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arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing

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Journal of Biosafety and Biosecurity 2021年第1期3卷 58-65页

作者： Shan Lu Weijia Wang Yanpeng Cheng Caixin Yang Yifan Jiao Mingchao Xu Yibo Bai Jing Yang Hongbin Song Ligui Wang Jiaojiao Wang Bing Rong Jianguo Xu State Key Laboratory of Infectious Disease Prevention and Control Chinese Center for Disease Control and PreventionNational Institute for Communicable Disease Control and PreventionBeijingPR China Shanghai Public Health Clinical Center Shanghai Institute for Emerging and Re-emerging Infectious DiseasesShanghaiPR China Research Units of Discovery of Unknown Bacteria and Function Chinese Academy of Medical SciencesBeijingPR China University of Connecticut StorrsCTUSA Scholl of Public Health Shanxi Medical UniversityTaiyuanShanxi ProvincePR China Center for Disease Control and Prevention of Chinese People’s Liberation Army Fengtai DistrictBeijingPR China The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingPR China Department of Epidemiology Center for Global HealthSchool of Public HealthNanjing Medical UniversityNanjingPR China Institute of Public Health Nankai UniversityTianjingPR China Department of Epidemiology and Biostatistics School of Public HealthNanjing Medical UniversityNanjingPR China

The re-emerging outbreak of COVID-19 in Beijing,China,in the summer of 2020 originated from a SARSCoV-2-infested wholesale food *** postulated that the Xinfadi market outbreak has links with food-trade *** Susceptible to the disease,Infectious,and Recovered coupled Agent Based Modelling(SIR-ABM)analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number(R0).The retail shop closure strategy reduced the number of visitors to the market by nearly *** addition,the buy-local policy option reduced the infection by more than 70%in ***,retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.

关键词： COVID-19 outbreak Transmission mechanism Food distribution network control measures Agent Based Modelling

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Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification

Learning Network-Invariant and Label-Discriminative Represen...

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Chinese automation Congress (CAC)

作者： Linyao Yang Yancai Xu Jiachen Hou Yuxin Dai Chen Lv The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China Institute of Systems Engineering Macau University of Science and Technology Macau China School of Electrical Engineering and Automation Wuhan University Wuhan China China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665426480

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules.

关键词： Support vector machines Knowledge engineering Analytical models automation Transfer learning Network analyzers Data structures

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Learning Disentangled Representation for One-Shot Progressive Face Swapping

arXiv

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arXiv 2022年

作者： Li, Qi Wang, Weining Xu, Chengzhong Sun, Zhenan Yang, Ming-Hsuan State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China The Department of Computer Science and Engineering University of California MercedCA95340 United States Department of Computer Science and Engineering Yonsei University Korea Republic of

Although face swapping has attracted much attention in recent years, it remains a challenging problem. Existing methods leverage a large number of data samples to explore the intrinsic properties of face swapping without considering the semantic information of face images. Moreover, the representation of the identity information tends to be fixed, leading to suboptimal face swapping. In this paper, we present a simple yet efficient method named FaceSwapper, for one-shot face swapping based on Generative Adversarial Networks. Our method consists of a disentangled representation module and a semantic-guided fusion module. The disentangled representation module comprises an attribute encoder and an identity encoder, which aims to achieve the disentanglement of the identity and attribute information. The identity encoder is more flexible, and the attribute encoder contains more attribute details than its competitors. Benefiting from the disentangled representation, FaceSwapper can swap face images progressively. In addition, semantic information is introduced into the semantic-guided fusion module to control the swapped region and model the pose and expression more accurately. Experimental results show that our method achieves state-of-the-art results on benchmark datasets with fewer training samples. Our code is publicly available at https://***/liqi-casia/FaceSwapper. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Distributed end-effector formation control for networked 2-DoF flexible-joint manipulators with partially underactuated agents

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Automatica 2025年 179卷

作者： Zhiyu Peng Bayu Jayawardhana Xin Xin School of Automation Southeast University and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen The Netherlands Institute of Intelligent Unmanned Systems Southeast University China

The presence of faulty or underactuated manipulators can disrupt the end-effector formation keeping of a team of manipulators. This paper investigates the distributed end-effector formation control of networked planar two-link manipulators with flexible joints, emphasizing cases where some agents are underactuated. The underactuated agents can be active–passive (AP) manipulators, where only the first joint is actuated; passive–active (PA) manipulators, where only the second joint is actuated; or a combination thereof. First, we establish an equilibrium property for the discussed underactuated manipulators with flexible joints. Building upon this property, we introduce the novel concept of virtual end-effectors for manipulators, whose positions serve as intermediate variables in the controller design. Then, we extend the distance-based and displacement-based formation controllers to accommodate this group of manipulators by coupling virtual end-effectors, rather than actual end-effectors used in existing works. Finally, we investigate the set of desired and reachable shapes for the networked end-effectors to highlight how the presence of underactuated manipulators affects the applicability of the proposed controllers. This analysis demonstrates that the distance-based method accommodates more underactuated manipulators in the group than the displacement-based method. We validate both our analysis and the proposed distributed controllers through numerical simulations.

关键词：

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Distributed Solving Sylvester equations with an explicit exponential Convergence

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IFAC-PapersOnLine 2020年第2期53卷 3260-3265页

作者： Songsong Cheng Xianlin Zeng Yiguang Hong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology 100081 Beijing China

This paper addresses distributed achieving the least squares solution of Sylvester equations in the form of AX + XB = C. By decomposing the parameter matrices A, B and C, we formulate the problem of distributed solving Sylvester equations as a distributed optimization model and propose a continuous-time algorithm from the primal-dual viewpoint. Then, by constructing a Lyapunov function, we prove that the proposed algorithm can achieve a least squares solution of Sylvester equations with an explicit exponential convergence rate. Additionally, we illustrate the convergence performance by using a numerical example.

关键词： Sylvester equations Distributed optimization Least squares solution exponential convergence

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An Island Remote Sensing Image Segmentation Algorithm Based on A Fusion Network with Attention Mechanism 3

An Island Remote Sensing Image Segmentation Algorithm Based ...

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2020 3rd International Conference on Computer Information Science and Artificial Intelligence, CISAI 2020

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Wu, Peng Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, in view of the problems that remote sensing images contain richer ratio information and complex background interference, we propose an island remote sensing image segmentation algorithm based on a fusion network with attention mechanism, called AFU-Net. The network is built on the basis of FC_U-Net [1]. An attention mechanism is added to pre-weight the shallow features before deep-shallow layer feature fusion, in order to enhance the response capability of the target features, suppress the background interference and improve the segmentation accuracy of the network. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of AFU-Net are greatly improved compared to FC_U-Net, and are also superior to other three state-of-the-art methods, U-Net, FCN, SegNet, which indicates the effectiveness of our method. © Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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An Island Remote Sensing Image Segmentation Algorithm Based on FC_U-Net Network

An Island Remote Sensing Image Segmentation Algorithm Based ...

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2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020

作者： Chen, Tianyuan Wang, Hongfei Liu, Hao Du, Junrong Wu, Peng Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194813

With the increasing importance of islands in many fields, it has become the focus of research to obtain information from island remote sensing images efficiently by using image semantic segmentation algorithm. In recent years, deep learning methods based on convolutional neural network have been widely used in image segmentation. However, for remote sensing images with rich details and different target scales, there are still some problems such as insufficient segmentation accuracy. To handle these problems, we propose an island remote sensing image segmentation algorithm based on FC_U-Net network. In this network, U-Net and FCN (Fully Convolutional Network) are improved respectively to form two new modules, and then the semantic features and detail features from the two modules are fused by a fusion module. The testing and comparative experiments on NWPU-RESISC45 dataset show that the quantitative metrics and visual effects of FC_U-Net are better than other three state-of-the-art methods, U-Net, FCN (Fully Convolutional Network), SegNet, which indicates the effectiveness of our method. © 2020 IEEE.

关键词： Semantic Segmentation

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Robotic Autonomous Grasping Technique: A Survey

Robotic Autonomous Grasping Technique: A Survey

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Lili Wang Zhen Zhang Jianhua Su Qipeng Gu the School of Artificial Intelligence University of Chinese Academy of Sciences Beijing P.R. China the State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P.R. China

ISBN: (纸本)9781665426312

This paper provides a comprehensive survey of robotic autonomous grasping techniques. We summarize three key tasks: grasp detection, affordance detection, and model migration. Grasp detection determines the graspable area and grasping posture of the manipulator, so that the robot can successfully perform the grasps. The grasp detection methods based on deep learning are divided into 3DoF grasp and 6DoF grasp. The object affordances based grasping methods can further improve the robot's understanding of objects and environment, thereby improving the robot's intelligence and autonomy. Methods for object affordances detection are classified as learning-based, knowledge-based, and simulation-based. Model migration means that when the grasping model is migrated to other scenes where lightness and background changes, only little or no label data is required, so that the grasping model can be used in the target scene quickly and efficiently. This paper focuses on domain adaptation (DA) methods in model migration.

关键词： Deep learning Adaptation models Affordances Knowledge based systems Focusing Grasping Manipulators

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Kinematics modeling and trajectory planning for the skull reconstruction robot

Kinematics modeling and trajectory planning for the skull re...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Wang, Weiqun Wang, Xiru Hou, Zeng-Guang Fang, Zhijie Jiao, Yuze Luo, Yangyu Gong, Jian State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Beijing100080 China Center for Excellent in Brain Science and Intelligence Technology Beijing100190 China Department of Electronics Polytech Nice Sophia Nice06200 France CASIA UCAS Department of Pediatric Neurosurgery Beijing Tiantan Hospital Capital Medical University Beijing100050 China

ISBN: (纸本)9781665436786

A skull reconstruction robot has been developed to effectively overcome shortcomings of the traditional skull surgery and improve its efficiency and quality. In this study, kinematics modeling and trajectory planning for the robot are carried out. Firstly, the kinematic model of the robot is established based on the D-H method. The positioning points, which are used to determine the cutting plane, are given on the surface of the skull by the surgeon. The contour data for cutting can be obtained using a two dimensional laser scanning sensor, by adjusting the scanning light curtain to coincide with the cutting plane. Then, to ensure that the tools fixed at the robot end can always be perpendicular to the skull surface during the cutting or drawing process, the principal component analysis is introduced to design the normal vectors at the selected key points of the point cloud data for the skull surface. Moreover, the quintic polynomial interpolation method is used to construct the curves between the adjacent key points to make up for the defects of the skull surface and ensure the smoothness of the trajectory. Finally, workspace analysis based on Monte Carlo method is implemented, and the proposed kinematics modeling and trajectory planning methods are validated by simulation. © 2021 IEEE.

关键词： Monte Carlo methods

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