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18th IEEE International Conference on Intelligence and Security Informatics, ISI 2020

作者： Miao, Pu Tian, Hu Zhang, Xingwei He, Saike Zheng, Xiaolong Dash Wu, Desheng Zeng, Daniel Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China School of Artificial Intelligence Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Stockholm Business School Stockholm University StockholmSE-106 91 Sweden

ISBN: (纸本)9781728188003

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies. © 2020 IEEE.

关键词： COVID-19

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Parallel Governance for Decentralized Autonomous Organizations enabled by Blockchain and Smart Contracts

Parallel Governance for Decentralized Autonomous Organizatio...

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

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

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.

关键词： Computational modeling Digital twin Conferences Smart contracts Tools systems modeling Market research

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Pruning-Based YOLOv4 Algorithm for Underwater Gabage Detection

Pruning-Based YOLOv4 Algorithm for Underwater Gabage Detecti...

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第40届中国控制会议

作者： Manjun Tian Xiali Li Shihan Kong Licheng wu Junzhi Yu School of Information Engineering Minzu University of China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Dept.Adv.Manuf.Robot. BIC-ESAT College of Engineering Peking University

To tackle the problem of aquatic environment pollution, a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. This paper proposes a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object detection. More specifically, the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection speed, the channel pruning and layer pruning are implemented on the trained YOLOv4 model, while the fine-tuned mechanism assists the pruned model to restore accuracy. In virtue of the improved detection methods, the robot has the ability to collect garbage *** experimental results indicate that the pruned YOLOv4 detection method can still maintain the high performance even though the parameter amount is 7.062% of the original model.

关键词： Object detection aquatic environment garbage cleaning robot modified YOLOv4

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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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IEEE International Conference on Robotics and Biomimetics

作者： De-Xing Huang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou Ning Ma Long Yan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The School of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing China

ISBN: (纸本)9781665481106

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (6~12 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians.

关键词： Image registration Estimation error Three-dimensional displays Navigation Biomimetics Medical services Real-time systems

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An Adaptive Federated control Strategy for Participant Selection in Multi-Client Collaboration

An Adaptive Federated Control Strategy for Participant Selec...

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

作者： Yizhu Chen Xiaoming Du Xiao Wang Jun Jason Zhang Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Electrical Engineering and Automation Wuhan University Wuhan China

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %,9 respectively.

关键词： Training Adaptation models Costs Biological system modeling Collaboration Optimization methods Ecology

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FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, droprule, and powerball adabelief

arXiv

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arXiv 2020年

作者： Shi, Zhenhua Wu, Dongrui Guo, Chenfeng Zhao, Changming Cui, Yuqi Wang, Fei-Yue Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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AI Graphical Programming Learning Platform for Children

AI Graphical Programming Learning Platform for Children

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Chinese automation Congress (CAC)

作者： Hongxia Zhao Xiwei Liu Xiaoyan Gong Qiang Li Sifeng Jing Yaofeng Xue The Stale Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China University of Chinese Academy of Sciences Beijing China East China Normal University Shanghai China

ISBN: (纸本)9781665426480

In this era of rapid development of artificial intelligence, the truth of competition in the future is the talent. Countries all over the world attach great importance to the programming and artificial intelligence education of children. In the future, the theoretical development and practical application of this emerging field will usher in a period of rapid development. This paper presents an artificial intelligence graphical programming learning platform based on Jetson Nano, which combines artificial intelligence education with modular software programming, provides students with a more open, intelligent, and interesting programming environment through the cross-platform development mode of "cloud service + intelligent hardware", and adopts visual programming to reduce the difficulty of artificial intelligence programming. The platform can fully tap learning resources, reduce education costs, and make artificial intelligence education more intuitive and humanized.

关键词： Visualization Costs automation Education Learning (artificial intelligence) Games Software

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Turning control of a Tuna-Like BAUV for a Minimum Turning Radius

Turning Control of a Tuna-Like BAUV for a Minimum Turning Ra...

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第40届中国控制会议

作者： Sheng Du Junzhi Yu Zhengxing Wu Di Chen Ming Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Dept.Adv.Manuf.Robot. BIC-ESAT College of Engineering Peking University School of Information and Electrical Engineering Shandong Jianzhu University

In order to break the bottleneck of conventional autonomous underwater vehicles(AUVs), biologically-inspired AUVs(BAUVs) have drawn great attention. This paper explores the turning control issue of a tuna-like BAUV with the purpose of seeking a minimum turning radius. Specifically, for better understanding of the turning motion, a dynamical model is established and the force acting on the tail fin is taken as the main source. Because of the complex nonlinear relationship and physical restrictions, it is hard to optimize the turning radius directly. To tackle with this problem, the input signals of joints are generated by sine function so as to satisfy physical constraints, and simulations under different parameter combinations are ***, a fixed tail joint method and a half-cycle flapping method are chosen and compared in an indoor pool. Finally,the experimental results verify the effectiveness of proposed methods, and a minimum turning radius of the tuna-like BAUV is achieved as small as 0.37 body lengths.

关键词： Turning control biologically-inspired autonomous underwater vehicles tuna-like robot

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A Safety Joint with Passive Compliant and Manual Override Mechanisms for Medical Robotics

A Safety Joint with Passive Compliant and Manual Override Me...

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IEEE International Conference on Intelligence and Safety for Robotics (ISR)

作者： Jia Zheng Shuangyi Wang James Housden Zeng-Guang Hou Davinder Singh Kawal Rhode School of General Engineering Beihang University Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Biomedical Engineering and Imaging Sciences King's College London London UK Xtronics Ltd. Gravesend UK

Force and collision control is a primary concern to guarantee the safe use of medical robots as such systems normally need to interact with clinicians and patients, while at the same time cooperate with other devices. Among different strategies, passive features working with intrinsically safety components are treated as one of the most effective approaches and therefore deserve in-depth study. In this study, we focus on the design of a novel back-drivable safety joint that incorporates a torque limiter with passive compliance and a manual override mechanism to disconnect the robotic joint from its drive train. The design and working principle of the proposed joint are explained, followed by the mathematical analysis of its performance and the impacts of parameters. An example of the design was manufactured and tested experimentally to validate the working concepts. It is concluded that the proposed multi-functional safety joint provides more versatility and customization to the design of bespoke medical robots and would limit the maximum torque that can be exserted onto the patient, allow the clinician to push the joint back, and enable the operator to switch back to manual override.

关键词： Torque Medical robotics Mathematical analysis Manuals Switches Drives Back

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A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 961-968页

作者： Xiuqin Shang Dayong Shen Fei-Yue Wang Timo R. Nyberg the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences (CAS)Beijing 100190China the Qingdao Academy of Intelligent Industries Qingdao 266109China IEEE the Beijing Enineering Research Center of Intelligent Systems and Technology Institute of AutomationCASBeijing 100190China the Cloud Computing Center CASDongguan 523808China the School of Science at Aalto University AaltoFinland

We study the fabric spreading and cutting problem in apparel *** the sake of saving the material costs,the cutting requirement should be met exactly without producing additional garment *** reducing the production costs,the number of lays that corresponds to the frequency of using the cutting beds should be *** propose an iterated greedy algorithm for solving the fabric spreading and cutting *** algorithm contains a constructive procedure and an improving *** the constructive procedure creates a set of lays in sequence,and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay *** improving loop will run until it cannot obtain any smaller lay set or the time limit is *** experiment results on 500 cases show that the proposed algorithm is effective and efficient.

关键词： Construction and improvement cutting and packing fabric spreading and cutting heuristic algorithm

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