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Chinese Automation Congress (CAC)

作者： Qi Wang Xisong Dong Zhen Shen Gang Xiong Jun Yan Lulu Niu The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The crack is a serious disease of bridges and one of the important topics in the bridge detecting. Compared with the traditional manual methods for measuring crack size directly or indirectly, this paper proposes a new method based on Matlab toolbox for digital calculation of crack images. After preprocessing and detecting the original image, some morphological operation like corrosion and expansion have been used in this paper to improve the calculation accuracy. Relying on the connected region calculation, the stepped line is introduced to approach the real crack and obtain the number of pixels in length and width. Then through some calculations, the true size of the crack can be converted from pixel values, which provides a good basis for determining its hazard level. At the end of this paper, experiment results verify the accuracy and practicability of the method, and this method can be used as a subsequent part of crack identification to obtain crack size information.

关键词： Bridges Matlab Matrices Ultrasonic variables measurement Image edge detection Size measurement Feature extraction

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Bio-inspired Modeling and Position control for Pneumatic Artificial Muscle

Bio-inspired Modeling and Position Control for Pneumatic Art...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hang Fu Yang Liu Jiahao Chen School of Mechanical Engineering University of Science and Technology Beijing Beijing P. R. China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P. R. China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

In this paper, the modeling and position control problem of a class of pneumatic artificial muscle (PAM) is investigated for the bio-inspired robotic manipulator. The system model of PAM is firstly developed based on experiment data identification. Then, the improved particle swarm optimization (PSO) control parameter tuning method is established for the effective position control of PAM. In particular, the concept of information entropy is used during the optimal procedure. In the end, the simulation results are given for demonstrating the effectiveness of the proposed design and control approach.

关键词： Simulation Position control Muscles Particle swarm optimization Information entropy Tuning Optimization

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Distributed Formation of Autonomous Underwater Vehicles with Unreliable Switching Topologies and Transmission Delays

Distributed Formation of Autonomous Underwater Vehicles with...

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Chinese control Conference (CCC)

作者： Chao Ma Wei Wu Yidao Ji Hang Fu School of Mechanical Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper investigates the formation problem of autonomous underwater vehicles with unreliable switching communication topologies and time-varying transmission delays. More precisely, the switching topologies contains achievable and unachievable sub-topologies, which can better describe the practical underwater communication environment. By performing model transformation and constructing appropriate multiple Lyapunov-function method, sufficient conditions are established based on admissible edge-dependent average dwell time, such that the desired formation configuration can be achieved with transmission delays. Finally, an illustrative example is given at last to verify the effectiveness of the main results.

关键词： Topology Switches Delays Network topology Underwater autonomous vehicles Switched systems

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Heuristic rank selection with progressively searching tensor ring network

arXiv

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arXiv 2020年

作者： Li, Nannan Pan, Yu Chen, Yaran Ding, Zixiang Zhao, Dongbin Xu, Zenglin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China Shenzhen China Pengcheng Lab Shenzhen China

Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named Progressively Searching Tensor Ring Network Search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Position control of an Underwater Biomimetic Vehicle-Manipulator system via Reinforcement Learning

Position Control of an Underwater Biomimetic Vehicle-Manipul...

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Data Driven control and Learning systems (DDCLS)

作者： Ruichen Ma Yu Wang Zisen Gao Tianzi Zhao Rui Wang Shuo Wang Chao Zhou University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Institute of Petrochemical Technology Beijing China Naval Research Academy Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

This paper addresses a position control method of an underwater biomimetic vehicle-manipulator system (UBVMS) through reinforcement learning. The system description of the UBVMS with undulating fins is given. Considering the force/torqu generated by undulating fins and hydrodynamic force/torqu, the dynamic model of this UBVMS is established. The position control problem is modeled into a continuous-state, continuous-action Markov decision process (MDP) with a deterministic state transition algorithm based on the dynamic model. To solve this MDP, a reinforcement learning method is presented, which is based on the deep deterministic policy gradient (DDPG) theorem. The simulations of the position control in 5 cases are shown in the end.

关键词： Robots Force Robot kinematics Position control Propellers Reinforcement learning Heuristic algorithms

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A comprehensive survey on image aesthetic quality assessment 18

A comprehensive survey on image aesthetic quality assessment

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18th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2019

作者： Yang, Hongtao Shi, Ping He, Saike Pan, Da Ying, Zefeng Lei, Ling College of Information Engineering Communication University of China Beijing China School of Electrical and Information Engineering Beijing Polytechnic College Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728108018

Image aesthetic quality assessment has demonstrated tremendous success in variety of application domains in recent years. This field has been growing so rapidly that various approaches have been proposed trying to solve this challenging problem. This report presents a comprehensive survey on image aesthetic quality assessment, mainly focus on the contributions and novelties of the existing approaches recently. In this work, we firstly illustrate datasets related to image aesthetics and investigate feature extractions. Then five different aesthetic tasks are reviewed, including aesthetic classification, aesthetic regression, aesthetic distribution, aesthetic factors and aesthetic description. In addition, we reviewed recent applications concerning image aesthetics. Finally, different evaluation criterions in different literatures are summarized. We hope the survey could serve as a comprehensive reference and be useful for those who are interested in exploiting image aesthetic for their research. © 2019 IEEE.

关键词： Surveys

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Defining digital quadruplets in the cyber-physical-social space for parallel driving

arXiv

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arXiv 2020年

作者： Liu, Teng Xing, Yang Chen, Long Cao, Dongpu Wang, Fei-Yue Vehicle Intelligence Pioneers Inc Shandong Qingdao266109 China Qingdao Academy of Intelligent Industries QingdaoShandong266109 China Department of Mechanical and Mechatronics Engineering University of Waterloo N2L 3G1 Canada State Key Laboratory of Management and Control for Complex System Institute of Automation Beijing China

Parallel driving is a novel framework to synthesize the vehicle intelligence and transport automation. This article aims to define the digital quadruplets in parallel driving. In the cyber-physical-social systems (CPSS), based on the ACP method, the names of the digital quadruplets are first given, which are descriptive, predictive, prescriptive and real vehicles. The objectives of the three virtual digital vehicles are interacting, guiding, simulating and improving with the real vehicles. Then, the three virtual components of the digital quadruplets are introduced in detail and their applications are also illustrated. Finally, the real vehicles in parallel driving system and the research process of the digital quadruplets are depicted. The presented digital quadruplets in parallel driving are expected to make the future connected automated driving safety, efficiently and synergistically. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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A Vision-Based Path Planning and Following system for a Miniature Robotic Fish

A Vision-Based Path Planning and Following System for a Mini...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yue Lu Xingyu Chen Zhengxing Wu Yan Yan Junzhi Yu University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Most existing path following algorithms for middle-or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation.

关键词： Fish Robot kinematics Path planning Robot sensing systems Solenoids Magnetic resonance imaging

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Publisher Correction: Ozone pollution mitigation strategy informed by long-term trends of atmospheric oxidation capacity

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Nature Geoscience 2024年 172页

作者： Wenjie Wang Xin Li Ying Liu Sihua Lu Yusheng Wu Shiyi Chen Keding Lu Min Hu Limin Zeng Min Shao Yuanhang Zhang Hang Su Yafang Cheng Ruijing Ni Ulrich Pöschl Zhaofeng Tan Franz Rohrer Andreas Wahner David D. Parrish Cheng Huang Xudong Tian K. M. Leung Liangfu Chen Meng Fan Qiang Zhang State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and Engineering Peking University Beijing China State Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Max Planck Institute for Chemistry Mainz Germany International Joint Laboratory for Regional Pollution Control Ministry of Education Beijing China Collaborative Innovation Centre of Atmospheric Environment and Equipment Technology Nanjing University of Information Science & Technology Nanjing China Institute of Carbon Neutrality Peking University Beijing China Institute for Environmental and Climate Research Jinan University Guangzhou China State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex Shanghai Academy of Environmental Sciences Shanghai China Institut für Energie-und Klimaforschung: Troposphäre (IEK-8) Forschungszentrum Jülich Jülich Germany Key Laboratory of Ecological and Environmental Monitoring Forewarning and Quality Control of Zhejiang Zhejiang Ecological and Environmental Monitoring Center Hangzhou China Environmental Protection Department The Government of the Hong Kong Special Administrative Region Hong Kong China State Key Laboratory of Remote Sensing Science The Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Department of Earth System Science Tsinghua University Beijing China

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Improving Road Detection Results Based on Ensemble Learning and key Samples Focusing

Improving Road Detection Results Based on Ensemble Learning ...

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International Conference on Intelligent Transportation

作者： Siqi Fan Fenghua Zhu Hui Zhang Yisheng Lv Xiao Wang Gang Xiong Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Clouding Computing Center Chinese Academy of Sciences Dongguan Guangdong China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Road detection is fundamental for many applications, especially vision-based autonomous driving systems. To improve the accuracy of the detection results, most of previous research focus on designing feature encoders and classifiers. In this paper, a road detection method is proposed based on ensemble learning and key samples focusing. A road detection network is designed, which integrates classification results based on different feature combinations by weighted voting. The outputs of the network are further processed by morphological transformation. To focus on key samples, a novel loss function is proposed. The loss function can attach importance to hard samples and pay different attention to missed detection and false detection. The method is evaluated on KITTI dataset, and its effectiveness is verified.

关键词： Roads Feature extraction Training Decoding Focusing Bagging Image color analysis

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