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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是211-220 订阅

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Multimode process monitoring based on modified probabilistic linear discriminant analysis 8

Multimode process monitoring based on modified probabilistic...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Liu, Yi Zeng, Jiusun Xie, Lei Lang, Xun Luo, Shihua Su, Hongye Institute of Cyber Systems Control Zhejiang University Hangzhou310027 China College of Metrology Measurement Engineering China Jiliang University Hangzhou310018 China School of Statistics Jiangxi University of Finance and Economics Nanchang330013 China

ISBN: (纸本)9781728114545

this paper focus on developing an effective method to monitor the industrial process with multiple operation conditions. By utilizing the technique of probabilistic linear discriminant analysis (PLDA), the between- and within-class latent variables can extract more useful information. the proposed method, the modified PLDA (MPLDA), transforms the centralized samples into a new type of between-class latent variables. the current mode operation condition can be identified by comparing a series of cosine similarities deduced by the original and the new between-class latent variables. the online monitoring procedures are built on the basis of this mode identification. Unlike the conventional $T^th$ and $Q$ statistics designed for within-class latent variable, the proposed monitoring statistics take both between- and within-class latent variables into consideration. For the model training, the joint updating expectation-maximization (EM) algorithm is developed. the enhanced performance of the MPLDA based method is illustrated by the application of Tennessee Eastman (TE) process. © 2019 ieee.

关键词： Maximum principle

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Fault Diagnosis of Satellites under Variable Conditions based on Domain Adaptive Adversarial Deep Neural Network 10

Fault Diagnosis of Satellites under Variable Conditions base...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Gu, Yuxing Mao, Zehui Yan, Xinggang Liang, Hanyu Liu, Wenjing Liu, Chengrui School of Automation Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Engineering Digital Arts University of Kent Canterbury KentCT2 7NT United Kingdom Beijing Institute of Control Engineering Beijing100190 China Science and Technology on Space Intelligent Control Laboratory Beijing100190 China

ISBN: (纸本)9781665424233

Fault diagnosis of satellite attitude control system is an important task to ensure the safe and reliable operation of on-orbit satellites. At present, most fault diagnosis methods are to diagnose independent identically distributed(i.i.d) task objects. However, even if the same device works under different working conditions, the distribution domain of the collected data almost always changes. At the same time, the training of fault diagnosis model under full working conditions can increase the model complexity and training time, and there may unknown working conditions. In view of the above situation, this paper proposed a domain adaptive adversarial deep neural network based fault diagnosis method. By combining the feature extractor, label classifier and domain classifier with the convolutional neural network and gradient inversion layer (GRL), the effective label classification can be achieved while the resolution of different domains can be reduced. We achieved feature extraction of the classification learning task in the source domain and transfer of the classification task between the two domains. the effectiveness of the diagnosis model is verified in the ground simulation data of a certain satellite under different conditions. © 2021 ieee.

关键词： Failure analysis

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A Long Short Term Memory Network Based on Surface Electromyography for Continuous Estimation of Elbow Joint Angle 9

A Long Short Term Memory Network Based on Surface Electromyo...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Chai, Yuanyuan Liu, Keping Sun, Zhongbo Wang, Gang Shi, Tian Changchun Univ Technol Dept Control Engn Jilin 130000 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Bion Engn Changchun 130025 Peoples R China Jilin Univ Dept Control Engn Jilin 130000 Jilin Peoples R China

ISBN: (纸本)9781728159225

A simple long short term memory (LSTM) network is built to estimate the model which is described the relationship between the elbow joint angle and the surface electromyography (sEMG) signals in this paper. the sEMG time series of biceps and triceps are the inputs of the model, and the elbow joint angle is the output of the model. the sEMG signals while the user is performing flexion and extension movements are collected by Biopac. Elbow joint angle is measured by angle sensor. the results show that for simple flexion and extension movements, the model is able to eatimate the movement intention of the elbow.

关键词： sEMG Rehabilitation Elbow joint LSTM network

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Robot Skill learning and Generalization based on Human-robot Collaboration and Computer Vision 13

Robot Skill Learning and Generalization based on Human-robot...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Xin Zhang, Ruiqing Huang, Deqing Li, Yanan School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350361674

the paper presents a novel approach for human-robot skill transferring. Firstly, we propose a method that combines dynamic time warping (DTW) with the Gaussian mixture model (GMM) to reconstruct the demonstrated skills, including reference path and pose, for each workpiece. Secondly, we integrate hand-eye coordination to facilitate dataset creation and employ YOLOv8 for model training. Finally, we utilize a neural network to obtain the current workpiece's category and pose information and then transform the previous skills into the current workpiece coordinate system. the effectiveness and robustness of our proposed method have been validated on a 7-DOF Sawyer robot equipped with a camera. © 2024 ieee.

关键词： Robot learning

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Sensor Correlation Network Based Anomaly Detection for thermal systems on Ships 9

Sensor Correlation Network Based Anomaly Detection for Therm...

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9th ieee data driven control and learning systems conference (ddcls)

作者： Zheng, Wei Zhou, Hongkuan Qiu, Zhiqiang Ke, Zhiwu Tao, Mo Chen, Zhaoxu Sci & Technol Thermal Energy & Power Lab Wuhan 430064 Peoples R China China State Shipbldg Corp Ltd Wuhan Ship Design & Res Inst 2 Wuhan 430064 Peoples R China

ISBN: (纸本)9781728159225

In this paper, we propose an approach to handle the anomaly detection for the thermal system on ships by the sensor associated network method. A large number of sensors are placed in different positions of the thermal system. these sensors form a topological network which can represent the operation state of the thermal system. there are both linear correlation and nonlinear correlation between the operating parameters reflected by these sensors. the MAS index from MINE is utilized to represent the correlation information between sensors when the thermal system is in dynamic operation condition. Based on the MAS correlation coefficient, the sensor correlation network is constructed to represent the dynamic operation process of thermal system. Using DBSCAN clustering algorithm, the large topological network is divided into different subnetworks. When the system is running dynamically, the Manhattan distance between the sub networks can reflect the running state of the system. Based on the distance of sub networks, the similarity of sensor networks with continuous changes is calculated and the similarity correlation sequence is formed. through the matching between the similarity correlation sequence and the historical experience sequence, we can judge whether the system dynamic condition is abnormal. By the simulation experiment data, we verify the effectiveness of the proposed method.

关键词： Anomaly Detection Correlation Network Correlation Coefficient thermal System

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Iterative learning enhanced Integral Terminal Sliding Mode control for Precision Motion systems 10

Iterative Learning enhanced Integral Terminal Sliding Mode C...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Feng, Zhao Ling, Jie Wan, Feng Yang, Zhi-Xin University of Macau Department of Electrical and Computer Engineering China College of Mechanical Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China University of Macau State Key Lab. of Internet of Things for Smart City and Department of Electromechanical Engineering China

ISBN: (纸本)9781665424233

the rapid development and applications of precision motion systems pose a great challenge on tracking performance improvement to complete various industrial or scientific tasks. In this paper, an iterative learning enhanced integral terminal sliding mode control (IL- ITSMC) is developed to further enhance the performance of such systems under repetitive trajectory and disturbance. For the generally used second-order model in precision motion systems, an integral terminal sliding surface is utilized to improve the steady-state performance and robustness to unexpected disturbance. A novel reaching law is also designed to realize the finite-time convergence of the sliding surface. In addition, an iterative learning law is proposed based on the sliding surface to compensate the repetitive term through updating the feedforward control input iteratively. the stability in time domain and convergence in iterative domain are proven theoretically based on the well-known Lyapunov theory, respectively. the simulation results on a pizeo-actuated stage with hysteresis nonlinearity demonstrate that the proposed IL-ITSMC achieves the best tracking performance through comparisons, and the convergence speed is improved significantly in comparison with ITSMC with traditional P-type ILC (PIL- ITSMC) for a 10Hz sinusoidal repetitive trajectory. © 2021 ieee.

关键词： Sliding mode control

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Detection of Anatomical Landmarks during Laparoscopic Cholecystectomy Surgery Based on Improved Yolov7 Algorithm 13

Detection of Anatomical Landmarks during Laparoscopic Cholec...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Yang, Zongjin Yang, Guang Chen, Xingyu Chen, Zhenhao Qin, Na Huang, Deqing Institute of Systems Science and Technology Southwest Jiaotong University Chengdu610031 China

ISBN: (纸本)9798350361674

Many adverse events during laparoscopic cholecystectomy surgery occur due to errors in visual perception and judgment by surgeons, leading to misunderstandings of anatomical structures, particularly in surgeries performed by young and inexperienced doctors. this paper proposes a YOLOv7_CA model for detecting anatomical landmarks during laparoscopic cholecystectomy surgery, based on an attention mechanism. the network efficiently detects the gallbladder, Calot's triangle, and common bile duct, which are prone to intraoperative mis-manipulations. We introduce data augmentation and transfer learning methods to address the issue of small datasets. the mAP of YOLOv7_CA is 69.34%, with improvements of 3.22%, 5.59%, and 6.10% in mAP, Precision, and Recall, respectively, compared to YOLOv7. Experimental results demonstrate the network's excellent performance in detecting critical intraoperative anatomical landmarks, thereby achieving real-time assistance for surgeons. It also provides new ideas for the application of deep learning in the surgical field of vision. © 2024 ieee.

关键词： Laparoscopy

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A Novel ADRC Design with Cascade of Observers for Rigid-body Motion control systems 11

A Novel ADRC Design with Cascade of Observers for Rigid-body...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Chen, Zhixiang Xue, Wenchao Mi, Wenpeng Kong, Xiangtong Bai, Wenyan Qingzhou High-tech Institute Weifang262500 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Beijing Aerospace Automatic Control Institute Beijing100089 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper considers the disturbance rejection problem for general rigid-body motion control systems. A novel active disturbance rejection control (ADRC) design with cascade of one high gain observer and one reduced-order extended state observer is proposed. It is shown that compared with the regular ADRC, the proposed ADRC method only needs the control direction, avoiding the difficulty of choosing the nominal control input gain. Moreover, such novel design has the same order as that of normal extended state observer. Moreover, it can be proved that the resulting closed-loop system is semi-globally uniformly bounded (SGUB). Finally, simulation results of a 1 degree-of-freedom (DOF) manipulator control system demonstrate the effectiveness of our method. © 2022 ieee.

关键词： Closed loop systems

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Fault-Tolerant control Based on State Observer and Optimal Allocation for Four-Wheel Independent Drive Electric Vehicle 11

Fault-Tolerant Control Based on State Observer and Optimal A...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Li, Fengyang Chen, Yong Tang, Hui School of Automation Engineering Institute of Electric Vehicle Driving System and Safety Technology University of Electronic Science and Technology of China Chengdu611730 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, the velocity tracking control problem and the lateral stability problem of the four-wheel independent drive electric vehicle (4WID-EV) system under actuator fault conditions are studied. Firstly, the 7 degrees of freedom (DOF) vehicle model and linear 2 DOF ideal vehicle model are constructed in this paper. Secondly, a nonlinear state observer is designed to track the real system states. then, a double-layer controller is constructed to guarantee the longitudinal motion performance and lateral stability of the vehicle by updating the input voltage of the four in-wheel motors to control both the longitudinal driving force and the yaw moment, where the sliding mode control (SMC) and quadratic programming are used in the upper and lower controllers, respectively. Finally, the effectiveness of the method proposed is verified by three different maneuvers. © 2022 ieee.

关键词： Quadratic programming

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Event-triggered modular neural network control for containment maneuvering of second-order MIMO multi-agent systems 8

Event-triggered modular neural network control for containme...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhang, Yibo Liu, Lu Wang, Dan Peng, Zhouhua Sun, Gang School of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Jiangxi University of Science and Technology Ganzhou China

ISBN: (纸本)9781728114545

An event-triggered modular neural network controller is designed for containment maneuvering of second-order MIMO nonlinear multi-agent systems under an undirected graph. For the distributed containment maneuvering controller, the estimation loop and the control loop are designed separately. In the estimation loop, an estimator is developed to design the adaptation law. the uncertain nonlinear dynamics is identified by using the neural network. An event-triggered mechanism is utilized for reducing the communication burden of followers. In the control loop, a local control law are designed based on a modified dynamic surface control method. By using an event-triggered mechanism, the path update law is designed and the communication burden of leaders is reduced. the closed-loop system is proved to be input-to-state stable. Besides, the closed-loop system is analyzed to exclude Zeno behavior. A numerical example is given to reveal the efficacy of the proposed event-triggered controller for containment maneuvering of second-order nonlinear MIMO multi-agent systems. © 2019 ieee.

关键词： Multi agent systems

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