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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Meng Zheng Bo Yang Wei Liang Haibin Yu Lin Chen Laboratory of Industrial Control Network and System Shenyang Institute of Automaton Shenyang France Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang France University of Chinese Academy of Sciences Beijing France Laboratoire de Recherche en Informatique (LRI) University of Paris-Sud XI Orsay France

This paper proposes an adaptive frequency hopping (AFH) approach that allows Industrial Wireless Sensor Networks (IWSNs) to cognitively switch working channels for high transmission reliability. Assuming the communication spectrum state follows a Markov Process (MP), we build a theoretical AFH framework based on the theory of Markov Decision Process (MDP). With this decision-theoretic framework, we can achieve an AFH strategy that maximizes the expected cumulative transmission reliability over a finite horizon. Judging the high computational complexity of the proposed MDP model, we further propose a myopic AFH with reduced complexity by assuming that each channel evolves independently. Without additional computation burdens or control messages exchange between sensors, the proposed AFH strategies are centrally computed by the network manager. Simulations finally demonstrate the efficiency of the proposed AFH strategies.

关键词： Wireless sensor networks Sensors Reliability Wireless communication Computational modeling Complexity theory Ad hoc networks

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Aspect-Ratio and Scale Aware Rotated Defect Detection for Fiber-reinforced Composites

Aspect-Ratio and Scale Aware Rotated Defect Detection for Fi...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Yuanye Xu Yuanhao Liu Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. This paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. The network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods.

关键词： Deep learning Stacking Detectors Optical fiber networks Feature extraction Product design Quality assessment

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High-Accuracy Pose Estimation for 3D Reconstruction Considering the Manipulator Motion Constraints with an Eye-in-Hand RGB-D Camera

High-Accuracy Pose Estimation for 3D Reconstruction Consider...

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Chinese control Conference (CCC)

作者： Yanling Zhou Xuebo Zhang Runhua Wang Shiyong Zhang Mingxing Yuan Jing Yuan Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin P. R. China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin P. R. China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education

Three-dimensional(3D) environmental reconstruction of the manipulator workspace is a prerequisite to enable autonomous collision-free path planning and subsequent task execution, wherein the positioning accuracy of the equipped sensor is one key issue to determine the performance of 3D reconstruction. In this paper, an eye-in-hand RGB-D camera is used to complete the manipulator workspace scanning and a novel high-accuracy pose estimation method for 3D reconstruction is proposed, which not only considers the geometric and photometric constraints constructed from the visual information used in traditional methods, but also considers the manipulator motion constraints since the positioning accuracy of the manipulator is usually high. Based on above three constraints, three error functions are constructed respectively and fused with different weights to form an integrated objective function. Then, the problem is converted into solving the least squares problem by the Gaussian Newton nonlinear iterative method, which involves the computation of Jacobian matrices and residuals of the three error functions. To this end, the mathematical derivation of their formulas considering the manipulator motion constraints are given in detail. By qualitative and quantitative experimental comparison with traditional methods, the proposed method can greatly improve the accuracy of pose estimation and 3D reconstruction, which always maintains the error within 3 cm, and show good robustness even in the challenging environments with weak texture or geometric features. (Supplemented video link: https://***/FkFTxssnWms)

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Refining a Randomized Post-optimization Method for Covering Arrays

Refining a Randomized Post-optimization Method for Covering ...

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IEEE International Conference on Software Testing, Verification and Validation Workshops

作者： Xiaohua Li Zhao Dong Huayao Wu Changhai Nie Kai-Yuan Cai CAS Academy of Mathematics and System Science Beijing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of automatic control Beijing University of Aeronautics and Astronautics Beijing China

Combinatorial testing is an efficient technique that reveals the faults caused by parameters interaction in software systems. It uses covering arrays as test suites to avoid the combinatorial explosion of parameter values. However, the construction of covering arrays with minimum size is still a challenging problem. This is because most of the existing methods to construct covering arrays result in extensive repetition of coverage. To address this challenging problem, some researchers proposed a randomized post-optimization method to reduce the number of rows in a covering array that has been already constructed. It repeatedly adjusts the array without loss of coverage. However, it seems that this method is not efficient as desired. In order to improve its efficiency, we propose a refined algorithm to replace the randomized algorithm adopted in the method. This avoids the local optima problem that arises in randomized algorithm. Experimental results are presented to confirm the benefits of the refined algorithm. In the cases of CA(N;6, k, 2) (38 ≤ k ≤ 50) generated by IPO, this refined algorithm not only reduces more number of rows than the randomized algorithm, but also produces covering arrays that are smaller than previously best known ones. Furthermore, we provide a reasonable estimator to predict the extent to which covering arrays can be improved by this refined algorithm.

关键词： Prediction algorithms Testing Upper bound Optimization Conferences Software

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Obstacle avoidance path planning for six-DOF manipulator based on voronoi diagram

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Journal of Information and Computational Science 2014年第18期11卷 6663-6671页

作者： Zhang, Guansheng Zhang, Qinghua Zhang, Yi Research Center of Intelligent System and Robot Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China School of Mathematics and Physics Chongqing University of Posts and Telecommunications Chongqing China

In order to solve obstacle avoidance for manipulator in static environment, a method of obstacle avoidance path planning for six-DOF manipulator based on the Configuration space (C-space) and Voronoi diagram is proposed in this paper. Obstacles in the workspace are mapped to the C-space at first. Several Voronoi cells are chosen from the obstacles and the path map based on Voronoi diagram in the C-space is generated, then the obstacle avoidance path planning from the starting point to the target point is completed. Finally, the experiment results show that compare to obstacle avoidance path planning for manipulator based on A* algorithm, this method has lower search cost and ensures manipulator keep a greater distance with obstacles.

关键词： Motion planning

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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PID parameters optimization research for hydro turbine governor by an improved fuzzy particle swarm optimization algorithm

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Open Electrical and Electronic Engineering Journal 2016年 10卷 101-117页

作者： Chen, Gonggui Yangwei, Du Yanyan, Guo Shanwai, Huang Lilan, Liu Key Laboratory of Industrial Wireless Network and Networked Control of Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Research Center on Complex Power System Analysis and Control Chongqing University of Posts and Telecommunications Chongqing400065 China The Department of Locomotive and Vehicle Engineering Wuhan Railway Vocational College of Technology Wuhan430205 China

Parameter optimization of water turbine regulating system (WTRS) is decisive in providing support for the power quality and stability analysis of power system. In this paper, an improved fuzzy particle swarm optimization (IFPSO) algorithm is proposed and used to solve the optimization problem for WTRS under frequency and load disturbances conditions. The novel algorithm which is based on the standard particle swarm optimization (PSO) algorithm can speed up the convergence speed and improve convergence precision with combination of the fuzzy control thought and the crossover thought in genetic algorithm (GA). The fuzzy control is employed to get better dynamics of balance between global and local search capabilities, and the crossover operator is introduced to enhance the diversity of particles. Two different types of WTRS systems are built and analyzed in the simulation experiments. Furthermore, the sum of regulating time and another number that is the integral of sum for absolute value of system error and the squared governor output signal is considered as the fitness function of this algorithm. The simulation experiments for parameter optimization problem of WTRS system are carried out to confirm the validity and superiority of the proposed IFPSO, as compared to standard PSO, Ziegler Nichols (ZN) algorithm and fuzzy PID algorithm in terms of parameter optimization accuracy and convergence speed. The simulation results reveal that IFPSO significantly improves the dynamic performance of system under all of the running conditions. © Gonggui et al.

关键词： Fuzzy control

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Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

Distributed Computation Offloading using Deep Reinforcement ...

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IEEE International Conference on Communications in China (ICCC)

作者： Chen Chen Zheng Wang Qingqi Pei Ci He Zhibin Dou State Key Laboratory of Integrated Services Networks Xidian University Xi'an P.R.China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing China The Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

关键词： Computational modeling Simulation Reinforcement learning User experience Delays Internet Task analysis

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Mixed time synchronization method for industrial Wireless control Networks

Mixed time synchronization method for industrial Wireless Co...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Zhaowei Wang Peng Zeng Meng Zheng Dong Li Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning CN

Time synchronization plays an important role in Industrial Wireless control Networks (IWCNs). To meet the demand for IWCNs, this paper presents a novel mixed time synchronization method, which combines the characteristics of the centralized time synchronization methods and the distributed time synchronization methods. The method could synchronize the network nodes to a given time source. And the models of logical clock and hardware clock have been built. At first, according to the maximum consensus theory, it realizes the synchronization of the logical time between the neighbor nodes. Then based on the revised information of logical clock, which is from the source node, ordinary nodes achieve synchronization with the hardware time of the source node. Simulation results show that the proposed method yields a faster convergence rate and higher synchronization precision in comparison to existing works.

关键词： Synchronization Hardware Clocks Convergence Wireless communication Wireless sensor networks Accuracy

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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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