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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 857-861页

作者： Zhao, Xiaoxuan Han, Yingqi Lewlomphaisarl, Udom Wang, Haoyu Hua, Jing Wang, Xiujuan Kang, Mengzhen 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 101408 Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Natl Elect & Comp Technol Ctr Pathum Thani 12120 Thailand Qingdao AgriTech Co Ltd Qingdao 266000 Peoples R China Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China

Highly intelligent greenhouse without human intervention is the goal of autonomous greenhouse control. In this paper, a parallel control framework for greenhouse climate is proposed which aims to minimize the need for monitored data and expert knowledge. GreenLight climate model is used as a knowledge-based model that produces simulated data. LSTM with control units is pre-trained with these data. Test on necessary data size is done by transferring the model to other greenhouses. The new transferred model has a good improvement in the prediction of indoor temperature, humidity and CO2 concentration with approximate 0.05, 0.05 and 0.1 of R-2, respectively, which shows the feasibility of the transferable prediction model.

关键词： Transfer learning long short-term memory greenhouse modeling parallel control

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Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

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IEEE ACCESS 2020年 8卷 5777-5786页

作者： Zhang, Shunchao Wang, Yonghua Wan, Pin Zhuang, Jiawei Zhang, Yongwei Li, Yi Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Peoples R China

In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.

关键词： Cognitive radio robust cooperative spectrum sensing sensing data fusion K-medoids clustering algorithm Mean-shift clustering algorithm

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Novel PIO Algorithm with Multiple Selection Strategies for Many-Objective Optimization Problems

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complex System Modeling and Simulation 2021年第4期1卷 291-307页

作者： Zhihua Cui Lihong Zhao Youqian Zeng Yeqing Ren Wensheng Zhang Xiao-Zhi Gao School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Computing University of Eastern FinlandKuopioFI-70211Finland

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial ***,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization *** selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto ***,the accuracy,convergence,and diversity of solutions are *** is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization *** results indicate the superiority of ImMAPIO on these test functions.

关键词： pigeon-inspired optimization algorithm many-objective optimization problem multiple selection strategy elite individual retention

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An AdaBoost Ensemble Model for Fault Detection and Classification in Photovoltaic Arrays

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 794-800页

作者： Lodhi, Ehtisham Wang, Fei-Yue Xiong, Gang Dilawar, Adil Tamir, Tariku Sinshaw Ali, Hub Chinese Acad Sci Inst Automat SKL Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The photovoltaic (PV) arrays are susceptible to numerous faults. Fault diagnosis is essential in improving a PV system's output power, reliability, and life span. This research paper suggests an AdaBoost Ensemble Model (AEM) approach for detecting and classifying PV system faults. The AEM approach includes several weak base learners stacked sequentially so that they learn from the mistakes of prior weak learners to produce an improved predictive model. This study considers open-circuit fault (OCF), short-circuit fault (SCF), and degradation fault (DF). A complete quantitative evaluation of the suggested AEM approach is compared to earlier machine learning classification techniques to diagnose faults in PV arrays. The results of the proposed AEM approach are superior to those of the traditional methods, with an accuracy of 97.84 percent in fault detection. The findings indicate that the AEM approach improves classification performance while preserving a powerful generalization capability for PV system fault diagnostics. Consequently, the proposed AEM approach is more effective at detecting and classifying faults in PV array systems.

关键词： Photovoltaic (PV) AdaBoost ensemble model (AEM) open-circuit fault (OCF) short-circuit fault (SCF) degradation fault (DF) machine learning fault classification fault detection

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Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2020年第4期21卷 1624-1630页

作者： Chen, Yuanyuan Lv, Yisheng Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Shandong 266109 Peoples R China

Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.

关键词： Generators Data models Gallium nitride Generative adversarial networks Training Loss measurement Biological system modeling Parallel data generative adversarial networks traffic flow imputation data augmentation deep learning

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Mechanical Design Paradigm based on ACP Method in Parallel Manufacturing

Mechanical Design Paradigm based on ACP Method in Parallel M...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Shimeng Li Yutong Wang Xiao Wang Feiyue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China

Parallel Manufacturing is a new manufacturing paradigm in industry, deeply integrating informalization, automation, and artificial intelligence. In this paper we propose a new mechanical design paradigm in Parallel Manufacturing based on ACP method. The key is to regard the design procedure based on artificial design and emulation method as two independent procedures, which can be modeled as a parallel system. The design procedure based on ACP method does not include a real system, which is an inventive extension of the traditional parallel system. This method can be implemented with social information by introducing the definition of SDV, SDM, and intelligent Design Manager, making it highly adaptive for social manufacturing and Parallel Manufacturing.

关键词： Industries Adaptation models Automation Digital twin Design methodology Conferences Emulation

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Limit-Cycle-Based Design of Formation control for Mobile Agents

Limit-Cycle-Based Design of Formation Control for Mobile Age...

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作者： Wang, Chen Xia, Weiguo Xie, Guangming National Engineering Research Center for Software Engineering Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian116024 China State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this article, we study the formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a static/moving target. The prescribed distribution pattern is a class of general formations where the distances between neighboring agents or the distances between each agent and the target do not need to be equal. Each agent is modeled as a double integrator and can merely perceive the relative information of the target and its neighbors, and the acceleration of the target. In order to solve the formation problem, a limit-cycle-based controller design is delivered. We divide the overall control objective into two subobjectives, where the first is target circling that each agent keeps its own desired distance to the static/moving target as well as rotating around or remaining static relative to the target as expected, and the second is distribution adjustment that each agent maintains the desired distance to its neighbors. Then, we propose a controller comprised of two parts, where a limit cycle oscillator named a converging part is designed to deal with the first subobjective, while a layout part is introduced to address the second subobjective. One key merit of the controller is that it can be implemented by each agent in its local frame so that only local information is utilized without knowing global information. Theoretical analysis of the convergence to the desired formation, of which the agents are required to be evenly distributed on a circle around the target, is provided for the multiagent system under the proposed controller. Numerical simulations are given to validate the effectiveness of the proposed controller for the cases of general formations, and to show that no collision between agents ever takes place throughout the system's evolution. © 1963-2012 IEEE.

关键词： Multi agent systems

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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and “On-Demand” Smart Services

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IFAC-PapersOnLine 2024年第3期58卷 139-144页

作者： Xiao Wang Jun Huang Jing Yang Xiaoxing Wang Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China School of artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Beijing SANBODY Technology Company Ltd Beijing 214000 China Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of “on-demand” smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

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Double deep Q-learning for anti-saturation attack problem of warship group 1

Double deep Q-learning for anti-saturation attack problem of...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Li, Wei Xiong, Gang Tao, Hao Han, Yunjun Wang, Fang Dong, Xisong Shen, Zhen Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan430064 China Shandong University Jinan China

ISBN: (纸本)9781665433372

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios. © 2021 IEEE.

关键词： Reinforcement learning

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Learning Markets: An AI Collab.ration Framework Based on Blockchain and Smart Contracts

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IEEE INTERNET OF THINGS JOURNAL 2022年第16期9卷 14273-14286页

作者： Ouyang, Liwei Yuan, Yong Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Minist Educ Engn Res Ctr Finance Computat & Digital Engn Beijing 100872 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Artificial intelligence (AI) has been witnessed to provide valuable solutions to all walks of life. However, data island and computing resources limitations in the centralized AI architectures have increased their technical barriers, and thus distributed AI collab.ration in data, models, and resources has attracted intensive research interests. Since the existing trust-based collab.ration models are no longer applicable for the large-scale distributed collab.ration among trustless machines in open and dynamic environments, this article proposes a novel decentralized AI collab.ration framework, i.e., learning markets (LM), in which blockchain provides a trustless environment for collab.ration and transaction, while smart contracts serve as software-defined agents to encapsulate and process scalab.e collab.ration relationships and market mechanisms. LM can not only help those participants without mutual trust realize collab.rative mining with dynamic and quantitative rewards but also build an AI market with natural auditability and traceability for trading trusted and verified models. We implement and comprehensively analyze LM based on the Ethereum interplenary file system platform (IPFS), and the results prove that it has advantages in collab.ration fairness, transparency, security, decentralization and universality. Based on our collab.ration framework, distributed AI contributors are expected to cooperate and complete those learning tasks that cannot be done previously due to lack of complete data, sufficient computing resources and state-of-the-art models.

关键词： Collab.ration Artificial intelligence Data models Computational modeling Smart contracts Blockchain Distributed databases Artificial intelligence (AI) collab.ration blockchain ensemble learning federated learning (FL) smart contracts

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