2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)最新文献_第6页

Disturbance Observer Based Adaptive Fuzzy Sliding Mode Controller for Cable Arrangement System 基于扰动观测器的电缆布放系统自适应模糊滑模控制器

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516075

Xiufeng Li, Ling Ma

引用次数: 0

The Mechatronic system design of the bed-chair integration system E-Bed* 床-椅一体化系统E-Bed机电一体化系统设计

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516119

Weihan Tian, Hu Yin, Diansheng Chen, Yifei Li

引用次数: 1

Fault Diagnosis of Train Clamp Based on Faster R-CNN and One-class Convolutional Neural Network 基于更快R-CNN和一类卷积神经网络的列车夹具故障诊断

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516167

Zonghong Zhang, Junjie Ma, Deqing Huang, Zhonghe Zhou, Zipeng Wan, N. Qin

{"title":"Fault Diagnosis of Train Clamp Based on Faster R-CNN and One-class Convolutional Neural Network","authors":"Zonghong Zhang, Junjie Ma, Deqing Huang, Zhonghe Zhou, Zipeng Wan, N. Qin","doi":"10.1109/ICIEA51954.2021.9516167","DOIUrl":"https://doi.org/10.1109/ICIEA51954.2021.9516167","url":null,"abstract":"In recent years, Chinese high-speed railway ushered in a great development. With the high-speed railway operation gradually gets busy, the traditional method of relying on manual inspection of train fault has been unable to keep pace with the pace. As a key part of the train, the rod and spring components of clamp is essential for the safe and smooth operation of the train. In this paper, a novel method combining Faster R-CNN and One-class Convolutional Neural Network (OC-CNN) is proposed for fault diagnosis of the clamp part on train. Firstly, the rod and spring on the clamp part are located by Faster R-CNN, and the rod component is detected to determine whether there is any abnormality. Meanwhile, the spring area is cropped from the clamp part picture and resized as a fixed size. Then, the image contains spring area is feeded into the OC-CNN algorithm which is trained by positive samples and fine tuned by negative samples to determine whether there are cracks in the spring. Through specific experiments, the conclusions show that this method is effective and it surpasses the other three types of combined methods, namely You Only Look Once version-4-tiny(YOLOv4-tiny) and OC-CNN, Single Shot Multibox Detector 512 (SSD512) and OC-CNN, as well as Nanodet and OC-CNN.","PeriodicalId":6809,"journal":{"name":"2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)","volume":"16 1","pages":"1394-1399"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79442290","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

Interface Identification of Automatic Verification System Based on Deep Learning 基于深度学习的自动验证系统接口识别

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516409

Andi Zheng, Yaqiong Fu, Mingze Dong, Xinyi Du, Yue-ning Chen, Jinglin Huang

引用次数: 0

The Tikhonov Regularization Method Improved by Genetic Algorithm is used to Retrieve the Non-Spherical Particle Spectral Distribution 采用遗传算法改进的Tikhonov正则化方法反演非球形粒子谱分布

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516179

Jiaqi Guo, Hu Zhao, Qingqing Xie, Yapeng Liu, Xirui Ma, Ze Qiao, B. Zhong, F. Ding

引用次数: 1

An Explainable Recurrent Neural Network for Solar Irradiance Forecasting 太阳辐照度预测的可解释递归神经网络

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516440

Bin Zhou, Shengnan Du, Lijuan Li, Huaizhi Wang, Yang He, Diehui Zhou

{"title":"An Explainable Recurrent Neural Network for Solar Irradiance Forecasting","authors":"Bin Zhou, Shengnan Du, Lijuan Li, Huaizhi Wang, Yang He, Diehui Zhou","doi":"10.1109/ICIEA51954.2021.9516440","DOIUrl":"https://doi.org/10.1109/ICIEA51954.2021.9516440","url":null,"abstract":"The factors affecting solar irradiance are usually complex and diverse, making it difficult to accurately predict the photovoltaic power generation. In this paper, an explainable recurrent neural network (ExRNN) algorithm is proposed based on deep recurrent neural network (RNN) and additive index model for solar irradiance forecasting problems. The proposed ExRNN is designed as an ante-hoc explainable algorithm with cyclic units by linearly combining single-feature models to learn explainable features of solar irradiances, and the ridge function is used as an activation function to extract and explain mapping correlations between meteorological features and solar irradiances. Furthermore, the RNN is used with memory characteristics to discover the time correlation hidden in the solar irradiance data sequence and retain the explainability. Therefore, the factors affecting solar irradiances can be quantified by the proposed ExRNN, and a legible explanation on the relationship between meteorological inputs and solar irradiances can be provided. Solar irradiance samples from Lyon France are used to evaluate the prediction accuracy and explainability of the proposed ExRNN.","PeriodicalId":6809,"journal":{"name":"2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)","volume":"65 1","pages":"1299-1304"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83715566","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

Diagnosis of Open Circuit Faults for Three-Phase Three-Level Converters Based on the Change Rate of Current Residual 基于剩余电流变化率的三相三电平变流器开路故障诊断

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516070

Zifeng Gong, Xiaoqiong He, Pengcheng Han

引用次数: 0

Video summary generation based on density peaks clustering with temporal characteristics 基于时间特征的密度峰聚类视频摘要生成

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516340

Ningli Tang, Fang Dai, Wenyan Guo

{"title":"Video summary generation based on density peaks clustering with temporal characteristics","authors":"Ningli Tang, Fang Dai, Wenyan Guo","doi":"10.1109/ICIEA51954.2021.9516340","DOIUrl":"https://doi.org/10.1109/ICIEA51954.2021.9516340","url":null,"abstract":"Video summary is a new content-based video compression technology, which can effectively find important information from the video and eliminate redundant data in video. The density peaks clustering (DPC) can quickly find the density peaks of datasets of arbitrary shapes and efficiently allocate data. In order to apply it to video summary generation, we consider the temporal characteristics of the video, and introduce it into the DPC algorithm, and propose an improved DPC algorithm with temporal characteristics (called T-DPC), which is applied for the Hue histogram clustering of video frames, and the video shot is segmented based on the clustering results. In the keyframe selection stage, calculate the similarity between each frame and its cluster center, and the entropy of each frame, then select the frame with the largest linear combination of entropy and similarity in each category as the keyframe. At the same time, the histogram intersection method is employed to remove similar frames in the keyframes to generate video summary. The proposed method in this paper is evaluated with 50 videos in the open video library. The experimental results show that the accuracy of the video summary generated by our method is higher than that of OV, STIMO, and VSUMM1, but not as good as DT and VSUMM2. The recall rate is higher than the OV, DT, and VSUMM2, the same as the STIMO, and slightly lower than the VSUMM1. The F values are all higher than the comparison algorithms OV, DT, STIMO, VSUMM1 and VSUMM2.","PeriodicalId":6809,"journal":{"name":"2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)","volume":"1 1","pages":"1421-1425"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77778832","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

ZVS Analysis and Control Strategy for Back-Stage of Single-Phase AC-DC-DC Converter With Low-Frequency DC-Link Voltage Ripple 低频直流链纹波单相AC-DC-DC变换器后级ZVS分析及控制策略

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516166

Linzhe Li, Linghui Meng, Shuai Zhang, Tianxiang Wang, Zeliang Shu

{"title":"ZVS Analysis and Control Strategy for Back-Stage of Single-Phase AC-DC-DC Converter With Low-Frequency DC-Link Voltage Ripple","authors":"Linzhe Li, Linghui Meng, Shuai Zhang, Tianxiang Wang, Zeliang Shu","doi":"10.1109/ICIEA51954.2021.9516166","DOIUrl":"https://doi.org/10.1109/ICIEA51954.2021.9516166","url":null,"abstract":"As a module of power electronic transformer (PET), single-phase isolated ac-dc-dc converter is popular in high-power applications. However, there is inherent instantaneous power difference between ac input and dc output, which leads to a significant low-frequency voltage ripple on the dc-link capacitor. It will affect the performance of dual active bridge (DAB), such as the range of zero voltage switch (ZVS) and output voltage ripple. So, the relationship between ZVS range of DAB converter and the input voltage is deduced. According to this relationship, leakage inductance of transformer is designed to realize full power range soft switching of the back-stage DAB. To suppress output voltage ripple, a feedback linearization control strategy is proposed. Finally, the rationality of the designed transformer leakage inductor and effectiveness of the proposed control strategy are verified by simulation. The simulation results show that the dc-link voltage ripple does not significantly increase the peak and RMS current of transformer, and the designed leakage inductor can realize ZVS in full power range of when the dc-link capacitor contains ripple. Moreover, the proposed control strategy can suppress the dc-link ripple transferring to the output.","PeriodicalId":6809,"journal":{"name":"2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)","volume":"51 1","pages":"1001-1006"},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77782040","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

Lower Limb Rehabilitation Exoskeleton Control based on SSVEP-BCI 基于SSVEP-BCI的下肢康复外骨骼控制

2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2021-08-01 DOI: 10.1109/ICIEA51954.2021.9516146

Zijie Qi, Weihai Chen, Jianhua Wang, Jianbin Zhang, Xuhua Wang

引用次数: 3