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

Fiber Optic Speckle Recovery Based on Lightweight Adversarial Network 基于轻量级对抗网络的光纤散斑恢复

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455515

Yanzhu Zhang, Haishuai Zhang, Xiaomeng Zhang, J. Pu, Xiaoyan Wang

引用次数: 0

An Iterative Learning Algorithm Based on RBF Neural Network in Upper Limb Rehabilitation Robot 基于RBF神经网络的上肢康复机器人迭代学习算法

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/ddcls52934.2021.9455640

Zaixiang Pang, Tongyu Wang, Shuai Liu, Zhanli Wang, Xiyu Zhang, Yan Hao

引用次数: 1

MSB-Net: Multi-Scale Boundary Net for Polyp Segmentation 用于息肉分割的多尺度边界网

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455514

Dongchao Wang, Mingjie Hao, Ruirui Xia, Jinhui Zhu, Sheng Li, Xiongxiong He

{"title":"MSB-Net: Multi-Scale Boundary Net for Polyp Segmentation","authors":"Dongchao Wang, Mingjie Hao, Ruirui Xia, Jinhui Zhu, Sheng Li, Xiongxiong He","doi":"10.1109/DDCLS52934.2021.9455514","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455514","url":null,"abstract":"Polyp of intestinal tract is the precursor of colorectal cancer. Accurate computer-aided polyp location and segmentation in colonoscopy is of great importance since it provides valuable information for endoscopists. However, polyps are arduous to be segmented due to their high inter-class similarity, high intra-class variation, and low contrast with surrounding mucosa. To address these challenges, we propose a multi-scale boundary network (MSB-Net) for polyp segmentation. We first focus on the multi-scale feature representation and propose a novel architectural unit to extract intra-stage and contextual information, which is named ResU-Block (RUB). RUBs are connected by the proposed multi-squeeze-and-excitation (Multi-SE) units which can recalibrate the feature information from a multi-scale perspective. We then generate a coarse prediction using the partial decoder, of which the boundary is further refined by a shallow-level attention (SA) module. In addition, we exploit the boundary details using a set of reverse attention (RA) modules, which can progressively establish relationships between regions and boundaries from deep-level features. Comprehensive experiments on five public datasets across five metrics elucidate that our architecture outperforms other SOTA methods by a large margin while maintaining comparable model complexity and inference speed.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116949630","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

Unsupervised Feature Learning with Data Augmentation for Control Valve Stiction Detection 基于数据增强的无监督特征学习控制阀粘滞检测

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455535

Kexin Zhang, Yong Liu

{"title":"Unsupervised Feature Learning with Data Augmentation for Control Valve Stiction Detection","authors":"Kexin Zhang, Yong Liu","doi":"10.1109/DDCLS52934.2021.9455535","DOIUrl":"https://doi.org/10.1109/DDCLS52934.2021.9455535","url":null,"abstract":"This paper proposes an unsupervised feature learning approach on industrial time series data for detection of valve stiction. Considering the commonly existed characteristics of industrial time series signals and the condition that sometimes massive reliable labeled-data are not available, a new time series data transformation and augmentation method is developed. The transformation stage converts the raw time series signals to 2-D matrices and the augmentation stage increases the diversity of the matrices by performing transformation on different timescales. Then a convolutional autoencoder is used to extract the representative features on the augmented data, these new features are taken as the inputs of the traditional clustering algorithms. Unlike the traditional approaches using hand-crafted features or requiring labeled-data, the proposed strategy can automatically learn features on the time series data collected from industrial control loops without supervision. The effectiveness of the proposed approach is evaluated through the International Stiction Data Base (ISDB). Compared with the traditional machine learning methods and deep learning based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we provide a visualization process of feature learning via principal component analysis.","PeriodicalId":325897,"journal":{"name":"2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116156175","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: 1

Fractional-Order New Generation of $6k+1$-Order Repetitive Control for Three-phase Grid-connected Converters 新一代分数阶6k+1阶三相并网变流器重复控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455628

Wei Wang, Wenzhou Lu, Keliang Zhou, Qigao Fan

引用次数: 0

Battery Fault Diagnosis Scheme Based on Improved RBF Neural Network 基于改进RBF神经网络的电池故障诊断方案

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455621

Zhenyu Liu, Yan Li

引用次数: 1

Multi-fusion Network for Single Image Deraining 单幅图像训练的多融合网络

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455487

Huanlei Guo, Jie Wang, Tingwei Zhou, Wenkang Huang, Junqing Yuan, Xiongxiong He

引用次数: 0

Load frequency active disturbance rejection control for an interconnected power system via deep reinforcement learning 基于深度强化学习的互联电力系统负荷频率自抗扰控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455664

Yongshuai Wang, Zengqiang Chen, Mingwei Sun, Qinglin Sun

引用次数: 0

Model-Free Adaptive Security Tracking Control for Networked Control Systems 网络控制系统的无模型自适应安全跟踪控制

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455667

Meng-Ying Su, Weiwei Che, Zhenling Wang

引用次数: 1

Manufacturing Big Data Modeling Based on KNN-LR Algorithm and Its Application in Product Design Business Domain 基于KNN-LR算法的制造业大数据建模及其在产品设计业务领域的应用

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI: 10.1109/DDCLS52934.2021.9455547

Yi Xiao, Hongru Ren, Renquan Lu, Shen Cheng

引用次数: 1