xilingol Vocat Coll Dept Mech & Elect Engn Xilinhot 026000 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Frequent state monitoring and controller updates can enhance the precision of tracking control, while simultaneously overburdening the communication network transmission. In this paper, For the purpose of saving communication costs, we propose event-triggered control algorithms for the optimal tracking control problem. First, we reconstruct the discrete-time nonlinear system into a converted system. Then, the adaptive dynamic programming algorithm is employed to find the optimal controller off-line, and the event-triggered scheme is used to reduce the communication costs online. Novel triggering conditions with fewer assumptions are designed to implement the event-triggered scheme. Different from existing works, the event-triggered scheme can be introduced not only into the converted system but also into the actual system, which is more practical because the actual controller is what one can only access in practice. In addition, with the developed algorithms, the tracking error can be proved to be stable at the origin, in other words, the actual system can be guaranteed to track the desired trajectory. Algorithms developed in this paper are implemented by three neural networks, the model network, the action network and the critic network. Finally, examples are presented to verify the effectiveness and rationality of the algorithms.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Optimal control Adaptive dynamic programming Event-triggered control Neural network

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Soft Contrastive Learning With Q-Irrelevance Abstraction for Reinforcement Learning

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2023年第3期15卷 1463-1473页

作者： Liu, Minsong Li, Luntong Hao, Shuai Zhu, Yuanheng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beihang Univ Sch Software Beijing 100191 Peoples R China

The difference between training and testing environments is a huge challenge to generalizing reinforcement learning (RL) algorithms. We propose a soft contrastive learning with a coarser approximate Q-irrelevance abstraction for RL (SCQRL) to increase RL generalization. Specifically, we specify the coarser approximate Q-irrelevance abstraction as the feature of the state with a theoretical analysis for better generalization ability. We construct a positive and negative sample selection mechanism based on the Q value for contrastive learning to achieve efficient representation learning. Considering the selection error of positive and negative samples, we design soft contrastive learning and combine it with RL in the form of an auxiliary task to propose SCQRL. The generalization experiments on several Procgen environments demonstrate that SCQRL outperforms the excellent generalized RL algorithm.

关键词： Contrastive learning generalization reinforcement learning (RL) representation learning

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Performance Improvement of a High-Speed Swimming Robot for Fish-Like Leaping

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IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第2期7卷 1936-1943页

作者： Chen, Di Wu, Zhengxing Zhang, Pengfei Tan, Min Yu, Junzhi Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peking Univ Coll Engn BIC ESAT State Key Lab Turbulence & Complex SystDept Adv Beijing 100871 Peoples R China

Many aquatic animals are able to leap out of water effortlessly, however, it is still exceedingly challenging for a swimming robot. Inspired by the fast-swimming mechanism of fish, in this letter, we develop an untethered high-speed swimming robot with the integration of high-frequency oscillation and compliant passive components for performance improvements in swimming speed, efficiency and even fish-like leaping motion. The stiffness optimization of compliant components is conducted via experiments. As a result, both swimming speed and efficiency have been significantly improved and the robot surprisingly reaches a swimming speed of 1.88 m/s corresponding to 7.1 body lengths per second (BL/s). That is, the compliant components' stiffness is of great importance to the performance improvement of a swimming robot at high-frequency oscillation. Additionally, the leaping motion is successfully performed with the optimized swimming robot and the maximum height of center of mass (CM) can reach up to 0.23 m. A simplified model is developed to analyze the leaping height with different speeds and body lengths, which suggests that the robot with small size leaps out of water more easily. The obtained results in this letter will offer some significant insights into the design and optimization for a high-speed swimming robot, as well as the cross-domain motions between water and air.

关键词： Biologically-inspired robots mechanism design compliant joints and mechanisms high-speed swimming fish-like leaping motion

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1885-1889页

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C. L. Philip South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Dept New Network Shenzhen 518000 Peoples R China China Tobacco Guangdong Ind Corp Guangzhou 510385 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks, and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenSwap, a data augmentation technique designed explicitly for semi-supervised semantic segmentation with Vision Transformers. TokenSwap aligns well with the global attention mechanism by mixing images at the token level, enhancing the learning capability for contextual information among image patches and the utilization of unlabeled data. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations.

关键词： Training Decoding Computational modeling Data models Semantic segmentation Computer vision Transformers Predictive models Optimization Image augmentation Dual-branch framework semi-supervised segmentation token swap vision transformer

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Software-Defined Active LiDARs for Autonomous Driving: A Parallel Intelligence-Based Adaptive Model

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第8期8卷 4047-4056页

作者： Liu, Yuhang Sun, Boyi Tian, Yonglin Wang, Xingxia Zhu, Yin Huai, Rouxing Shen, Yu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Chinese Acad Sci Dept Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China North Automat Control Technol Inst Taiyuan 030006 Peoples R China Beijing Huairou Acad Parallel Sensing Beijing 101407 Peoples R China

LiDAR is an indispensable sensor for autonomous driving that can provide precise 3D information about the environment. Among various types of LiDARs, mechanical LiDARs are the most commonly used on vehicles that uniformly perceive the scene utilizing rotating motors. However, accurate perception of foreground objects is considered as the most important task in automotive LiDARs, therefore the current operating mode of mechanical LiDARs wastes a significant amount of sensing resources on the useless background. Besides, the development of LiDAR hardware and software systems is currently split into two independent segments, lacking real-time interaction between physical entities and digital models in cyberspace. To address these issues, we propose software-defined active LiDARs for autonomous driving based on parallel intelligence. Active LiDARs redefine LiDAR's hardware operation through software systems to achieve adaptive sensing resource allocation, constituting a closed loop between physical space and cyberspace. During the working process, it calculates scenario heatmaps with the constructed high-definition maps (HD maps) in cyberspace at first. Then it takes prescriptive control of physical LiDARs based on heatmaps to improve sensing resource utilization. We build two adaptive LiDAR models in CARLA and construct a hardware prototype in the parallel sensing platform, DAWN. Active Point Cloud (APC), a new dataset collected in CARLA, is proposed and a 3D object detection task is selected to demonstrate the effectiveness of active LiDARs. Our experimental results show that active LiDARs can both improve raw data quality and model performance compared with mechanical LiDARs, especially for the perception of distant objects.

关键词： Software-defined active LiDARs parallel intelligence adaptive sensing resources allocation

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Collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution 19th

Collaborative Decision-Making Processes Analysis of Service ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Yan, Xiangpei Xue, Xiao Peng, Chao Liu, Donghua Feng, Zhiyong Xiao, Wang School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China China Waterborne Transport Research Institute Tianjin China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031545306

With the collaboration of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. As a novel service organization model, the Service Ecosystem (SE) can provide more sophisticated, precise, and thorough services and has attracted widespread attention. However, it also brings negative effects such as involution, and information cocoon room. Thus, how to analyze the collaborative decision-making mechanism between the SE regulation algorithm and the crowd intelligence group, exploring the reasons behind the negative effects, and finding effective intervention strategies have become problems in this field. To solve the challenges, we propose a Computational Experiments-based method Decision-making processes Analysis model in SE, namely CEDA. The proposed CEDA model consists of three modules: the autonomous evolution mechanism module, the learning evolution mechanism module, and the collaborative decision-making analysis module. Among them, the computational experiments can provide a customized test environment for the analysis of collaborative decision-making processes and find out the appropriate intervention strategy. Finally, the validity of the CEDA model is verified through the case of academic ecosystem involution. The results show that computational experiments can provide new ideas and paths for collaborative decision-making processes analysis. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Ecosystems

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Civilian Autonomous Carriers for Peaceful Services: Intelligent Vehicle Carries Enabled Future Transportation and Logistics

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-5页

作者： Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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