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Chinese automation Congress (CAC)

作者： YanQiong Jin XuJun Guan Hai Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Science and Technology on Aircraft Control Laboratory Beihang University Beijing China

In this paper, we propose a novel method to detect rectangular and triangular traffic signs in natural scenes according to the geometric features of the rectangle and triangle traffic signs. It is a Gradient-direction-based method that mainly includes four stages: gradient information classification, filter templates-based line segments with different orientation extraction, structural corners detection and rectangle/triangle detection. The extracted corners are categorized into different sets according to the orientation of the line segments, by which we can quickly extract rectangle and triangle traffic signs in image. And the experimental results demonstrate under different conditions are shown that the proposed method can detect rectangular and triangular traffic signs in natural scenes efficiently and robustness.

关键词： Image segmentation Feature extraction Image edge detection Image color analysis Detection algorithms Shape Gray-scale

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Simulation Analysis of Complex Aluminium Alloy Structural Component of High-Speed Train

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IOP Conference Series: Materials Science and engineering 2021年第3期1043卷

作者： Wenxue Qian Xianhai Zeng Shuanghui Huang Xiaowei Yin School of Mechanical Engineering and Automation Northeastern University Shenyang 110819 P. R. China Key Laboratory of Vibration and Control of Aero-Propulsion System of Ministry of Education Northeastern University Shenyang 110819 China School of Mechanical Engineering Shenyang Institute of Engineering Shenyang 110136 P. R. China

High-speed trains are now the most important medium- and long-distance mode of transport in many countries, and green economy is one of the important directions of high-speed train design. Aluminum alloy material is widely used in various structural parts of high-speed trains due to its advantages of high strength and low density, etc. As the speed of high-speed trains is getting faster and faster, the load on the car body structure is getting bigger and bigger, the safety, reliability and longevity of aluminum alloy structure has received wide attention. In this paper, the stress distribution, maximum stress value and maximum stress point position of aluminum alloy structural members of high-speed trains are analyzed in detail based on the real load situation, and the influence of stress level on train operation is analysed. The above work has certain reference significance for the design of high-speed train.

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A novel fault classification method based on neural network in smart grid

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International Journal of Electrical engineering 2018年第1期25卷 1-7页

作者： Zhang, Tong Sun, Lanxiang Liu, Jianchang Yu, Haibin College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System CAS Shenyang China University of Chinese Academy of Sciences Beijing China

This paper presents a back propagation (BP) neural network method to identify fault types and phases in the smart grid with renewable sources. A fault classification method based on the BP neural network is used to identify patterns of voltage and current measured with phasor measurement unit (PMU). The voltage and current values are decomposed, and then they are used as the input matrix of the BP neural network, which identifies the fault type and fault phasor. All the common short-circuit faults occurred are analyzed, BP neural network method is proposed for fault classification in a power active distribution network. Simulation results show feasibility and usefulness of the BP neural network method, which provides good classification performances.

关键词： Smart power grids

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An approach to extract state information from multivariate time series

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Journal of Computers (Taiwan) 2020年第6期31卷 1-11页

作者： Gui, Jun Zheng, Zeyu Qin, Zhaobo Jia, Danping Gao, Yuan Liu, Zhi Yao, Qingfeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang110016 China School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China

system behaviors could be recorded as multivariate time series by smart sensors. It is challenging to interpret such high-dimensional dataset with a one-dimensional temporal state sequence. In this paper, we propose a new approach DBSE (Distribution-based States Extraction) which is based on statistical and clustering analysis. State extraction problem could be resolved through using distribution parameters to describe each subsequence in multivariate time series and applying clustering method to extract states based on distribution similarity in order to obtain a temporal state sequence and information of each state. We validate DBSE and demonstrate how to use DBSE in real-world by extracting state information from a wearable sensor dataset (PAMAP2_Dataset). By comparing DBSE with TICC (Toeplitz Inverse Covariance-based Clustering) and FCM (Fuzzy C-means Clustering), the new approach is more accurate and effective. Moreover, DBSE is also expected to facilitate future behavior analysis. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Extraction

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Digital Quadruplets for Cyber-Physical-Social systems based Parallel Driving: From Concept to Applications

arXiv

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arXiv 2020年

作者： Liu, Teng Yang, Xing Wang, Hong Tang, Xiaolin Chen, Long Yu, Huilong Wang, Fei-Yue State Key Laboratory of Mechanical Transmissions College of Automotive Engineering Chongqing University Chongqing400044 China Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Vehicle and Mobility Tsinghua University Beijing China Waytous Inc. Qingdao Shandong266109 China Department of Mechanical and Mechatronics Engineering University of Waterloo N2L 3G1 Canada State Key Laboratory for Management and Control of Complex System Institute of Automation Beijing China

Digital quadruplets aiming to improve road safety, traffic efficiency and driving cooperation for future connected automated vehicles are proposed with the enlightenment of ACP based parallel driving. The ACP method denotes Artificial societies, Computational experiments and Parallel execution modules for cyber-physical-social systems. Four agents are designed in the framework of digital quadruplets: descriptive vehicles, predictive vehicles, prescriptive vehicles and real vehicles. The three virtual vehicles (descriptive, predictive, and prescriptive) dynamically interact with the real one in order to enhance the safety and performance of the real vehicle. The details of the three virtual vehicles in the digital quadruplets are described. Then, the interactions between the virtual and real vehicles are presented. The experimental results of the digital quadruplets demonstrate the effectiveness of the proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Vehicles

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Neural Network control Using Composite Learning for USVs with Output Error Constraints

Neural Network Control Using Composite Learning for USVs wit...

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International Conference on Unmanned systems and Artificial Intelligence

作者： Puyong Xu Chenguang Yang Shi-Lu Dai Zhaoyong Mao Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Unmanned System Research Institute Northwestern Polytechnical University Xi'an China

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

In this paper, by focusing on trajectory tracking control of unmanned surface vessel (USV), we present a control method considering uncertain dynamics and output error constrains. Firstly, by using the properties of tan-type barrier Lyapunov function (BLF), the output tracking error can be constrained. Secondly, we use radical basis function neural network (RBF NN) to approximate the uncertain dynamics. Considering that the estimated parameters convergence cannot be achieved in the absence of persistent excitation (PE) conditions, the composite learning update law of the weight matrix in the NN is adopted to guarantee the parameters convergence under interval excitation (IE) conditions which is easier to reach. In simulation studies, it is proven that the USV have good ability to follow the pre-designed trajectory with small tracking error and the parameters convergence can be ensured.

关键词： unmanned surface vessel (USV) barrier Lyapunov function neural network composite learning Unmanned Surface Vessel Neural network outlet error network control control Methods neural network control follow error Converge Learning

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Intelligent Physical Attack Against Mobile Robots With Obstacle-Avoidance

arXiv

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arXiv 2019年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

The security issue of mobile robots has attracted considerable attention in recent years. In this paper, we propose an intelligent physical attack to trap mobile robots into a preset position by learning the obstacle-avoidance mechanism from external observation. The salient novelty of our work lies in revealing the possibility that physical-based attacks with intelligent and advanced design can present real threats, while without prior knowledge of the system dynamics or access to the internal system. This kind of attack cannot be handled by countermeasures in traditional cyberspace security. To practice, the cornerstone of the proposed attack is to actively explore the complex interaction characteristic of the victim robot with the environment, and learn the obstacle-avoidance knowledge exhibited in the limited observations of its behaviors. Then, we propose shortest-path and hands-off attack algorithms to find efficient attack paths from the tremendous motion space, achieving the driving-to-trap goal with low costs in terms of path length and activity period, respectively. The convergence of the algorithms is proved and the attack performance bounds are further derived. Extensive simulations and real-life experiments illustrate the effectiveness of the proposed attack, beckoning future investigation for the new physical threats and defense on robotic systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile robots

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Research on model based design method of permanent magnet synchronous motor servo system based on FPGA

Research on model based design method of permanent magnet sy...

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International Conference on Mechanical, Electric and Industrial engineering, MEIE 2018

作者： Yu, Shimao Zhang, Tao Zhang, Hualiang Zeng, Peng Chen, Xiyou Key Laboratory of Industrial Control Network and System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

The ac servo system plays an increasingly important role in modern society. The ac servo drive technology based on FPGA becomes important research direction for its advantages of fast processing speed and flexible designing. A permanent magnet synchronous motor servo control system based on FPGA was model-based designed in this paper. It built three-closed-loop servo control model in Simulink which can simulate the performance of the algorithm, and it completed automatic code generation and configuration in Xilinx Zynq-7000. In addition, it built a complete set of permanent magnet synchronous motor servo control hardware platform. This paper proposes an integrated design and simulation method based on FPGA servo control to improve the efficiency of system design. © Published under licence by IOP Publishing Ltd.

关键词： Field programmable gate arrays (FPGA)

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Establishment and Optimization of Gas Flow Prediction Model for Annealing Furnace Based on GA-SVM 37

Establishment and Optimization of Gas Flow Prediction Model ...

引用

37th Chinese control Conference, CCC 2018

作者： Qu, Qilin Yuan, Yan Yali, Jin Zhang, Yongyue China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China School of Information Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789881563941

T0616. The temperature accuracy of the steel coil is directly affected the production quality of the steel plate. In the annealing furnace temperature control system, strip temperature control is mainly decided by various furnace annealing furnace gas flow in the area, so setting up a precise model between gas flow and temperature has become an important problem of the control system of annealing furnace. In this paper, SVM is applied to the prediction model of the gas flow in the annealing furnace since it's suitable for nonlinear and high dimensional problems as a machine learning method. In view of that the choice of C, G and the model parameters of the SVM directly affect the prediction precision of the model, this paper uses GA to optimize these two parameters. Finally, we use the data of field production as training and testing samples to establish and verify the model. The experimental results showed that the established model well achieved the requirements of the accuracy of the target. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Flow of gases

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Joint Beam Guardrail Detection and Tracking by Lidar for Real-Time Applications

Joint Beam Guardrail Detection and Tracking by Lidar for Rea...

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Chinese automation Congress (CAC)

作者： Hao Zhu Fangrong Chen Bin Guo Automotive Electronics and Embedded System Engineering Research Center Chongqing University of Posts and Telecommunications Chongqing China Automotive Electronics and Embedded System Engineering Research Center and Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education College of Automation Chongqing University of Posts and Telecommunications Chongqing P. R. China

This paper presents a robust and real-time guardrail detection and tracking algorithm, based on the three-dimensional information obtained from lidar sensor. Height feature and corner feature are used to extract guardrail feature points out in guardrail detection process, while a cluster method is used to filter the useless guardrail points. Furthermore, an adaptive region of interest (AROI) is proposed to improve the efficiency and accuracy of the detection algorithm. To determine the boundary of AROI, an improved Kalman filter algorithm is proposed to track the feature point set on both sides of the road at the same time in each frame. The whole system works in real-time and achieves detection accuracy above 96% on a test database.

关键词： Feature extraction Laser radar Clustering algorithms Radar tracking Roads Prediction algorithms Real-time systems

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