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8th International Workshop on Advanced Computational Intelligence and intelligent Informatics, IWACIII 2023

作者： Rehman, Zia Ur Wang, Xin Alsumeri, Abdulrahman Abdo Ali Khan, Malak Abid Ali Ma, Hongbin State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819975891

Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Lithium batteries

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Medical Image Segmentation Using Mean Shift Algorithm and General Edge Detection

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IFAC Proceedings Volumes 2011年第1期44卷 9656-9661页

作者： Jinghua Lu Jie Chen Juan Zhang Lihui Zou School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China (Tel: 86-010-68913795)

Abstract The segmentation of the medical image faces the challenges of the existence of large number of diverse structures of human anatomy and inevitable artifacts induced from the imaging procedure. In this paper we treat some structures and artifacts as general edge features, and introduce the edge information into the mean shift segmentation algorithm in both clustering and the fusion steps. Considering the medical images as two dimensional signals, the general edge can be detected and described with its local spatial frequency properties. The segmentation results show the improvement in preserving the completeness of details while sketching the overall structures.

关键词： image segmentation signal processing medical application

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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第42届中国控制会议

作者： Malak Abid Ali Khan Hongbin Ma Ying Jin Jingxiang Ma Zia Ur Rehman Mizanur Rahman School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products,and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its *** technologies provide connectivity and a fast-updating system to eliminate the paper-based *** is one of the contenders to design the system for electronic shelf labels(ESLs).In this paper,LoRa has been used to minify data losses and guarantee the successful decoding of the carrier *** data parallelism at the network server(NS) is used to distribute the data packets among the gateways(GWs) for concurrent transmissions to the end devices(EDs).The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and *** data rate(DR) and spreading factors(SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated *** orthogonality principles follow industrial,scientific,and medical regulations(ISM) to avoid data traffic *** under different duty cycles(DC) and bandwidth(BW) are examined to minify the network saturation and reduce the energy harvesting of the tags.

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Distributed Quadratic Stabilization of Uncertain Linear Multi-Agent systems

Distributed Quadratic Stabilization of Uncertain Linear Mult...

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The 24th Chinese control and decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

作者： Zhongkui Li Zhisheng Duan Lihua Xie Xiangdong Liu Key laboratory for Intelligent Control &Decision of Complex Systems School of Automation Beijing I State Key Lab for Turbulence and Complex Systems Department of Mechanics and Aerospace Engineering School of Electrical and ElectronicEngineering Nanyang Technological University 639798 Singapore Key laboratory for Intelligent Control & Decision of Complex Systems School of Automation Beijing

This paper considers the distributed quadratic stabilization problems of uncertain continuous-time linear multiagent systems with undirected communication topologies. It is assumed that the agents have identical nominal dynamics while subject to different norm-bounded parameter uncertainties, leading to weakly heterogeneous multi-agent systems. A distributed controller is proposed, based on the relative states of neighboring agents and a subset of absolute states of the agents. It is shown that the distributed quadratic stabilization problem under such a controller is equivalent to the H∞ control problems of a set of decoupled linear systems having the same dimensions as a single agent. A two-step algorithm is presented to construct the distributed robust controller, which does not involve any conservatism and meanwhile decouples the feedback gain design from the communication topology. Furthermore, the distributed quadratic H∞ control problem of uncertain linear multi-agent systems with external disturbances is discussed, which can be reduced to the scaled H∞ control problems of a set of independent systems whose dimensions are equal to that of a single agent.

关键词： Multi-agent system distributed control robustness H∞ control parameter uncertainty

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Data-Driven Model for Detection,Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft

Data-Driven Model for Detection,Isolation and Accommodation ...

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第34届中国控制与决策会议

作者： Bemnet Wondimagegnehu Mersha Hongbin Ma School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Ethiopian Airlines' Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10,2019,at Ejere Town,south of Addis Ababa.A faulty angle of attack(AOA) sensor was the cause of the *** airplane accidents have been linked to faulty AOA sensors in the *** majority of the AOA sensor fault detection,isolation,and accommodation(SFDIA) literature relied on linear model-driven techniques,which are not suitable when the system's model is uncertain,complex,or *** multilayer perceptron(MLP) models have been employed in datadriven models in the literature and the effectiveness of deep learning-based data-driven models has not been *** this work,a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is *** proposed model uses a deep learning-based recurrent neural network(RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement,faulty total velocity measurement,and faulty pitch rate *** residual analysis with a fixed threshold is used to detect and isolate faulty AOA *** proposed and benchmark models are trained with the adaptive momentum estimation(Adam) *** show that the proposed model effectively detects,isolates,and accommodates faulty AOA measurements when compared to other data-driven benchmark *** method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： Data-driven modeling intelligent transportation systems Faulty angle of attack measurement Sensor fault Detection Isolation and Accommodation(SFDIA) and Recurrent neural network

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Deadbeat Predictive Current control of Permanent Magnet Synchronous Motors Based on Disturbance Compensation

Deadbeat Predictive Current Control of Permanent Magnet Sync...

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第33届中国控制与决策会议

作者： Wei Shen Huan Liu Luwei Shao Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

The deadbeat predictive current control(DPCC) exhibits strong dynamic performance in the current control of permanent magnet synchronous motor(PMSM),whereas the performance of the traditional DPCC depends on the accuracy of model parameters to a great *** cope with this problem,parameter sensitivity of the traditional DPCC has been analyzed in detail,with a disturbance observation compensation method proposed to improve the robustness of the current control of ***,a method of non-singular terminal sliding mode disturbance observer(NTSMDO) which employs a novel reaching law,combined with the DPCC is put forward to enhance the parameters robustness of the current loop.A harmonic compensation(HC) term has been added to the output of the current control for ignoring the harmonic electromagnetic process in the mathematical model of the PMSM to further promote the performance of the current loop *** and experimental results demonstrate that the proposed method offers robustness against parameters mismatch and control precision compared with the traditional DPCC.

关键词： PMSM Predictive current control NTSMDO Parameter mismatch Harmonic compensation

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Fractional-order Virtual Model control for Trotting Motion of Quadruped Robot

Fractional-order Virtual Model Control for Trotting Motion o...

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第32届中国控制与决策会议

作者： JiangBo Zhao Shicheng Ma Shanshuai Niu Junzheng Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

Virtual model control(VMC) is the most widely-used method for motion control of quadruped *** could decrease the abrupt ground reaction force and realize compliant interaction between foot and ground at the cost of control *** improve the robustness and precision of VMC,we integrate a fractional-order virtual damper with the classical VMC and propose a fractional-order VMC(FOVMC) for quadruped robot trotting *** we provide the implementation details about optimization-based FOVMC parameter tuning method and carry out the trotting experiments on the quadruped robot *** experiment results demonstrate that FOVMC could improve the precision of single-leg motion control and achieve a better whole-body trotting performance than classical VMC.

关键词： Fractional-order calculus Virtual model control Quadruped robot Trotting

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Torque Compensation Model and Touchdown Detection Model of the Hydraulic Quadruped Robot controlled by VMC

Torque Compensation Model and Touchdown Detection Model of t...

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第34届中国控制与决策会议

作者： Ranjiang Wang Jiangbo Zhao Junzheng Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

In this paper,according to the different dynamic models of the support phase and the swing phase,the joint torque compensation of the support phase and the swing phase are given to compensate the joint torque of the hydraulic quadruped robot controlled by the virtual model control(VMC).This compensation can increase the tracking effect of the foot end on the swing trajectory,reduce the elasticity and damping coefficient of the virtual model method,and increase the flexibility of the *** this paper establishes the touchdown detection function based on the joint torque obtained by the Lagrange dynamics equation and the joint torque obtained by the force sensor to determine the touch state of the foot of the quadruped robot to switch the phase of the *** the end of this paper,simulation and experiment prove that the compensation model and touchdown detection function model have feasibility and correctness.

关键词： Quadruped robot Torque compensation Touchdown detection Virtual model control

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A General 6-DOF Industrial Robot Arm control system Based on Linux and FPGA

A General 6-DOF Industrial Robot Arm Control System Based on...

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第30届中国控制与决策会议

作者： Cong Han Hongbin Ma Wenchao Zuo Sunjie Chen Xinghong Zhang School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

This paper presents a general six degrees of freedom(6-DOF) robot arm control system based on Linux and field-programmable gate array(FPGA).The system provides a friendly human-computer interaction *** can complete palletizing,handling and other processes through simple programming of the interactive *** adopt industrial personal computer(IPC) as the controller to accomplish the kinematics calculation,design motion control board connected with the controller and the servo system,and use the servo system to implement the motion of the robot *** kinematics calculation and trajectory generation based on cubic polynomial are developed based on the Linux system,which improves the portability of the *** a normal method is adopted in updating trajectory points to improve *** control board is designed based on FPGA,which with the characteristic of parallel computing raises the running speed of the robot arm.

关键词： 6-DOF industrial robot arm control system human-machine interaction Linux FPGA

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A novel sparse auto-encoder for deep unsupervised learning

A novel sparse auto-encoder for deep unsupervised learning

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Xiaojuan Jiang Yinghua Zhang Wensheng Zhang Xian Xiao State Key Laboratory of Intelligent Control and Management of Complex System at Institute of Automation Chinese Academy of Sciences Beijing

This paper proposes a novel sparse variant of auto-encoders as a building block to pre-train deep neural networks. Compared with sparse auto-encoders through KL-divergence, our method requires fewer hyper-parameters and the sparsity level of the hidden units can be learnt automatically. We have compared our method with several other unsupervised leaning algorithms on the benchmark databases. The satisfactory classification accuracy (97.92% on MNIST and 87.29% on NORB) can be achieved by a 2-hidden-layer neural network pre-trained using our algorithm, and the whole training procedure (including pre-training and fine-tuning) takes far less time than the state-of-art results.

关键词： Training Classification algorithms Data models Principal component analysis Lead

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