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2020 IEEE International Conference on Networking, Sensing and control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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Optimization of Robot End-Effector Load Gravity Compensation for Unmanned Live Work in Electric Power Distribution Networks Based on Improved PSO Algorithm

Optimization of Robot End-Effector Load Gravity Compensation...

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Chinese automation Congress (CAC)

作者： Jinge Chen Xin Chen Chenyu Hu Guangxian Huang Shu Sun Xu Jian School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering-Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Distribution Department State Grid Hubei Electric Power Co. Ltd. Xianning Power Supply Company Xianning China Equipment Technology Center State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan China

In the field of electric power distribution network operation, most tasks involve contact operations. A key technology to enable the flexible operation of robots in live distribution network tasks is the installation of six-dimensional force sensors on the robot's end flange. These sensors allow the robot to perceive external environmental contact forces. However, the presence of the end load interferes with the accurate perception of external forces. Aiming at the gravity compensation of robot end load, an optimization algorithm of gravity compensation based on particle swarm optimization is proposed. In this method, the optimal solution model is established with the objective of minimum sum of squared errors, and the particle swarm optimization algorithm is used to estimate the mounting declination angle of the force sensor and improve the precision of gravity compensation. The experiments show that the proposed method can effectively reduce the maximum gravity compensation error and the average gravity compensation error in each direction after optimization, in which the maximum gravity compensation error of the resultant force is reduced by 9.11% and the average gravity compensation error of the resultant force is reduced by 12.34%.

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical engineering 2024年 1148 LNEE卷 v页

作者： Sikander, Afzal Zurek-Mortka, Marta Chanda, Chandan Kumar Mondal, Pranab Kumar Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar Nati onal Institute of Technology Jalandhar Punjab Jalandhar India Department of Control Systems Lukasiewicz Research Network—Institute for Sustainable Technologies Radom Poland Department of Electrical Engineering Indian Institute of Engineering Science West Bengal Howrah India Department of Mechanical Engineering Indian Institute of Technology Guwahati Assam Guwahati India

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Correction to: Optimization of the control paper production system of using the extremum seeking control method

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SN Applied Sciences 2023年第3期5卷 1-1页

作者： Ermilina, Olga Chemodanov, Vladimir Department “Automation and control” Penza State Technological University Penza Russian Federation Department “Control systems informatics and power engineering” Moscow Aviation Institute (National Research University) Moscow Russian Federation

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Decision-Making in Driver-automation Shared control:A Review and Perspectives

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1289-1307页

作者： Wenshuo Wang Xiaoxiang Na Dongpu Cao Jianwei Gong Junqiang Xi Yang Xing Fei-Yue Wang IEEE California PATH University of CaliforniaBerkeleyCA 94720 USA Department of Engineering University of CambridgeCambridge CB21PZU.K. Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L3G1Canada Department of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Research Center for Military Computational Experiments and Parallel Systems TechnologyNational University of Defense TechnologyChangsha 410073China

Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to *** human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making *** interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of *** open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared ***,we give a further discussion on the key future challenges and *** are likely to shape new potential research directions.

关键词： Automated vehicle decision-making human driver human-vehicle interaction shared control

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Imitation dynamics on networks with incomplete information

arXiv

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arXiv 2023年

作者： Wang, Xiaochen Zhou, Lei McAvoy, Alex Li, Aming Center for Systems and Control College of Engineering Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China

Imitation is an important learning heuristic in animal and human societies. Previous explorations report that the fate of individuals with cooperative strategies is sensitive to the protocol of imitation, leading to a conundrum about how different styles of imitation quantitatively impact the evolution of cooperation. Here, we take a different perspective on the personal and external social information required by imitation. We develop a general model of imitation dynamics with incomplete information in networked systems, which unifies classical update rules including the death-birth and pairwise-comparison rule on complex networks. Under pairwise interactions, we find that collective cooperation is most promoted if individuals neglect personal information. If personal information is considered, cooperators evolve more readily with more external information. Intriguingly, when interactions take place in groups on networks with low degrees of clustering, using more personal and less external information better facilitates cooperation. Our unifying perspective uncovers intuition by examining the rate and range of competition induced by different information situations. © 2023, CC BY.

关键词： Complex networks

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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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Avian influenza viruses suppress innate immunity by inducingtrans-transcriptional readthrough via SSU72

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Cellular & Molecular Immunology 2022年第6期19卷 702-714页

作者： Yan Zhao Fengming Huang Zhen Zou Yuhai Bi Yang Yang Cong Zhang Qiang Liu Daozhen Shang Yiwu Yan Xiangwu Ju Song Mei Peng Xie Xiao Li Mingyao Tian Shuguang Tan Huijun Lu Zongsheng Han Kangtai Liu Yuqing Zhang Junbo Liang Zhu Liang Qingchao Zhang Jiahui Chang William JLiu Cong Feng Tanshi Li Michael Q.Zhang Xiaoyue Wang George FGao Yingxia Liu Ningyi Jin Chengyu Jiang State Key Laboratory of Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesDepartment of BiochemistryPeking Union Medical CollegeBeijing 100005China Shenzhen Key Laboratory of Pathogen and Immunity State Key Discipline of Infectious DiseaseSecond Hospital Affiliated with Southern University of Science and TechnologyShenzhen Third People’s HospitalShenzhenGuangdong 518112China CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of MicrobiologyCenter for Influenza Research and Early-warning(CASCIRE)CAS-TWAS Center of Excellence for Emerging Infectious Diseases(CEEID)Chinese Academy of SciencesBeijing 100101China School of Life Science University of Science and Technology of ChinaHefeiAnhui 230027China Department of Automation Tsinghua UniversityBeijing 100084China Genetic Engineering Laboratory Institute of Military VeterinaryAcademy of Military Medical SciencesChangchunJilin 130062China State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy West China HospitalSichuan UniversityChengduSichuan 610000China State Key Laboratory of Medical Molecular Biology Department of Biochemistry and Center for BioinformaticsInstitute of Basic Medical SciencesChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China National Institute for Viral Disease Control and Prevention Chinese Centers for Disease Control and PreventionBeijing 102206China Department of Emergency General Hospital of the PLAHaidian DistrictBeijing 100853China Department of Biological Sciences and Center for Systems Biology The University of Texas at DallasRichardsonTXUSA

Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can betterescape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by whichtranscriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouselungs, and patient PBMCs, we showed that genes on the complementary strand (“trans” genes) influenced by TRT were involved inthe disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishmentincreased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 inducedTRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT byreducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.

关键词： AIV infection TRT SSU72 NS1 Immune escape

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Society 5.0: Metaverse Facilitated Human-centric “5H” Services Across Cyber-Physical-Social Spaces

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IFAC-PapersOnLine 2024年第3期58卷 159-164页

作者： Xiao Wang Jun Huang QingHua Ni Zhe Wan HuaDan Xue Larry Stapleton 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 Beijing Normal University Beijing 100875 China Department of Radiology Peking Union Medical College Hospital Shuaifuyuan Beijing 100730 Dongcheng District China Brownsbarn Thomastown Co. Kilkenny Ireland R95D8P2 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

Cyber-Physical-Social systems bridge the gap between social resource organization and distribution, and give birth to Society 5.0, which represents a transformative vision aimed at realizing the human-centric paradigm for more efficient and effective sustainable development. In this paper, we discuss the decentralized paradigm in fostering human needs-driven smart services, in line with Maslow’s hierarchy of needs theory. Societies 5.0 is committed to optimizing the use of natural, artificial, and social resources by enhancing democratic participation and ensuring that decision-making processes are more transparent, reliable, and aligned with the needs of the populace in sustainable ways. Moreover, our discussion extends to how Society 5.0’s principles correlate with the United Nations Sustainable Development Goals (SDGs), providing a framework that supports health, education, and poverty reduction, among other targets. This approach also aligns with the Human Development Index (HDI), reinforcing the notion that technological progress in Society 5.0 does not overshadow but rather complements and enhances human values, paving a synergistic path towards sustainable development that is both advanced and fundamentally human-centric.

关键词： Cyber-Physical-Social systems Society 5.0 Human-Centric Services Socio-technical synergistic sustainable development

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Structural reduction of CRNs with linear sub-CRNs

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IFAC-PapersOnLine 2021年第14期54卷 149-154页

作者： Katalin M. Hangos György Lipták Gábor Szederkényi Systems and Control Laboratory Computer and Automation Research Institute P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University Budapest Hungary

A novel systems theory-based structural reduction method is proposed in this paper that can be applied to chemical reaction networks (CRNs) including subsystems consisting of linear reactions, i.e. a linear sub-CRN. The reduced model is a delayed CRN with distributed delays having less complexes (monomials) and reactions than the original model. The reduction is based on the fact that the input/output response of a linear sub-CNR can be seen as a time-delayed input to output relationship, therefore linear sub-CNRs can be interpreted as reactions with distributed delay from their input to their output complexes. The practically important example of a kinetic model describing the spread of the COVID-19 epidemic is used as a case study to illustrate the basic concepts and the computation method.

关键词： Delay Model reduction Chemical Reaction Networks COVID

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