2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第7页

Multiple Recurrent Neural Networks Based Fault Diagnosis Model for Multi-mode Process 基于多元递归神经网络的多模式过程故障诊断模型

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858405

Chuan Wan, Tongshuai Zhang, Xiaojun Yang, Z. Xiong

引用次数: 1

Tracking Control for Constrained Nonlinear Systems 约束非线性系统的跟踪控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858480

Lin Zhu, Bai Yue, Weiwei Che

引用次数: 0

Enhancing EEG Motor Imagery Decoding Performance via Deep Temporal-domain Information Extraction 基于深度时域信息提取提高脑电运动图像解码性能

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858575

Qihong Yang, Mingzhao Yang, Ke Liu, Xin Deng

{"title":"Enhancing EEG Motor Imagery Decoding Performance via Deep Temporal-domain Information Extraction","authors":"Qihong Yang, Mingzhao Yang, Ke Liu, Xin Deng","doi":"10.1109/DDCLS55054.2022.9858575","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858575","url":null,"abstract":"Electroencephalography (EEG) based motor imagery Brain-Computer Interface (MI-BCI) has been widely applied in constructing a pathway between human brains and external machines. However, decoding of MI-EEG signals is challenging as EEG is severely affected by non-stationarity and high variability in signal patterns. In this work, to fully extract the spatial and temporal information of EEG for MI decoding, we designed an end-to-end compact deep convolutional neural network model, combining EEGNet and a temporal convolutional network. The proposed model requires few data pre-processing and a small number of trainable parameters, achieving significant performance improvement on MI-EEG decoding tasks. Experimental results under the subject-dependent manner show that our method achieves 56.73%, 73.9%, and 75.4% classification accuracy on the 2020 BCIC, Same Limb, and OpenBMI datasets, respectively, which outperforms the state-of-the-art (STOA) convolutional neural network (CNN). Under the subject-independent with the 2020 BCIC dataset, the proposed approach achieves an accuracy improvement of 2.8% compared to the STOA CNN. The code of the proposed method is available at https://github.com/ingod/DDCLS-MI-EEG-BCI.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125747504","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

Soft-sensors Based on Gaussian Process Regression for Wastewater Treatment Plants 基于高斯过程回归的污水处理厂软传感器

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858578

Tong Liu, W. Chai, Congcong Wang

引用次数: 0

Spatio-Temporal View GAIN for Data Imputation and Dynamic Soft Sensor 用于数据输入和动态软测量的时空视图增益

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858447

Jiayi Ren, Xu Chen, Chunhui Zhao

{"title":"Spatio-Temporal View GAIN for Data Imputation and Dynamic Soft Sensor","authors":"Jiayi Ren, Xu Chen, Chunhui Zhao","doi":"10.1109/DDCLS55054.2022.9858447","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858447","url":null,"abstract":"Soft sensor plays a key role in the safe operation of industrial processes and product quality control. Affected by closed-loop feedback, process data often demonstrate certain dynamic characteristics. In addition, traditional soft sensor methods are often based on the assumption of completeness. Once part of the data is missing, the above methods will collapse. In this paper, we propose a Spatio-Temporal view Generative Adversarial Imputation Network (GAIN) for data imputation and apply dynamic soft sensor for block missing accompanied with completely random missing data. Initially, a data pre-imputed strategy is designed to first impute the missing data through GAIN from the perspective of data distribution. Secondly, on the basis of pre-imputation, a data imputation strategy that integrates time and space information is proposed to impute the data precisely, which can comprehensively consider the time series of process data and the correlation between variables, and further improve the accuracy of data imputation. Finally, we propose a dynamic soft sensor method to make real-time predictions of the data. The effectiveness of the proposed method is verified by the Tennessee Eastman (TE) process.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808716","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: 0

Feature Extraction of Sequence Data Based on LSTM and its Application to Fault Diagnosis of Industrial Process 基于LSTM的序列数据特征提取及其在工业过程故障诊断中的应用

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858505

Xiaojun Yang, Chuan Wan, Tongshuai Zhang, Z. Xiong

引用次数: 1

An Automatic Detection Approach for Wearing Safety Helmets on Construction Site based on YOLOv5 基于YOLOv5的施工现场安全帽佩戴自动检测方法

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858511

Pengqiang Ge, Yiyang Chen

{"title":"An Automatic Detection Approach for Wearing Safety Helmets on Construction Site based on YOLOv5","authors":"Pengqiang Ge, Yiyang Chen","doi":"10.1109/DDCLS55054.2022.9858511","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858511","url":null,"abstract":"The environment of construction site is generally complex and uncertain, and there may be different kinds of unexpected potential risks. Therefore, it is necessary for construction workers to wear safety helmets on construction site, which saves their lives on sudden accidents. The traditional way to handle helmet wearing issue is through manual inspection and video surveillance, which has poor efficiency and lacks immediate warning. To solve this issue, the authors propose a helmet wearing detection approach based on YOLOv5 and annotate a dataset with 3, 965 images. Then, the authors divide labelled dataset into training set (3, 108 images) and validation set (857 images) to train the neural network with the pre-training weight YOLOv5s.pt. Lastly, the authors use the trained neural network to predict real scene images collected from the construction site. Experimental results indicate that safety helmet wearing detection using the trained YOLOv5 network performs well in term of high precision rate, high recall rate, low training classification loss, and low training objectness loss. In addition, it is capable of detecting safety helmets precisely as well as making a fair distinction between different categories.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126675477","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

$H_{infty}$ control for uncertain T-S fuzzy systems $H_{infty}$ 不确定T-S模糊系统控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858583

Yue Wu, Liangcheng Cai

引用次数: 0

Interval Type-2 Dynamic Fuzzy Neural Network with Tensor Inverse 具有张量逆的区间型2动态模糊神经网络

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858483

Jiale Hu, Guoliang Zhao, Sharina Huang, Huhe Dai

引用次数: 0

An Optimal Sliding Mode Controller Against False Data Injection Attacks 抗假数据注入攻击的最优滑模控制器

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858407

Chengwei Wu, Bolei Dong, Shuo Han, Weiran Yao

引用次数: 0