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IEEE International Conference on Robotics and automation (ICRA)

作者： Zhen-Liang Ni Gui-Bin Bian Zeng-Guang Hou Xiao-Hu Zhou Xiao-Liang Xie Zhen Li 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

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The real-time segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, it is still a challenging task to implement deep learning models to do real-time segmentation for surgical instruments due to their high computational costs and slow inference speed. In this paper, we propose an attention-guided lightweight network (LWANet), which can segment surgical instruments in real-time. LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution. Depthwise separable convolution is used as the basic unit to construct the decoder, which can reduce the model size and computational costs. Attention fusion block captures global contexts and encodes semantic dependencies between channels to emphasize target regions, contributing to locating the surgical instrument. Transposed convolution is performed to upsample feature maps for acquiring refined edges. LWANet can segment surgical instruments in real-time while takes little computational costs. Based on 960x544 inputs, its inference speed can reach 39 fps with only 3.39 GFLOPs. Also, it has a small model size and the number of parameters is only 2.06 M. The proposed network is evaluated on two datasets. It achieves state-of-the- art performance 94.10% mean IOU on Cata7 and obtains a new record on EndoVis 2017 with a 4.10% increase on mean IOU.

关键词： Instruments Convolution Semantics Computational efficiency Decoding Surgery Real-time systems

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Shift-Invariant Convolutional Network Search

Shift-Invariant Convolutional Network Search

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International Joint Conference on Neural Networks (IJCNN)

作者： Nannan Li Yaran Chen Zixiang Ding Dongbin Zhao 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

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The development of Neural Architecture Search (NAS) makes Convolutional Neural Networks (CNN) more diverse and effective. But previous NAS approaches don't pay attention to the shift-invariant of CNN. Without the shift-invariant, convolutional network is not robust enough when input data is disturbed or damaged. Besides, taking accuracy as the only optimization goal of NAS cannot meet the increasingly diverse needs. In this paper, we propose Shift-Invariant Convolutional Network Search (SICNS). It uses one-shot NAS to search for shift-invariant convolutional network by incorporating the low-pass filter into the one-shot model. Furthermore, SICNS optimizes multiple indicators simultaneously through the multi-objective evolutionary algorithm. Through training one-shot model and evolving the architecture, we obtain convolutional networks which are robust and powerful on image classification task. Especially, our work can achieve 4.52% test error on CIFAR-10 with 0.7M parameters. And in case the input data are disturbed, the accuracy of searched network is 2.96% higher than network without low-pass filter.

关键词： Training Optimization Kernel Convolution Adaptation models Task analysis Search problems

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Cooperative Multi-Agent Deep Reinforcement Learning with Counterfactual Reward

Cooperative Multi-Agent Deep Reinforcement Learning with Cou...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kun Shao Yuanheng Zhu Zhentao Tang Dongbin Zhao 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

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In partially observable fully cooperative games, agents generally tend to maximize global rewards with joint actions, so it is difficult for each agent to deduce their own contribution. To address this credit assignment problem, we propose a multi-agent reinforcement learning algorithm with counterfactual reward mechanism, which is termed as CoRe algorithm. CoRe computes the global reward difference in condition that the agent does not take its actual action but takes other actions, while other agents fix their actual actions. This approach can determine each agent's contribution for the global reward. We evaluate CoRe in a simplified Pig Chase game with a decentralised Deep Q Network (DQN) framework. The proposed method helps agents learn end-to-end collaborative behaviors. Compared with other DQN variants with global reward, CoRe significantly improves learning efficiency and achieves better results. In addition, CoRe shows excellent performances in various size game environments.

关键词： Games Learning (artificial intelligence) Machine learning Collaboration Training Task analysis Multi-agent systems

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Multi-UAV 3D Path Planning in Simultaneous Attack

Multi-UAV 3D Path Planning in Simultaneous Attack

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International Conference on control and automation (ICCA)

作者： Chuyi Xiong Bin Xin Miao Guo Yulong Ding Hao Zhang School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

As the key technology of UAV mission planning, path planning has a very important impact on the combat effectiveness of UAV. In order to destroy enemy units, multiple UAVs are often needed to attack the target at the same time. In the complex combat environment, the path planning of multiple UAVs is a very complex problem. On the basis of genetic algorithm, this paper designs a path planning algorithm to ensure that multiple UAVs arrive at the same time from different angles on the premise of satisfying environment constraints and the motion constraints of UAV. The algorithm uses the method of regionalization to initialize the path, designs a reasonable fitness function, and adds an adaptive disturbance operator to plan the path of each UAV. Finally, the length difference between the paths planned for each UAV is taken as the path evaluation factor. Some of the paths are replanned to achieve the goal of the same path length and simultaneous attack by UAV. The effectiveness of the algorithm is verified by experiments in typical combat scenarios, and the superiority of the adaptive disturbance operator in the optimization of the algorithm is verified by the contrast experiment.

关键词： Path planning Radar Planning Optimization Radar detection Genetic algorithms Three-dimensional displays

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Attention based Graph Covolution Networks for Intelligent Traffic Flow Analysis

Attention based Graph Covolution Networks for Intelligent Tr...

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Hongxin Zhang Jiaxin Liu Ying Tang Gang Xiong State Key Lab of CAD & CG Zhejiang University China College of Software Engineering Zhejiang University of Technology China State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Predicting traffic flow is one of the fundamental issue to smart cities. However it is still challenging in vehicular cyber-physical systems because of ever-increasing urban traffic data. Most previous models for traffic flow prediction suffers the problem with variance error depends on time and location. In this paper, a novel graph convolution network (GCN) approach is proposed for predicting citywide traffic flow. Our key idea is to introduce spatial and temporal attention to the GCN model to lessen the impact of urban data complexity. A framework of flow-based graph convolutional network is established to improve traffic flow prediction while investigating the spatial and temporal correlation of traffic flow. We further propose practical strategies that efficiently learn parameters of the model. Experimental results demonstrate that the proposed approach to traffic flow prediction outperforms state-of-the-art approaches.

关键词： Predictive models Data models Analytical models Convolution Urban areas Chebyshev approximation Roads

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Data-Driven Modeling and Random Search Optimization Method for the Polysilicon Cvd Reactor

SSRN

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SSRN 2022年

作者： Xi, Bangwen Xiong, Gang Shen, Zhen Tamir, Tariku Sinshaw He, Chang Song, Yonggang Liu, Xiong 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 Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266109 China Xinte Energy Co. Ltd Urumqi830000 China

Polysilicon is mainly obtained by chemical vapor deposition(CVD) reaction, in which hydrogen gas and trichlorosilane(TCS) are fed into the CVD reactor to produce polysilicon. However, the feeding parameters are mainly adjusted in a step-by-step manner according to the actual deposition results, which is inefficient to optimize and prone to resource wastage. In this paper, based on the existing mechanistic model of the CVD reactor, we combine the history data with the specific parameters to construct a simulator. A random search algorithm is used to determine the optimal feeding parameters. With the simulator, the polysilicon productivity can be increased by 704 kg, and the power consumption reduced by 3.8 kWh/kg. © 2022, The Authors. All rights reserved.

关键词： Simulators

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Parallel Medical Imaging: An ACP-Based Approach for Intelligent Medical Image Recognition with Small Samples

Parallel Medical Imaging: An ACP-Based Approach for Intellig...

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

作者： Tianyu Shen Chao Gou Jiangong Wang Jun Huang Yonglan He Huadan Xue Zhengyu Jin Fei-Yue Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Guangzhou China Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing China

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the “medical small sample”, the Computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis.

关键词： Deep learning Visualization Image recognition Annotations Digital twin Conferences Computational modeling

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Guided Crowd Evacuation:Approaches and Challenges

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1081-1094页

作者： Min Zhou Hairong Dong Petros A.Ioannou Yanbo Zhao Fei-Yue Wang the State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China the Department of Electrical Engineering University of Southern CaliforniaLos AngelesCA 90089-2560 USA the Center for Advanced Transportation Technologies University of Southern CaliforniaLos AngelesCA 90089 USA the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the Research Center of Computational Experiments and Parallel Systems the National University of Defense TechnologyChangsha 410073China

Evacuation leaders and/or equipment provide route and exit information for people and guide them to the expected destinations, which could make crowd evacuation more efficient in case of emergency. The purpose of this paper is to provide an overview of recent advances in guided crowd evacuation. Different guided crowd evacuation approaches are classified according to guidance approaches and technologies. A comprehensive analysis and comparison of crowd evacuation with static signage, dynamic signage, trained leader, mobile devices, mobile robot and wireless sensor networks are presented based on a single guidance mode perspective. In addition, the different evacuation guidance systems that use high-tech means such as advanced intelligent monitoring techniques, AI techniques, computer technology and intelligent inducing algorithms are reviewed from a system’s perspective. Future researches in the area of crowd evacuation are also discussed.

关键词： Auxiliary equipment evacuation guidance system (EGS) guided crowd evacuation leader signage

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Development and Evaluation of a 7-DOF Haptic Interface

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 261-269页

作者： Jian-Long Hao Xiao-Liang Xie Gui-Bin Bian Zeng-Guang Hou Xiao-Hu Zhou Key Laboratory of Management and Control for Complex Systems Institute of Automation University of Chinese Academy of Sciences Beijing 100190 China IEEE

With the development of human robot interaction technologies, haptic interfaces are widely used for 3 D applications to provide the sense of touch. These interfaces have been utilized in medical simulation, virtual assembly and remote manipulation tasks. However, haptic interface design and control are still critical problems to reproduce the highly sensitive touch sense of humans. This paper presents the development and evaluation of a7-DOF(degree of freedom) haptic interface based on the modified delta mechanism. Firstly, both kinematics and dynamics of the modified mechanism are analyzed and presented. A novel gravity compensation algorithm based on the physical model is proposed and validated in simulation. A haptic controller is proposed based on the forward kinematics and the gravity compensation algorithm. To evaluate the control performance of the haptic interface, a prototype has been implemented. Three kinds of experiments: gravity compensation, static response and force tracking are performed respectively. The experimental results show that the mean error of the gravity compensation is less than 0.7 N and the maximum continuous force along the axis can be up to 6 N. This demonstrates the good performance of the proposed haptic interface.

关键词： Dynamic modeling evaluation haptic interface impedance control

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Milestones in Autonomous Driving and Intelligent Vehicles Part I: control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

arXiv

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arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The College of Artificial Intelligence Xi'an Jiaotong University China The School of Mechanical Engineering Tsinghua University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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