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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Fang, Zihan Hou, Yanxu Li, Jun Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southsast University Shenzhen518057 China

ISBN: (纸本)9781665437127

To promote the work capacity and efficiency in a weakly structured logistics sorting scene, a pick-And-Throw method based on reinforcement learning is proposed. First, a D N-based learning algorithm is used to obtain a picking feasibility distribution map for guiding an optimal picking action. Second, Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to train a throwing policy. The throwing policy outputs a throwing velocity of the end-effector of a robot to throw the picked object to a target area. In addition, a memory pool optimization algorithm is also proposed to enhance the convergence of the throwing policy. Experiments are conducted in both simulation and real scenarios and results demonstrate that the proposed method can improve the sorting efficiency and expand the sorting space significantly. © 2021 IEEE.

关键词： Reinforcement learning

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A Parallel Algorithm for Solving General Colored Traveling Salesman Problems 36

A Parallel Algorithm for Solving General Colored Traveling S...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Ding, Penghui Xu, Xiangping Li, Jun Ministry of Education Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

ISBN: (纸本)9781665437127

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. It utilizes colors to differentiate the accessibility of its cities to ward its salesmen. All the existing algorithms for solving CTSP are serial ones. This paper presents for the first time a parallel algorithm framework for solving general CTSP. Then, an improved variable neighborhood search algorithm is proposed by using this algorithm framework. Finally, extensive simulations are conducted in various test cases. The results show that the algorithm proposed is superior to the compared serial algorithms in terms of convergence and solution quality. © 2021 IEEE.

关键词： Traveling salesman problem

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A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise

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Defence Technology（防务技术） 2022年第2期18卷 183-193页

作者： Yu-xing Li Shang-bin Jiao Bo Geng Qing Zhang You-min Zhang School of Automation and Information Engineering Xi’an University of TechnologyXi’an710048China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an710048China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversityMontrealQuebec H3G 1M8Canada

Refined composite multi-scale dispersion entropy(RCMDE),as a new and effective nonlinear dynamic method,has been applied in the field of medical diagnosis and fault *** this paper,we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise,and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor(KNN),termed *** results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise,and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy(MPE)and KNN,multi-scale weighted-permutation entropy(MW-PE)and KNN,and multi-scale dispersion entropy(MDE)and KNN,termed MPE-KNN,MW-PE-KNN,and *** is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective,and can obtain a very high recognition rate.

关键词： Nonlinear dynamic Refined composite multi-scale dispersion entropy(RCMDE) Multi-scale dispersion entropy(MDE) Multi-scale weighted-permutation entropy (MW-PE) Multi-scale permutation entropy(MPE) Classification of ship-radiated noise

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Novel Embedded system Design and Implementation for Automatic Platelet Aggregometer 33

Novel Embedded System Design and Implementation for Automati...

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第三十三届中国控制会议

作者： JU Ben MA Xudong FANG Fang XU Xin DONG Ziquan SINNOWA Medical Science & Technology Co. LTD School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

ISBN: (纸本)9781479947249

The platelet aggregation function analysis is applied in clinical research and application more and more *** this paper,an automatic double-channel platelet analysis system PL12 is designed and *** system framework is based on embedded processor ARM and DSP architecture,in which a signal modulation circuit is *** an analog to digital conversion scheme is proposed and realized for measuring weak pulse signals of blood *** principle of control system and management software is also *** validate the high efficiency,precision and stability of the system.

关键词： double-channel,platelet aggregation,signal acquisition,embedded systems

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Optimization of Wide-Baseline Stereo Matching Based on Multi-view Geometry

Optimization of Wide-Baseline Stereo Matching Based on Multi...

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2024 China Automation Congress, CAC 2024

作者： Xu, Yinhui Shan, Shuo Han, Gongben Fang, Shixiong Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Shenzhen Research Institute Southeast University Shenzhen China

ISBN: (纸本)9798350368604

Stereo vision plays a crucial role in scientific expeditions and facility maintenance. However, fixed-baseline stereo vision lacks accuracy for long-distance measurements, while wide-baseline stereo vision encounters occlusion issues at short distances. In this study, a wide-baseline stereo matching based on multi-view geometric constraints is proposed. Initially, the camera poses of multiple wide-baseline images are estimated, followed by image rectification and depth estimation. Subsequently, the disparity searching is constrained using geometric relationships among the views, resulting in more precise depth maps. Experiments were conducted to verify the superiority of the proposed method. The results indicate that the proposed method achieves an RMSE as low as 0.15 meters, even with invalid depth points. Compared to traditional stereo vision methods, accuracy is improved by approximately 35%, and robustness is significantly enhanced. In the aircraft runway scenario, overall depth map accuracy is higher, and occluded areas are significantly reduced. © 2024 IEEE.

关键词： Stereo vision

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Secure output synchronization of heterogeneous multi-agent systems against false data injection attacks

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Science China(Information Sciences) 2022年第6期65卷 142-154页

作者： Shicheng HUO Dalin HUANG Ya ZHANG School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Cyber Science and Engineering Southeast University

This paper studies secure output synchronization for heterogeneous multi-agent systems against false data injection(FDI) attacks. Both the sensors and actuators of agents may be injected into FDI *** mitigate the malicious impact of these attacks on synchronization performance, a desired reference model,which is combined with an auxiliary system, is first designed for each agent to simulate its normal system dynamics. Then, based on this reference model, a state feedback cooperative controller and a static output feedback cooperative controller, which consist of adaptive compensators, are proposed, respectively. The integrated control protocols can ensure that the output synchronization error is small by adjusting some designed parameters even in the presence of FDI attacks. An illustrative example is employed to demonstrate the effectiveness of the proposed method.

关键词： output synchronization false data injection attacks adaptive compensators heterogeneous multi-agent systems secure control

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Effectiveness of Koopman Autoencoder Predicting Nonlinear system States: Effect of Latent Layer Neuron Number

Effectiveness of Koopman Autoencoder Predicting Nonlinear Sy...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhou, Jun Jia, Yubin Liu, Zhimin Yong, Panxiao Guo, Jun The School of Automation Southeast University Jiangsu Nanjing210096 China The Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9781665465335

Researchers have always been popular with analyzing and controlling nonlinear systems, yet most methods are based on local linearized models. As an alternative, Koopman theory was introduced to solve global linearization problems. Koopman operator is infinite-dimensional and has the ability to globally linearize nonlinear systems without information loss. In this work, a Koopman autoencoder, which combines the Koopman operator and autoencoder network, is used for predicting nonlinear systems. The autoencoder network is designed to find an approximate finite-dimensional Koopman operator. An inverted pendulum dynamic system is used to evaluate the effectiveness of the Koopman autoencoder, and accurate prediction results have been achieved. Furthermore, we discussed the effect of latent layer neuron number in the Koopman autoencoder, prediction results are given, and the best number of latent layer neurons for this system is found. © 2022 IEEE.

关键词： Nonlinear systems

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An Autoencoder with a Memory Module for Video Anomaly Detection 36

An Autoencoder with a Memory Module for Video Anomaly Detect...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Tang, Wei Feng, Yunjian Li, Jun Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

ISBN: (纸本)9781665437127

With the rise of deep convolutional neural networks (CNNs), considerable attention has been paid to video anomaly detection (VAD). Autoencoders are a popular type of framework for VAD, and many existing VAD methods are based on it. However, these methods take an assumption of closed-world VAD, i.e., do not comprehensively consider the diversity of normal patterns. Besides, a competent CNN allows the autoencoder to reconstruct or predict abnormal video frames proficiently, resulting in missing anomalies. To mitigate these drawbacks, we propose an Autoencoder with a Memory Module (AMM) to realize video anomaly detection by predicting video frames. AMM consists of three modules: An encoder, a decoder, and a memory module. First, the consecutive frames are fed into the encoder to yield latent spatial features. Then, the features are utilized to retrieve corresponding memory items in the memory module to generate memory mapping features. Finally, the memory mapping features are adopted in the decoder for predicting the next frame. To match the queries against memory items accurately, we propose a memory triplet loss, which takes into account both size and angle discrepancies between the queries and memory items. At the training stage, AMM utilizes the memory triplet loss, a prediction loss, and multi-scale structure similarity measure. Moreover, the modes of retrieving and updating memory items are ameliorated by a scaled dot product model, which can alleviate vanishing gradient problems to a certain extent. Extensive experiments are conducted on three benchmark public datasets, and the results demonstrate the superior performance of AMM. © 2021 IEEE.

关键词： Forecasting

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Line Feature Based Visual Servo control with TDE for Underwater Vehicle Manipulator systems Target Grasping

Line Feature Based Visual Servo Control with TDE for Underwa...

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2024 IEEE International Conference on Unmanned systems, ICUS 2024

作者： Zhang, Yunfei Bian, Xinyu Sun, Yize Zhang, Zongyu Huang, Hai Yang, Xu National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350384185

Underwater vehicle manipulator systems (UVMSs) is becoming a research hotspot in the field of marine intelligent equipment. Introducing visual feedback equipped with cameras in the loop to guide UVMS to complete the grasping has comprehensive advantages of response speed, work efficiency and intelligence. In this paper, mapping between the feature error and the motion of the UVMS generalized degrees of freedom is established to realize the suspended grasping under the coordinated motion of the vehicle body and the manipulator. Then, a UVMS motion control algorithm based on time delay estimation (TDE) is constructed for visual servo autonomous grasping process, the TDE of which is used to estimate and compensate the lumped interference in real time. The Lyapunov theory is applied to analyze its stability and its control performance is verified through simulation experiments. The simulation results shew the effectiveness of the proposed visual servo control with TED method based on line features. © 2024 IEEE.

关键词： Visual servoing

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Induction motor flux estimation based on Artificial Neural Network left-inversion

Induction motor flux estimation based on Artificial Neural N...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Hao Zhang Xianzhong Dai Key Laboratory of Measurement and Control of Complex system of Engineering Ministry of Education South-East University Nanjing China

This paper presents a new rotor flux estimation algorithm using neural network for induction motor, based on the left-inversion method. Using the standard fifth-order model of the three-phase induction motor in a stationary two axes reference frame, the flux ldquoassumed inherent sensorrdquo is constructed and its left-invertible is validated. The artificial neural network (ANN) left-inversion flux estimator is composed of two relatively independent parts - a static ANN used to approximate the complex nonlinear function and several differentiators used to represent its dynamic behaviors, so that the ANN left-inversion is a special kind of dynamic ANN in essence. The performance of the proposed algorithm is tested through simulation and experiment, proving good behavior in both transient and steady-state operating conditions.

关键词： Induction motors Artificial neural networks Rotors Stators Equations Voltage Inductance Sensor systems Electrical resistance measurement Laboratories

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