2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第5页

Fault Classification of Industrial Processes based on Generalized Zero-Shot Learning 基于广义零采样学习的工业过程故障分类

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455689

Jiacheng Huang, Zuxin Li, Lingjian Ye, Zhe Zhou

{"title":"Fault Classification of Industrial Processes based on Generalized Zero-Shot Learning","authors":"Jiacheng Huang, Zuxin Li, Lingjian Ye, Zhe Zhou","doi":"10.1109/DDCLS52934.2021.9455689","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455689","url":null,"abstract":"In the process industry, the supervised learning methods cannot classify the unseen faults (i.e., those faults without training samples to participate in the establishment of the model). Although Zero-Shot Learning (ZSL) has been proposed and successfully solved the problem of unseen fault classification, it failed to classify the seen faults (i.e., those faults participate in the establishment of the model). To overcome their shortcomings, in this paper, a generalized Zero-Shot Learning (GZSL) method is proposed to classify all the faults including the seen and the unseen faults by only using the samples of the seen fault and the human-defined fault semantic attribute description information. We use a gating mechanism based on Conditional Variational Autoencoder (CVAE) and a binary classifier to distinguish the online sample into the classes of the seen and unseen faults. Thus, the GZSL problem can be transformed into a supervised fault classification problem and a ZSL fault classification problem. Firstly, we train a CVAE to generate pseudo unseen fault samples and seen fault samples. Secondly, a binary classifier is trained to classify the online samples into seen and unseen categories. Finally, the specific category of the online samples will be determined by the supervised method and ZSL method, respectively. We validate our approach on the Tennessee-Eastman benchmark process.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121753828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Event-based Integral Reinforcement Learning Algorithm for Non-zero-sum Games of Partially Unknown Nonlinear Systems 部分未知非线性系统非零和博弈的基于事件的积分强化学习算法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455455

Hanguang Su, Huaguang Zhang, Yanhong Luo, Qiuye Sun

引用次数: 1

Blockchain-based Energy Transaction Model for Multiple Energy Hubs 基于区块链的多能源枢纽能源交易模型

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455644

Yujia Yang, L. Jia

引用次数: 0

A Novel Electric Vehicle Braking Energy Recovery Method Based on Model Free Adaptive Control Algorithm with Input and Output Constraints 基于输入输出约束的无模型自适应控制算法的电动汽车制动能量回收新方法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/ddcls52934.2021.9455530

Shida Liu, Zhen Li, Honghai Ji, Z. Hou, Lingling Fan

{"title":"A Novel Electric Vehicle Braking Energy Recovery Method Based on Model Free Adaptive Control Algorithm with Input and Output Constraints","authors":"Shida Liu, Zhen Li, Honghai Ji, Z. Hou, Lingling Fan","doi":"10.1109/ddcls52934.2021.9455530","DOIUrl":"https://doi.org/10.1109/ddcls52934.2021.9455530","url":null,"abstract":"This study focus on the problem of pure electric vehicle's braking energy recovery with the uncertain dynamic external factors. For this problem, a novel model free adaptive control with input and output constraints (IOC-MFAC) method is introduced. The dynamic process can be considered as a nonlinear two inputs and two outputs system with hydraulic braking torque and motor braking torque as inputs and braking energy and braking deceleration as outputs. By using IOC-MFAC, the constraints of limitation of current and voltage on the maximum motor braking torque and the constraints of the vehicle's comfort on braking deceleration are considered. Consequently, the recovered energy is controlled in a stable range while guaranteeing the energy recovery to prolong the storage battery's operating life. The major advantages of IOC-MFAC are that not only the controller is designed only with input and output data of the regenerative brake control system, but also the constraints of the system inputs and outputs are considered. Further, the efficiency of IOC-MFAC is verified with a series of numerical simulations.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133966283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Active Disturbance Rejection Based Iterative Learning Control for Direct Torque Control of Switched Reluctance Motor Drive 基于自抗扰迭代学习控制的开关磁阻电机直接转矩控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455497

W. Ai, M. Wu, Xinling Li, Xiangyang Li

引用次数: 1

Structural Balance Preserving and Consensus of Uncertain Euler-Lagrange Systems in Cooperation-Competition Networks 合作-竞争网络中不确定欧拉-拉格朗日系统的结构平衡保持与一致性

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455673

Jia Wang, Yingxia Zhou, Hong-xiang Hu

引用次数: 1

The Estimation Error of Extended State Observer in Rigid-Flexible Coupling Motion Stage 刚柔耦合运动阶段扩展状态观测器的估计误差

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455536

Ruirui Huang, Liyun Su, Yutai Wei, Zhijun Yang

引用次数: 0

KCF-Match Target Tracking Algorithm for Tracking Swing Angle of Coupler Based on Video 基于视频的耦合器摆角跟踪的kcf匹配目标跟踪算法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455694

Jiahao Du, N. Qin, Yiming Zhang, Bi Wu, Shiqian Chen

引用次数: 0

Iterative learning identification for a class of Wiener nonlinear time-varying systems 一类Wiener非线性时变系统的迭代学习辨识

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455696

Guomin Zhong, Qile Yu, Qiang Chen, Mingxuan Sun

引用次数: 0

An improved method for cloth pattern cutting based on Holistically-nested Edge Detection 一种改进的基于整体嵌套边缘检测的布样裁剪方法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455545

Naigong Yu, Zhen Zhang, Qiao Xu, Essaf Firdaous, Jia Lin

{"title":"An improved method for cloth pattern cutting based on Holistically-nested Edge Detection","authors":"Naigong Yu, Zhen Zhang, Qiao Xu, Essaf Firdaous, Jia Lin","doi":"10.1109/DDCLS52934.2021.9455545","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455545","url":null,"abstract":"Image edge detection is the basis for precise positioning and accurate cutting of cloth pattern contours. Compared with the commonly used traditional edge detection methods, the Holistically-nested Edge Detection has clearer and more continuous detection results including the reduction of the false detection rate. However, this method extracts a coarser thick outline. In order to extract a high-precision cloth pattern outline, clearly distinguish the main body of the pattern from the background, provide convenience for the follow-up cutting machine for accurate cutting, this paper proposes an improved method for edge detection of cloth pattern cutting based on the holistically-nested Edge Detection method. The edge refinement and smoothing process are added, where the edge detection, edge refinement, and edge smoothing of the clothes images are carried out in sequences, so that the extracted cloth pattern contour is continuous, smooth, and detailed, allowing the respect of the cutting requirements of the cutting machine and the requirements of the factory production.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133871853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5