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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： He, Tianfang Xu, Aidong Wang, Kai Zeng, Peng Laboratory of Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781839538360

Blockages of pressure transmitter sensing line are a recurring problem in nuclear plants. The traditional solution is to purge them with nitrogen periodically, which is time consuming and radiation intensive. It is thus desirable to monitor the status of sensing lines on-site. The noise analysis method is a promising method for sensing line health monitoring. However, the existing noise analysis methods, which evaluate the sensing line status by analyzing time domain or frequency domain features of process noise signal, are susceptible to working conditions of pressure transmitters. Further, blockage degree of the sensing line can not be determined by existing methods. Therefore, this paper proposes a machine learning based noise analysis method for health monitoring of pressure transmitter sensing line, in which 1-D Convolutional Neural network (CNN) is used to extract features from process noise signals. Experimental results show that the proposed method achieved satisfactory results in diagnosis accuracy, false alarm rate and robustness of the model under different working conditions. Further, blockage degree of sensing line can also be evaluated. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Frequency domain analysis

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Distributed Optimization of Heterogeneous Linear Multi-Agent systems With Unknown Disturbances and Optimal Gain Tuning

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IEEE Transactions on Signal and Information Processing over networks 2025年 11卷 563-576页

作者： Mengmeng Duan Shanying Zhu Ziwen Yang Cailian Chen Xinping Guan State Key Laboratory of Submarine Geoscience Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Key Laboratory for Perception and Control in Industrial Network Systems Shanghai China

In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. To solve this problem, we propose a distributed controller design framework, which reduces the controller design for heterogeneous linear multi-agent systems to the stabilizer design for first-order multi-agent systems. By this framework, we propose two kinds of dynamic controllers under the strong convexity of the objective function and the restricted secant inequality condition, respectively. Based on the optimal condition and singular perturbation analysis technique, we prove that the system converges to the optimal state if the disturbances tend to be constant or vary slowly. To further optimize the performance criterion under system stability and input constraint, we provide an optimal gain tuning algorithm such that the system stability, optimality and feasibility are simultaneously achieved. Numerical examples are provided to illustrate the effectiveness of the theoretical results.

关键词： Optimization Multi-agent systems Linear programming Tuning Heuristic algorithms Real-time systems Stability criteria Signal processing algorithms Symmetric matrices Convergence

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HIDG-based PSO Algorithm: Suspicious Sensor Selection for Power Grid Monitoring system Under Data Integrity Attack 5

HIDG-based PSO Algorithm: Suspicious Sensor Selection for Po...

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5th International Conference on Electrical Engineering and control Technologies, CEECT 2023

作者： Li, Pandeng Liang, Zhihong Yang, Yiwei Dong, Liangyu Suo, Yuhan Hao, Yi Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Beijing Institute of Technology School of Automation Beijing China

ISBN: (纸本)9798350342253

With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data integrity attacks to compromise the measurements of certain sensors, causing the grid monitoring system to fail to grasp the correct system operating status. To solve the NP-hard suspicious sensor selection problem, this paper proposes an efficient attack detection scheduling algorithm, the Particle Swarm Optimization algorithm based on historical information directional guidance (HIDG-based PSO algorithm). The proposed algorithm is utilized with its unique evolutionary mechanism, which reduces the computational power requirements for sensor selection. For the problem of uncertainty in evolutionary algorithms, this paper uses historical information to guide the selection of suspicious sensors at the current moment. The simulation results show that the proposed algorithm can efficiently select suspicious sensors, which will greatly improve the efficiency of attack detection and ensure the security of information fusion of the power grid monitoring system. © 2023 IEEE.

关键词： Scheduling algorithms

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Continuous Perception Garment Classification Based on Optical Flow Variation

Continuous Perception Garment Classification Based on Optica...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, Li Yang, Tong Zhang, Yu Jiang, Rongxin Tian, Xiang Chen, Yaowu Institute of Advanced Digital Technologies and Instrumentation Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control and Technology Zhejiang University 310027 China Zhejiang Provincial Key Laboratory for Network Multimedia Technology Hangzhou310027 China Embedded System Engineering Research Center Ministry of Education of China Hangzhou310027 China

ISBN: (纸本)9781665481090

A novel continuous perception garment classification mechanism is proposed in this paper, with the aim to identify the correct category of the garment from a set of known categories. It has been observed that due to the severe folding and overlapped texture of garments, treating a video of the continuous deformation of cloth as a set of disordered static figures would be ineffective which leads to low classification precision performed by an image-based garment classifier. In contrast, a high-level decision making module that leverages the classification results of each single image would significantly increase the algorithm performance. In this paper, we incorporate the optical flow variation of deformable cloth between consecutive configurations as a representative of how it is traversing within the confidence interval of the image-based classifier. We claim that it is not the number of video frames but the sum of optical flow variation of the garment configuration between consecutive frames having the same category label that constitutes the belief of garment classification. In other words, if two consecutive visual appearances of the garment could be identified as the same category by the image-based classifier, then the more diverged that two configurations are, the more confident that the garment is correctly identified. Experimental comparisons between the state-of-the-art algorithm and the proposed algorithm in a public dataset have been provided which prove the validity of the proposed algorithm. © 2022 IEEE.

关键词： Decision making

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Health monitoring of pressure transmitter sensing line based on convolutional neural network

Health monitoring of pressure transmitter sensing line based...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)

作者： T. He A. Xu K. Wang P. Zeng Laboratory of Industrial Control Network and System State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang People's Republic of China

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Research on stereo vision technology based on improved Region growing method 2

Research on stereo vision technology based on improved Regio...

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Dai, Shilong Xia, Xinghua Zhang, Hualiang School of Information and Control Engineering Shenyang Jianzhu University Shenyang110168 China Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Image segmentation is the cornerstone of image analysis and image processing, its main difficulty is the ill-posedness of image segmentation. The region growing method is the most commonly used method in image segmentation. Its advantages are fast calculation speed, lower algorithm difficulty, and easy understanding. This article uses the area growing algorithm and TOF combined with binocular fusion technology. Discussed how to select the seed points in the region growing method. The algorithm proposed in this paper has high accuracy and high matching quality in the boundary area of the object and the area with large difference in depth. It has both matching time and reliability. It has good results and overcomes the limitations of the active and passive distance methods in the vision system. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image segmentation

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HIDG-based PSO Algorithm: Suspicious Sensor Selection for Power Grid Monitoring system Under Data Integrity Attack

HIDG-based PSO Algorithm: Suspicious Sensor Selection for Po...

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Electrical Engineering and control Technologies (CEECT), Conference on

作者： Pandeng Li Zhihong Liang Yiwei Yang Liangyu Dong Yuhan Suo Yi Hao Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou Guangdong China China Industrial Control Systems Cyber Emergency Response Team Beijing China School of Automation Beijing Institute of Technology Beijing China

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Research on Intrusion Detection Based on BP Neural network

Research on Intrusion Detection Based on BP Neural Network

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Haonan Chen Yiyang Liu Jianming Zhao Xianda Liu College of Information Science and Engineering Northeastern University Shenyang China Chinese Academy of Sciences Industrial Control Network and System Laboratory Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Industrial Control Network and System Laboratory Shenyang Institute of Automation Shenyang China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

The purpose of network security is to prevent the data transmitted over the Internet from being stolen and tampered with, and to ensure the security of the data. It is not only necessary to ensure that the information entering and exiting the network is not stolen or tampered with, but also to ensure the integrity and confidentiality of the information in the information system. The network environment is becoming more and more complex, and the attack methods are becoming more and more diverse. Therefore, intrusion detection systems have some common problems, such as low detection rate and high false alarm rate, and it is difficult to meet the real-time requirements of intrusion detection systems. Currently, deep learning is increasingly used in intrusion detection. In order to solve the problems existing in the current intrusion detection system, this paper studies the application of deep learning in intrusion detection. First, it analyzes the BP neural network (BP-NN) technology, and proposes an improvement method for the shortcomings of the current BP-NN, and finally conducts an empirical analysis. Experimental results show that intrusion detection based on BP-NN has a high accuracy rate, and the false alarm rate and false alarm rate are both at a low level.

关键词： Deep learning Neural networks Intrusion detection Feature extraction Real-time systems Internet Information systems

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Path planning of mobile robot based on improved DDQN 2

Path planning of mobile robot based on improved DDQN

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Yang, Yunxiao Wangjun Zhang, Hualiang Dai, Shilong School of Computer Science Shenyang University of Chemical Technology Shenyang110142 China Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Mobile robots

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Robotic Trajectory Planning for Non-Destructive Testing Based on Surface 3D Point Cloud Data 7

Robotic Trajectory Planning for Non-Destructive Testing Base...

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2021 7th International Forum on Manufacturing Technology and Engineering Materials, IFEMMT 2021

作者： Zhang, Zhen Zhang, Hualiang Yu, Xiaolong Deng, Yongsheng Chen, Zheng School of Mechanical Engineering and Automation Northeastern University Shenyang China Key Laboratory of Industrial Control Network and System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Hangzhou Innovation Institute Beihang University Hangzhou China School of Information Engineering Shenyang University of Chemical Technology Shenyang China

Robotics has been widely used in the field of non-destructive testing in recent years. However, for complex surfaces, manual teaching or offline programming is time-consuming and difficult to ensure high precision for non-destructive testing robot trajectory planning. Therefore, this work proposes a new method to generate non-destructive testing trajectory of the robot based on the pre-processed point cloud data. The workpiece surface is measured by 3D sensor to obtain the point cloud data. The trajectory line on workpiece surface is obtained by slicing pre-processed point cloud data. The dense trajectory points are obtained by isometric discretizing trajectory lines, and then they are compressed by Douglas-Peucker algorithm. The Principal Component Analysis (PCA) method is used to estimate the normal vector of the optimized trajectory points and unify their orientation. The pose of non-destructive testing robot can be obtained by biasing the trajectory points along their normal vectors finally. © Published under licence by IOP Publishing Ltd.

关键词： Nondestructive examination

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