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

作者： Ding, Wen Wen Liang, Xiaolong Hou, Jiachen Wang, Ge Yuan, Yong Li, Junqing Wang, Fei-Yue Institute of Systems Engineering Macau University of Science and Technology China School of Mathematics Renmin University of China Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works. © 2021 IEEE.

关键词： Smart contract

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Autonomous Free-Floating Underwater Assembly control for an Underwater Biomimetic Vehicle-Manipulator System

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IEEE Transactions on Industrial Electronics 2025年

作者： Bai, Xuejian Wang, Yu Lv, Jiaqi Cheng, Long Wang, Shuo Tan, Min Liaoning University of Technology Jinzhou 121001 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing 100190 China Idriverplus Technology Company Ltd. Beijing 100192 China Tsinghua University School of Vehicle and Mobility Beijing 100084 China

In this article, an improved coordinated control method based on the tracking differentiator with a median filter and dynamic surface control was proposed for an autonomous free-floating underwater assembly manipulation. In addition, the disturbances caused by the manipulator’s motion were considered and compensated by a feedforward controller of the manipulator. Furthermore, the inward counter-propagating sinusoidal waves of the biomimetic undulatory-finned propulsors were adopted and a fuzzy logic controller was designed to realize the transformation between the control parameters and the demanded propulsive forces. The stability of the proposed control scheme was proven by the Lyapunov theory. Finally, practical experiments verified that the control scheme can complete the autonomous free-floating underwater assembly manipulation task, which indicated the good feasibility and robustness of the proposed methods. © 1982-2012 IEEE.

关键词： Autonomous free-floating control dynamic surface control (DSC) underwater assembly control underwater biomimetic vehicle-manipulator system (UBVMS)

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A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson's Disease via Wearable Sensors

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第4期14卷 1553-1563页

作者： Wang, Chen Peng, Liang Hou, Zeng-Guang Li, Yanfeng Tan, Ying Hao, Honglin Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Med Sci Peking Union Med Coll Hosp Peking Union Med Coll Beijing 100730 Peoples R China

Parkinson's disease (PD) is the second most common neurodegenerative disorder and the heterogeneity of early PD leads to interrater and intrarater variability in observation based clinical assessment. Thus, objective monitoring of PD induced motor abnormalities has attracted significant attention to manage disease progression. Here, we proposed a hierarchical architecture to reliably detect abnormal characteristics and comprehensively quantify the multisymptom severity in patients with PD. A novel wearable device was designed to measure motor features in 15 PD patients and 15 age-matched healthy subjects, while performing five types of motor tasks. The abnormality classes of multimodal measurements were recognized by hidden Markov models (HMMs) in the first layer of the proposed architecture, aiming at motivating the evaluation of specific motor manifestations. Subsequently, in the second layer, three single symptom models differentiated PD motor characteristics from normal motion patterns and quantified the severity of cardinal PD symptoms in parallel. In order to further analyze the disease status, the multilevel severity quantification was fused in the third layer, where machine learning algorithms were adopted to develop a multisymptom severity score. The experimental results demonstrated that the quantification of three cardinal symptoms was highly accurate to distinguish PD patients from healthy controls. Furthermore, strong correlations were observed between the Unified PD Rating Scale (UPDRS) scores and the predicted subscores for tremor (R = 0.75, P = 1.40e - 3), bradykinesia (R = 0.71, P = 2.80e - 3), and coordination impairments (R = 0.69, P = 4.20e - 3), and the correlation coefficient can be enhanced to 0.88 (P = 1.26e - 5) based on the fusion schemes. In conclusion, the proposed assessment architecture holds great promise to push forward the in-home monitoring of clinical manifestations, thus enabling the self-assessment of disease progression.

关键词： Diseases Machine learning Hidden Markov models Accelerometers Monitoring Gyroscopes Parkinson's disease Wearable computing Sensor systems multilevel fusion multisymptom assessment Parkinson's disease (PD) wearable sensor system

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What will robots be like in the future?

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National Science Review 2019年第5期6卷 1059-1061页

作者： Yanfeng Lu Weijie Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences NSR

Robots are changing our lives:sweeping robots patrol our living rooms; interactive robots accompany our children; industrial robots assemble vehicles; rescue robots search and save lives in catastrophes; medical robots perform surgeries in *** better understand robots’challenges and impact, National Science Review (NSR) interviewed Professor Toshio Fukuda, who is one of the world’s most representative robotics experts and has developed a number of bionic robots and micro/nano-robots.

关键词： NSR China What will robots be like in the future

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Social manufacturing pattern in the intelligent reform of the shoes and clothing industry 1

Social manufacturing pattern in the intelligent reform of th...

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

作者： Cao, Yansong Wang, Yutong Dai, Juwu Zhu, Xihao Wang, Fei-Yue Macau Institute of Systems Engineering Macau University of Science and Technology China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Ningbo Sanbody Intelligent Technology Co. LTD Ningbo China University of Nottingham Faculty of Science and Engineering Ningbo China

ISBN: (纸本)9781665433372

Along with economic globalization, the shoes and clothing market is undergoing huge changes and is facing increasing individualized demands. To realize personalized customization, we propose a social manufacturing pattern for the shoes and clothing industry. On the customer side, we collect costumer information with the help of 3D scanning and virtual fitting. On the production side, we replace human labor with intelligent production equipment. On the enterprise management side, we construct a cloud platform to realize source maximization. Through this social manufacturing pattern, we expect to accelerate the intelligent reform of the shoes and clothing industry. © 2021 IEEE.

关键词： Shoe manufacture

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Three-dimensional target tracking control for a biomimetic underwater vehicle 30

Three-dimensional target tracking control for a biomimetic u...

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30th International Ocean and Polar Engineering Conference, ISOPE 2020

作者： Bai, Ge Wang, Rui Wang, Yu Wang, Shuo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781880653845

This paper presents a target tracking control method for a biomimetic underwater vehicle named RobCutt-II. The RobCutt-II is propelled by double undulatory fins which are distributed on both sides. In order to implement target tracking and observation in a certain distance, we propose a method by decoupling the target tracking control problem into distance, heading and depth control problems. Based on this idea, we design a target tracking controller composed of a navigation system, a distance controller, a heading controller, a depth controller and a switching logic controller. The simulation results verify the effectiveness of the proposed method on the biomimetic underwater vehicle. © 2020 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： controllers

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Parallel Theaters in CPSS: From Shadows of ISDOS to Intelligence of Decision Theaters

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1059-1062页

作者： Qinghua Ni Buday Viktória Fei Lin Jun Huang Levente Kovács Nan Zheng Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Innovation Management Doctoral School Obuda University Budapest Hungary John von Neumann Faculty of Informatics Obuda University Budapest Hungary State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China DeSci Center of Parallel Intelligence and the University Research and Innovation Center at Obuda University State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

As Artificial Intelligence (AI) is moving fast from Large Language Models (LLMs) to AI Agents and Agentic Intelligence, the need to incorporate new AI into Decision Intelligence (DI) is becoming more and more urgent for both practical and theoretic reasons: both decision and process complexities would be significantly increased due to the use of advanced AI tools and agents, and both traditional and recent thinking must be rethought and reconstructed accordingly. Our perspective would like to address this important issue based on some historical milestone developments in Computer-Aided Software Engineering (CASE) and recent efforts in digital theatrical technology [1].

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Modeling human travel and social contact with multi-layer networks for epidemic prediction 9

Modeling human travel and social contact with multi-layer ne...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Duan, Wei Wang, Tao Wang, Peng Ju, Rusheng Wang, Xiao Yang, Tian College of Systems Engineering National University of Defense Technology Changsha City China State Key Laboratry of Complex Systems Management and Control Institute of Automation Chinese Academy of Sciences Beijing City China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha City China

ISBN: (纸本)9780738132020

It is a key issue to reasonably represent human travel and social contact in epidemic models. Various measures were applied to develop the models of human mobility and contact in a long range or a short range, such as Brown movement, random walks, spatial networks, gravity models, contact networks. We proposed a method of representing human daily movement and social contact by using multi-layer networks with temporal edge weights. We combined bipartite networks with social networks to describe human daily trip and social contact, respectively. Temporal edge weights of multi-layer networks were employed to denote the propensity of individual movement and contact. We also verified our models and parameters by incorporating human daily travel and contact regularities, as well as comparing experimental results with human behavior statistical laws. At last, we applied a Chinese university campus as a case study to investigate students' daily travel and social contact, and studied the transmission and control strategies of COVID-19 virus. We found stricter control strategies are needed to mitigate the transmission of COVID-19 virus in a university. Once a patient case emerges in a university, it is better to close the campus and quarantine all students. Partial control strategies such as quarantining a part of students and buildings cannot achieve a great effect of mitigating the transmission of COVID-19 virus. Our works are beneficial for the practitioners in the field of computational epidemiology. © 2021 IEEE.

关键词： Viruses

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Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Fan, Siqi Zhu, Fenghua Feng, Zunlei Lv, Yisheng Song, Mingli Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Tsinghua University Beijing China The College of Computer Science and Technology Zhejiang University Hangzhou China

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Path Planning of Mobile Robot Based on an Improved Genetic Algorithm

Path Planning of Mobile Robot Based on an Improved Genetic A...

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

作者： Zenghua Chen Gang Xiong Sheng Liu Zhen Shen Yue Li State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Automobile Chang'an University Xi'an China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations; Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm; Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles; Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning.

关键词： Sociology Clustering algorithms Path planning Planning Trajectory Statistics Collision avoidance

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