Qinjun Zhao, Shanchang Fang, Yueyang Li, Hongwei Shang, Han Zhang, Tao Shen
{"title":"Track Defect Detection Based on Improved YOLOv5s","authors":"Qinjun Zhao, Shanchang Fang, Yueyang Li, Hongwei Shang, Han Zhang, Tao Shen","doi":"10.1109/tii.2024.3523593","DOIUrl":"https://doi.org/10.1109/tii.2024.3523593","url":null,"abstract":"","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"2 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"2024 Index IEEE Transactions on Industrial Informatics Vol. 20","authors":"","doi":"10.1109/TII.2025.3526005","DOIUrl":"10.1109/TII.2025.3526005","url":null,"abstract":"","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"20 12","pages":"1-251"},"PeriodicalIF":11.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830757","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Distribution, Scale, and Context Sensitive, Convolutional Neural Network-Based SOC Estimation for Li-ion Batteries","authors":"Halil Çimen","doi":"10.1109/TII.2024.3520180","DOIUrl":"10.1109/TII.2024.3520180","url":null,"abstract":"Li-ion batteries play a crucial role in green energy goals, but estimating their parameters is challenging due to their nonlinear structure, aging effects, and varying chemistries. In this article, a distribution, scale and context sensitive, convolutional neural network-based state of charge estimation model is proposed. First, the proposed model improves generalization by addressing data distribution shifts in batteries across different temperatures through individual sample handling. Second, by stacking convolutional layers with varied receptive fields, the model captures both local and global dependencies, providing the model with multiscale features and hierarchical representation. Finally, we add a self-attention module to enhance learning of input sequences by focusing on relevant parts and understanding the global context of features. Experiments were performed on single-domain and cross-domain settings to prove the effectiveness of the model. The results obtained demonstrate that the proposed model significantly outperforms state-of-the-art approaches in terms of both accuracy and generalization capability.","PeriodicalId":13301,"journal":{"name":"IEEE Transactions on Industrial Informatics","volume":"21 2","pages":"1990-1999"},"PeriodicalIF":11.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}