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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221-230 订阅

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Yarn-dyed Fabric Defect Detection using U-shaped De-noising Convolutional Auto-Encoder 9

Yarn-dyed Fabric Defect Detection using U-shaped De-noising ...

引用

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

作者： Zhang, Hong-wei Tan, Quan-lu Lu, Shuai Ge, Zhi-qiang Gu, De Xian Polytech Univ Sch Elect Informat Xian 710048 Peoples R China Zhejiang Univ Inst Ind Proc Control Hangzhou 310027 Peoples R China Beijing Univ Chem Technol Inst Math Beijing 100029 Peoples R China Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781728159225

Practical factors such as high labor cost of labelling defect samples and scarcity of defect samples make it difficult for supervised machine learning models to solve the problem of yam-dyed fabric defect detection. To solve this problem, this paper proposes an unsupervised yam-dyed fabric defect detection method based on U-shaped de-noising convolutional auto-encoder (UDCAE). Firstly, for tested samples of yam-dyed fabric, the training dataset was constructed by collecting the non-defect yarn-dyed fabric samples. then, the non-defect dataset is utilized to model and train the proposed UDCAE model. Finally, the defective area can be quickly detected by calculating the residual between the original tested yarn-dyed fabric image and its reconstructed item correspondingly. the experiment results show that the proposed method can accurately detect defects of yarn-dyed fabrics with different patterns.

关键词： yam-dyed fabric fabric defect detection de-noising auto-encoder convolutional auto-encoder Unet

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data-based analysis on traction energy for urban rail transit using clustering and classification methods 8

Data-based analysis on traction energy for urban rail transi...

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

作者： Zhao, Jiao Tang, Tao Xun, Jing Jiang, Shan Gao, Ming Ren, Baifeng State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Mass Transit Railway Operation Corporation Limited Beijing100044 China CRRC Tangshan Co. Ltd Hebei064000 China

ISBN: (纸本)9781728114545

the energy consumption of urban rail transit is increasing gradually in recent years. Accurate data analysis of traction energy is in urgent demand for improving energy-efficiency, thus it becomes an essential research subject for the metro system. According to the characteristics of the subway, this paper analyzes the traction energy consumption data based on the clustering and classification methods. the energy data collected by the energy consumption metering device is a line from Beijing Subway. the energy data feature vectors are obtained after simply pre-processing the raw data. Cluster algorithms are applied to divide the feature vectors into several sets with the same characteristics. the energy patterns is generated by decision tree algorithm. Finally, the outliers of traction energy under the same energy pattern are selected based on local outlier factor (LOF). the analysis results show that the traction energy data can be divided into four patterns and the outliers are detected under finer clusters than before, which can help the metro companies manage the energy data delicately. © 2019 ieee.

关键词： Energy utilization

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A Roadside Vehicle Line Pressing Detection Algorithm Based on YOLOv7 13

A Roadside Vehicle Line Pressing Detection Algorithm Based o...

引用

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

作者： Wang, Yuquan Feng, Shuhui Wang, Li School of Electronics & Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9798350361674

In order to solve the problem of inaccurate vehicle line pressing detection and recognition, a roadside vehicle line pressing detection algorithm based on YOLOv7 is proposed. Firstly, we utilize the YOLOv7 detection method to select the vehicle object in the video, and employ the keypoint estimation method to obtain the vehicle's reference point information. then, the parking line detection method was used to obtain the parking location information. Finally, according to the information about vehicles and parking spaces, we propose an algorithm to judge the vehicle line pressing by comparing the intersection of the vehicle and the parking space. Experiment results show that this algorithm has good detection performance on 294 test images. Compared with the existing algorithm, it does not require collecting the motion trajectory of the vehicle, and can be effectively applied to roadside parking scene. © 2024 ieee.

关键词： Line pressing detection Roadside parking Vehicle detection YOLOv7

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Output Regulation for Switched systems: An Event-Triggered Model Predictive control Approach 11

Output Regulation for Switched Systems: An Event-Triggered M...

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

作者： Guo, Shitong Qi, Yiwen Zhang, Simeng Tang, Yiwen Yu, Wenke Shenyang Aerospace University School of Automation Shenyang110136 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper studies the output regulation problem for switched systems with an event-triggered model predictive control approach. First, an event-triggering strategy is adopted to configure data sampling and communication efficiently. Moreover, the model predictive control approach is considered to optimize the control performance of the closed-loop system. By applying the piecewise Lyapunov function and average dwell time method, sufficient conditions for the solvability of the output regulation problem are given. then, the design of model predictive controller is transformed into the solution of linear matrix inequalities. Finally, simulation results verify the effectiveness of the proposed method. © 2022 ieee.

关键词： Closed loop systems

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LS-SVM Combined with ZNN for Predicting the Continuous Motion Joint Angle of Lower Limb 10

LS-SVM Combined with ZNN for Predicting the Continuous Motio...

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

作者： Zhang, Xin Liu, Keping Li, Chunxu Yi, Jiang Duan, Xiaoqin Sun, Zhongbo Changchun University of Technology Department of Control Engineering Changchun130000 China University of Plymouth Centre for Robotics and Neural Systems PlymouthPL48AA United Kingdom Medicine of the Second Hospital of Jilin University Department of Rehabilitation Changchun130000 China

ISBN: (纸本)9781665424233

In this paper, a method fusing least squares support vector machine (LS-SVM) with Gaussian kernel function and zeroing neural network (ZNN) is proposed to forecast the continuous motion of lower limb. the surface electromyography (sEMG) signal contains human behavior information and directly reflects the movement intention. In the experiment, the sEMG signal of the lower limbs when the tester is doing leg extension exercise is collected and the real knee joint angle is recorded at the same time. then the raw sEMG is subjected to a series of preprocessing, and the corresponding muscle activation is gained by calculation. the muscle activation is used as input to the LS-SVM model, and the output to the model is the knee joint angle. LS-SVM transforms the original problem into solving linear equations. When the amount of data is relatively large, the traditional solution method is very time-consuming, and the proposed ZNN method is able to solve the problem, thus speeding up the convergence speed and greatly reducing the learning time. Finally, the back propagation neural network (BPNN) model is utilized to form a comparative experiment. the numerical results indicate that a more stable and better performance is reflected in the raised method, which provides a valuable reference for the research of joint continuous estimation. © 2021 ieee.

关键词： Numerical methods

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Continuous Estimation of Human Knee-Joint Angles from SEMG UsingWavelet Neural Network 9

Continuous Estimation of Human Knee-Joint Angles from SEMG U...

引用

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

作者： Li, Wanting Liu, Keping Sun, Zhongbo Wang, Gang Li, Feng Zhang, Xin Zhou, Yanpeng Changchun Univ Technol Dept Control Engn Changchun 130025 Peoples R China Jilin Univ Key Lab Bion Engn Minist Educ Changchun 130025 Peoples R China

ISBN: (纸本)9781728159225

Surface electromyography (sEMG) signals contain a wealth of information associated with human's movement. In this paper, a wavelet neural network (WNN) model is proposed and implemented to estimate human knee-joint angles from the sEMG signals. With the processed signals as input, the WNN model is trained to estimate the knee-joint angles in continuous motion. To validate the effectiveness of the WNN model, one able-bodied person sit in a chair and accomplish leg stretching in the experiment, and simultaneously record the sEMG signals from the vastus rectus (VR) and the angles of the knee joint. then, the estimation results of the WNN model are compared with the RBF neural network and the BP neural network. the experimental results show that the WNN model has the best performance in the knee-joint angles estimation than the other two neural network models. the root mean square (RMS) error of the knee-joint angles is 6.5054 degrees and the time is 5.3271 seconds. the proposed method can be applied to rehabilitation robots or assisted exoskeleton.

关键词： Joint angles estimation Surface electronyogram (sEMG) Rehabilitation Wavelet neural network (WNN)

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A Deep-Convolution-Generative-Adversarial-Networks-based Missing data Filling Method for Blast Furnace Gas System in Steel Industry 11

A Deep-Convolution-Generative-Adversarial-Networks-based Mis...

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

作者： Yang, Canguang Jin, Feng Zhao, Jun Wang, Wei Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the integrity of monitoring data is of great significance to ensure the accuracy of data analysis and reliable operation in blast furnace gas system of steel industry. In this study, a Deep-Convolution-Generative-Adversarial-Networks (DCGAN)-based data filling method is proposed for high proportion missing in time series, and the corresponding network structure is designed. Time series are transformed non-destructively into time domain images by a Gram matrix, and their temporal characteristics are preserved. the authenticity constraint and the context similarity are established to optimize the hidden variables, and the high-precision time domain image is generated through the DCGAN. the completed time series is obtained by inversely transforming the Gram matrix. the simulation results based on the actual operating data of a steel enterprise indicate that the proposed method is capable of has high padding accuracy in the case of a high proportion of random missing data and continuous missing data. © 2022 ieee.

关键词： Time series

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control Performance Monitoring of RtR controllers Based on Improved KL Divergence in Semiconductor Manufacturing Processes 11

Control Performance Monitoring of RtR Controllers Based on I...

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

作者： Ling, Dan Li, Chaosong Lei, Ting Wang, Yan Guo, Danlei College of Electrical and Information Enginnering Zhengzhou University of Light Industry Zhengzhou45000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Exponential weighted moving average (EWMA) run-to-run (RtR) controller is a widely used controller in semiconductor manufacturing process. Machine aging, model-plant mismatch and other factors lead to system performance degradation of EWMA RtR control system, wafer yield reduction, and even the safety of the manufacturing pr ocess degradation. A performance monitoring method for EWMA RtR control system is proposed based on improved KL divergence. First, a novel control performance index (CPI) is constructed based on improved KL divergence only requiring routine operation data. then, the control limit of the proposed CPI is obtained by kernel density estimation (KDE) method. Next, the control performance online monitoring method is provided to monitor the performance of EWMA RtR control system. Finally, numerical simulations of chemical mechanical polishing (CMP) process are given to illustrate the efficiency of the proposed method. © 2022 ieee.

关键词： controllers

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Robust data-driven Model Predictive control via On-policy Reinforcement learning for Robot Manipulators 7

Robust Data-driven Model Predictive Control via On-policy Re...

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ieee 7th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Lu, Tianxiang Zhang, Kunwu Shi, Yang Univ Victoria Dept Mech Engn Victoria BC Canada

ISBN: (纸本)9798350363029;9798350363012

In this paper, a robust data-driven model predictive control (MPC) via on-policy reinforcement learning (RL) is presented for the regulation of constrained robot manipulators subject to both model mismatch and bounded additive disturbances. Based on the Euler-Lagrangian model of the robot manipulator, the model mismatch is characterized by the difference between the system matrix describing the Coriolis and centrifugal torques and its initial approximation. the additive disturbances considered in this work affect the torques applied to the joints. To reduce the complexity of the prediction model adopted by MPC and thus the computational load of solving the online control problem in MPC, the inverse dynamics controller is incorporated into the control framework. the rigid tube-based MPC with adaptive design of the terminal weighting matrix and terminal set is presented to ensure robust constraint satisfaction. the use of the inverse dynamics control policy leads to the selection of the on-policy RL algorithm: Sarsa for designing the policy to update key parameters of the MPC optimization problem. the efficacy of the proposed control framework is validated by a case study using a robot manipulator with two revolute joints in simulation.

关键词： Model predictive control reinforcement learning data-driven robot manipulators

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Intelligent Drug Recognition System Based on Improved YOLOv7 and PP-OCRv3 13

Intelligent Drug Recognition System Based on Improved YOLOv7...

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

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

ISBN: (纸本)9798350361674

the manual dispensing of drugs by pharmacists is time-consuming, labor-intensive, and has a low accuracy rate. In order to address this issue, this paper proposes an automated drug dispensing method based on improved object detection and text recognition. this method first embeds a Simple Attention Module (SimAM) into the network structure of YOLOv7, named YOLOv7_Sim, to achieve the localization and recognition of medicine bottles. Additionally, a lightweight PP-OCRv3 is employed for text recognition on liquid bags, and the similarity between the recognition results and the names of drugs on the prescription is used to determine a match. the novelty of this method lies in the combination of YOLOv7 and SimAM, which improves the accuracy of medicine bottle recognition and enhances the algorithm's robustness. Experimental results demonstrate that compared to YOLOv7, YOLOv7_Sim shows significant performance improvements, with a 3.12% increase in mAP, 0.69% increase in precision, and 2.83% increase in recall for the same type of medicine bottle. the proportion of fully recognized text on liquid bags is 90.6%. the drug recognition system, which combines YOLOv7_Sim and lightweight PP-OCRv3, has practical engineering significance for automating the drug dispensing process. © 2024 ieee.

关键词： Bottles

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