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automation 5.0: The Key to Systems Intelligence and Industry 5.0

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong wang Petros A.Ioannou lili Fan Xingxia wang Raffaele Carli Chen Lv lingxi li Xiaoxiang Na Qing-Long Han Fei-Yue wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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Convolution-Transformer for Image Feature Extraction

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 87-106页

作者： lirong Yin Lei wang Siyu Lu Ruiyang wang Youshuai Yang Bo Yang Shan liu Ahmed AlSanad Salman A.AlQahtani Zhengtong Yin Xiaolu li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

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Recent advances in artificial intelligence generated content

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 1-5页

作者： Junping ZHANG lingyun SUN Cong JIN Junbin GAO Xiaobing li Jiebo LUO Zhigeng PAN Ying TANG Jingdong wang School of Computer Science Fudan UniversityShanghai 200433China International Design Institute Zhejiang UniversityHangzhou 310058China School of Information and Communication Engineering Communication University of ChinaBeijing 100024China The university of Sydney Business School The University of SydneyNSW 2006Australia Department of AI Music and Music Information Technology Central Conservatory of MusicBeijing 100032China Department of Computer Science University of RochesterNY 14627USA School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China electrical and Computer Engineering Department Rowan UniversityGlassboroNJ 08028USA Baidu Beijing 100085China

Artificial intelligence generated content(AIGC)has been a research hotspot in the field of artificial intelligence in recent *** is expected to replace humans in performing some of the work of content generation at a low cost and a high volume,such as music,painting,multimodal content generation,news articles,summary reports,stock commentary summaries,and even content and digital people generated in the *** provides a new technical path for the development and implementation of AI in the future.

关键词： artificial replace content

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AI for science: Predicting infectious diseases

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Journal of Safety Science and Resilience 2024年第2期5卷 130-146页

作者： Alexis Pengfei Zhao Shuangqi li Zhidong Cao Paul Jen-Hwa Hu Jiaojiao wang Yue Xiang Da Xie Xi Lu Institute of automation Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBejing 100090China Systems Engineering Cornll UniversityIthacaNY 14853USA David Eccles School of Business the University of UtahUtahUSA Collge of electrical Engineering Sichuan UniversityChina Department of electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of electrical Engineering Southeast UniversityNanjing 210096China

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious *** epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease ***,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive *** is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease *** paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological *** harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced *** comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by *** essence,Al4S represents a paradigm shift in infectious disease *** addresses the limitations of traditional models and paves the way for a more proactive and informed response to future *** we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.

关键词： AI for science(AI4S) Data integration Global healthchallenges Infectious disease prediction Predictive modeling Real-timemonitoring

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IHMP:an improved hierarchical motion planner for mobile manipulator in static environment

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The Journal of china Universities of Posts and Telecommunications 2024年第3期31卷 56-71页

作者： wang Binpeng Huang Houqin Xu Fangzhou Ju Dianyuan li Zeqiang Feng Chao School of electrical Engineering and automation Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Jinan Engineering Laboratory of Human-Machine Intelligent Cooperation Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China International School for Optoelectronic Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Mobile manipulators are used in a variety of fields because of their flexibility and *** path planning capability of the mobile manipulator is one of the important indicators to evaluate the performance of the manipulator,but it is greatly challenged in the face of maps with narrow *** address the problem,an improved hierarchical motion planner(IHMP)is proposed,which consists of a two-dimensional(2D)path planner for the mobile base,and a three-dimensional(3D)trajectory planner for the on-board ***,a hybrid sampling strategy is proposed,which can reduce invalid nodes of the generated probabilistic *** test is used to locate the narrow channel areas,and a Gaussian sampler is deployed in these areas and the ***,a random sampler is deployed in the rest *** planner for on-board manipulator is to generate a collision-free and safe trajectory in the narrow channel with collaboration of the 2D path *** experimental results show that IHMP is effective for mobile manipulator motion planning in complex static environments,especially in narrow channel.

关键词： static environment mobile manipulator motion planner improved hierarchical motion planner(IHMP)

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时间约束三维超螺旋滑模制导控制一体化方法

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宇航学报 2024年第2期45卷 283-294页

作者：李雪王青北京航空航天大学自动化科学与电气工程学院北京100191 北京航空航天大学国际创新学院杭州311115

针对飞行器时间约束制导问题,提出了一种新型三维制导控制一体化方法。为满足大前置角及大期望到达时间下时间约束制导的需要,提出了一种新型滑模面,并证明了滑模运动时前置角的渐近收敛特性。在该滑模面的基础上,基于超螺旋算法和反步...

针对飞行器时间约束制导问题,提出了一种新型三维制导控制一体化方法。为满足大前置角及大期望到达时间下时间约束制导的需要,提出了一种新型滑模面,并证明了滑模运动时前置角的渐近收敛特性。在该滑模面的基础上,基于超螺旋算法和反步法,提出了有限时间稳定的制导控制一体化控制律,保证李普希茨有界扰动下的鲁棒,且控制量连续,其有限时间收敛特性通过李雅普诺夫稳定性理论得到证明,其有效性通过数值仿真得到了验证。

关键词：飞行器攻击时间控制制导制导控制一体化超螺旋算法反步法

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Event-triggered H_(∞) PI state estimation for delayed switched neural networks

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Journal of automation and Intelligence 2024年第1期3卷 26-33页

作者： Yuzhong wang Changyun Wen Xiaolei li Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and TechnologyShenzhen 518055China School of electrical and Electronic Engineering Nanyang Technological University639798Singapore School of electrical Engineering Yanshan UniversityQinhuangdao066004China

On state estimation problems of switched neural networks,most existing results with an event-triggered scheme(ETS)not only ignore the estimator information,but also just employ a fixed triggering threshold,and the estimation error cannot be guaranteed to converge to *** addition,the state estimator of non-switched neural networks with integral and exponentially convergent terms cannot be used to improve the estimation performance of switched neural networks due to the difficulties caused by the nonsmoothness of the considered Lyapunov function at the switching *** this paper,we aim at overcoming such difficulties and filling in the gaps,by proposing a novel adaptive ETS(AETS)to design an event-based H_(∞)switched proportional-integral(PI)state estimator.A triggering-dependent exponential convergence term and an integral term are introduced into the switched PI state *** relationship among the average dwell time,the AETS and the PI state estimator are established by the triggering-dependent exponential convergence term such that estimation error asymptotically converges to zero with H_(∞)performance *** is shown that the convergence rate of the resultant error system can be adaptively adjusted according to triggering ***,the validity of the proposed theoretical results is verified through two illustrative examples.

关键词： Switched neural networks H_(∞)performance PI state estimation Event-triggered scheme

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Reliability Analysis of a Switched Reluctance Starter/Generator

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Protection and Control of Modern Power Systems 2024年第1期9卷 112-121页

作者： Hao Chen Fan Yang lijun Xu Qiwei Lu Xiqiang Chang Xing wang lijiang Dong Yi Wen Jiang Chen Gang Miao Jianmin Hu Guanjun wang Wei li Xiaohua Bao School of electrical Engineering China University of Mining and TechnologyXuzhou 221116China Xinjiang Institute of Engineering Urumqi 830000China International Joint Research Center of Central and Eastern European Countries on New Energy Electric Vehicle Technology and Equipment Xuzhou 221008China International Cooperation Joint Laboratory of New Energy Power Generation and Electric Vehicles of Jiangsu Province Colleges and Universities Xuzhou 221008China School of Information and electrical Engineering China University of Mining and TechnologyXuzhouChina china university of Mining and Technology(Beijing) BeijingChina State Grid Xingjiang Electric Power Co. Ltd.UrumqiXinjiangChina china university of Mining and Technology XuzhouChina State Grid Xinjiang Company limited Electric Power Research Institute UrumqiXinjiangChina Xinjiang Fukang Pumped Storage Power Company limited UrumqiXinjiangChina State Grid Urumqi Electric Power Supply Company UrumqiXinjiangChina Wuxi Inspection and Testing Certification Research Institute WuxiJiangsuChina Anhui Wannan Electric Machine Co. Ltd.WannanAnhuiChina Hefei university of Technology in electrical and automation Engineering

In this paper,the combined k-out-of-n:G model and reliability block diagram model is used to analyze the reliability of a switched reluctance starter/generator ***,the different operational modes of a switched reluctance motor starter/generator are analyzed,and the fault states of the system are briefly *** the fault criteria of the system in different operational states are put ***,a relia-bility block diagram model is established to calculate the system-level reliability,and the k-out-of-n:G model is adopted to analyze the reliability of each part of the switched reluctance starter/generator *** verify effectiveness,the first-order Markov model is also used to analyze the reliability of each part of the switched reluc-tance starter/generator *** the compu-tational complexity and accuracy of the system,the k-out-of-n:G model is more suitable for system component level reliability ***,a 6/4 switched reluctance motor is used as the simulated and experimental platform *** final results verify the effectiveness of the reliability analysis model.

关键词： system switched generator

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A review on DFACS(I):System design and dynamics modeling

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Chinese Journal of Aeronautics 2024年第5期37卷 92-119页

作者： Bohan JIAO Qifan liU Zhaohui DANG Xiaokui YUE Yonghe ZHANG Yuanqing XIA li DUAN Qinglei HU Chenglei YUE Pengcheng wang Ming GUO Zhansheng DUAN Bing CUI Chu ZHANG Xiaodong SHAO School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China Innovation Academy for Microsatellites Chinese Academy of SciencesShanghai 710699China School of automation Beijing Institute of TechnologyBeijing 100081China Institute of Mechanics Chinese Academy of SciencesBeijing 710043China School of automation Science and electrical Engineering Beihang UniversityBeijing 100191China School of automation Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

The Drag-Free and Attitude Control System(DFACS)is a critical platform for various space missions,including high precision satellite navigation,geoscience and gravity field measurement,and space scientific *** paper presents a comprehensive review of over sixty years of research on the design and dynamics model of ***,we examine the open literature on DFACS and its applications in Drag-Free missions,providing readers with necessary background information on the ***,we analyze the system configurations and main characteristics of different DFACSs,paying particular attention to the coupling mechanism between the system configuration and dynamics ***,we summarize the dynamics modeling methods and main dynamics models of DFACS from multiple perspectives,including common fundamentals and specific ***,we identify current challenges and technological difficulties in the system design and dynamics modeling of DFACS,while suggesting potential avenues for future *** paper aims to provide readers with a comprehensive understanding of the state-of-the-art in DFACS research,as well as the future prospects and challenges in this field.

关键词： Drag-Free DFACS System Design Dynamics Modeling Control design

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A review on DFACS(II):Modeling and analysis of disturbances and noises

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Chinese Journal of Aeronautics 2024年第5期37卷 120-147页

作者： Chenglei YUE Bohan JIAO Zhaohui DANG Xiaokui YUE Yonghe ZHANG Yuanqing XIA li DUAN Qinglei HU Qifan liU Pengcheng wang Ming GUO Zhansheng DUAN Bing CUI Chu ZHANG Xiaodong SHAO School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China Innovation Academy for Microsatellites Chinese Academy of SciencesShanghai 710699China School of automation Beijing Institute of TechnologyBeijing 100081China Institute of Mechanics Chinese Academy of SciencesBeijing 710043China School of automation Science and electrical Engineering Beihang UniversityBeijing 100191China School of automation Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

This paper presents Part II of a review on DFACS,which specifically focuses on the modeling and analysis of disturbances and noises in *** Part I,the system composition and dynamics model of the DFACS were *** this paper,we discuss the effects of disturbance forces and noises on the system,and summarize various analysis and modeling methods for these interferences,including the integral method,frequency domain analysis method,and magnitude evaluation *** analyzing the impact of disturbances and noises on the system,the paper also summarizes the system’s performance under slight ***,we highlight current research difficulties in the field of DFACS noise ***,this paper provides valuable insights into the modeling and analysis of disturbances and noises in DFACSs,and identifies key areas for future research.

关键词： Drag-free DFACS Disturbance Analysis Noise Analysis Frequency Domain Analysis Performance Evaluation

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