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Chinese control Conference (CCC)

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds.

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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Chinese automation Congress (CAC)

作者： Tao Zhong Yahui Gan Zao Han Huan Gao Ang Li Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Automation Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments.

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites

Real-time In-situ Defect Detection in 3D Printing of Continu...

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

作者： Zhijian Yue Zhen Shen Xiangyang Dong Qihang Fang Weixing Wang Xisong Dong Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach.

关键词： Training Solid modeling Accuracy Attention mechanisms Training data Three-dimensional printing Real-time systems Quality assessment Optical fiber testing Defect detection

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Research on Multiple Automated Guided Vehicles Path Planning to Reduce Default Time 2

Research on Multiple Automated Guided Vehicles Path Planning...

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2nd IEEE International Conference on Data Science and Computer Application, ICDSCA 2022

作者： Liu, Yiyang Yan, Shuaihua Hou, Zheng Zhao, Yang Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Kunshan Intelligent Equipment Research Institute Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China College of Information Science and Engineering Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

This paper studies the path planning problem of multiple automated guided vehicles (multi-AGV) in a matrix manufacturing workshop. We proposed the multi-AGV path planning problem with default time, which is a variant of the classic path planning problem. In order to ensure the timely completion of each task, the default time of the task is explicitly considered. The goal is to obtain a conflict-free path with the minimum total default time, total time, and total path length. For these three goals, this problem is NP-hard. In order to solve this problem better, we established a multi-objective optimization model of total default time, total time and total path length of multi-AGV in flexible manufacturing environment. This paper proposes a multi-AGV path planning method that integrates game theory and A∗ algorithm. This method regards each AGV as a participant in the game model, through the default time obtained from each game dynamically adjusts the probability of each AGV planning the next path, and uses game theory to help the system dynamically select the most suitable AGV to plan the next path to improve the on-time delivery rate of tasks. Finally, the simulation results show that the multi-AGV path planning method integrating game theory and A∗ algorithm further reduces the total default time, total time and total path length of all AGVs. © 2022 IEEE.

关键词： Motion planning

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Energy-aware Trajectory Optimization for UAV-enabled wireless powered WSN

Energy-aware Trajectory Optimization for UAV-enabled wireles...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Feihang Qiu Jing Guo Huineng Diao Xu Zhang School of Mechatronic Engineering and Automation Foshan University Foshan China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Unmanned Aerial Vehicle enabled (UAV-enabled) wireless powered Wireless Sensor Networks (WSN) provide an effective way to deploy massive and passive sensors for environmental monitoring in harsh environments. This study focuses on maximizing the total energy received by all Sensor Nodes (SNs) through the optimization of the UAV trajectory. An energy-aware smoothing trajectory method is proposed by considering the dynamic energy requirements of SNs. First, the optimization problem (P1) is formulated according to the mathematical model. Second, the B-spline method is introduced to turn the smoothing problem into a solvable problem (P2). Finally, problem (P2) is solved under the constraints to obtain the trajectory solution. Simulation results indicate the proposed method can adjust the overall trajectory according to the SNs demands. Meanwhile, according to the wake-up distance and energy-aware, the trajectory of some nodes can be locally adjusted without affecting other trajectory segments.

关键词： Wireless communication Wireless sensor networks Smoothing methods Simulation Autonomous aerial vehicles Mathematical models Trajectory

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A Test Suite and An Optimizer for Dietary Nutrition Optimization Problem: From Constrained Many-Objective Perspective

A Test Suite and An Optimizer for Dietary Nutrition Optimiza...

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Congress on Evolutionary Computation

作者： Yaqi Ti Changhe Li Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

With increasing people who suffer from diet-related diseases, providing suggestions for personal daily nutrient-dense intake is highly expected. However, current dietary nutrition models are less precise, and dietary nutrition optimizers usually fail to give satisfactory solutions. Therefore, we construct a constrained many-objective nutrition model with more precise nutrient assessments and a scalable constrained many-objective benchmark set. This test suite has great flexibility in evaluating algorithms' performance on high dimensional search and objective spaces with some feasible region fragments. We also propose a kd-tree based dynamic constrained many-objective evolutionary algorithm to search for customized food combinations according to personal daily consumption and intake preference. Experiments show that our algorithm has better diversity maintenance ability in high dimension space.

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Bit Bounce Detection for Drilling Process Based on Multi-Feature Graph and Graph Convolutional Network

Bit Bounce Detection for Drilling Process Based on Multi-Fea...

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Annual Conference of Industrial Electronics Society

作者： Peng Zhang Wenkai Hu Weihua Cao Min Wu Jiandong Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao Shandong Province China

Prompt detection of bit bounce can prevent serious incidents and is of great importance for safe and efficient deep geological drilling. In the early stage of bit bounce, signal changes are relatively weak. In addition, there are differences in the topological relationships of samples at different time instances in normal state and bit bounce. These factors present a challenge to timely and accurate bit bounce detection. Therefore, this paper proposes a bit bounce detection method based on multi-feature graph and graph convolution networks. A multi-feature graph construction method using process variables, mean value, Mahalanobis distance, and Euclidean distance is proposed, and a two-layer graph convolutional network is designed to realize deep feature extraction and incident detection. The effectiveness and superiority of the proposed method are demonstrated by a real drilling industrial case.

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on decision and control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation intelligent sensors

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Resource Scheduling Base on Bayesian Cramer-Rao Lower Bound for Multi-Target Tracking in Netted Colocated MIMO Radar systems

Resource Scheduling Base on Bayesian Cramer-Rao Lower Bound ...

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第40届中国控制会议

作者： Sijian Liao Zhihong Peng Junqi Cai School of Automation Beijing Institute of Technology China State Key Laboratory of Intelligent Control and Decision of Complex System

In this paper, an effective joint beam selection and power allocation（JBSPA） method is proposed to solve the online resource scheduling problem for multi-target tracking in distributed MIMO radar system. Overall target tracking performance can be improved by optimizing radar system beam selection and power allocation, since each MIMO radar can generate a timevarying beam, and adjust the quality of the observed signal by controlling the transmit power of each beam. In our method the feedback information of the target tracking period is used to complete the optimal radar-targets assignment and power allocation,the objective function is formulated based on the Bayesian Cramér-Rao lower bound, as the formulated JBSPA problem is nonconvex, a three steps solution method based on reward matrix is proposed to solve it effectively. Numerical results show that the proposed JBSPA method can deliver better performance than the competitors in overall MTT performance.

关键词： Resource Scheduling BCRLB Target Tracking Power Allocation MIMO Radar systems

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Decentralized Dynamic Event-triggered Output-feedback control of Stochastic Non-triangular Interconnected systems with Unknown Time-varying Sensor Sensitivity

arXiv

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arXiv 2024年

作者： Sun, Libei Song, Yongduan Lv, Maolong Chongqing Key Laboratory of Intelligent Unmanned Systems School of Automation Chongqing University Chongqing400044 China College of Air Traffic Control and Navigation Air Force Engineering University Xi’an710051 China

This study addresses the intricate challenge of decentralized output-feedback control for stochastic non-triangular nonlinear interconnected systems with unknown time-varying sensor sensitivity in a dynamic event-triggered context. The presence of stochastic disturbances, non-triangular structural uncertainties, and evolving sensor sensitivity distinguishes this problem of global asymptotic stability from conventional event-triggered control scenarios. Existing event-triggered control approaches with static event conditions encounter difficulties in simultaneously ensuring zero tracking/stabilization error and preventing the occurrence of Zeno behavior. In this work, we develop a novel solution to address this complex issue. Firstly, we establish a linear relationship between the state vector of each interconnected subsystem and two error vectors through a unique coordinate transformation. This transformation effectively handles the complexities introduced by non-triangular structural uncertainties. Secondly, we introduce a decentralized dynamic event-triggered output-feedback control strategy, which involves a state observer and a decentralized output-feedback controller. Unlike conventional event-triggered control methods with static event conditions, this strategy formulates a modified clock-based dynamic triggering mechanism by introducing an auxiliary variable that evolves based on predicted plant state values, while utilizing a clock variable to guarantee the existence of a positive lower bound on inter-execution times. Rigorous Lyapunov analysis confirms the global asymptotic stability in probability of the closed-loop system, with the states and the output of each local subsystem converging to the equilibrium at the origin in probability. Additionally, the existence of a minimal dwell-time between triggering instants is guaranteed. Finally, we substantiate the advantages and efficiency of the proposed algorithm through comprehensive numerical simulations. Co

关键词： Stochastic systems

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