YuanHao Zhang, Zujun Yu, Liqiang Zhu, Baoqing Guo, Yao Wang
{"title":"Rail-PatchCore: unsupervised learning-based detection of visual anomalies in the railway-turnout environment","authors":"YuanHao Zhang, Zujun Yu, Liqiang Zhu, Baoqing Guo, Yao Wang","doi":"10.1007/s10489-025-06294-8","DOIUrl":"10.1007/s10489-025-06294-8","url":null,"abstract":"<div><p>The complexity and openness of railway turnout environments pose great challenges to anomaly detection, and supervised methods are highly dependent on labels, making it difficult to address the diverse types of anomalies and the scarcity of samples in turnout environments. To solve these problems, this paper proposes a new method, Rail-PatchCore, which is based on unsupervised learning and effectively reduces the interference of background noise and enhances the ability to capture anomalous features by adding a Dual-Dimensional Channel Attention (DDCA) module and a projection anomaly scoring module to the PatchCore model. The experiments on our railway-turnout anomaly detection dataset(RTAD) and other datasets (RSDDs, MVTec-AD, BTAD, AEBAD-S) show that the detection performance of Rail-PatchCore is better than that of the existing methods, and the image-level and pixel-level AUCROC indices of Rail-PatchCore on the railway turnout anomaly detection dataset reach 72.2% and 95.3%, respectively. This approach provides an efficient and reliable solution for anomaly detection in railway turnout environments.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"An automated incremental density-based clustering approach using unsupervised deep learning and multi-objective optimization","authors":"Binu Jose A. , Pranesh Das","doi":"10.1016/j.compeleceng.2025.110109","DOIUrl":"10.1016/j.compeleceng.2025.110109","url":null,"abstract":"<div><div>Incremental density-based clustering algorithms are designed to handle large datasets and streaming data. However, the automatic identification of critical input parameters and the merging threshold for dynamically merging clusters in incremental density-based algorithms presents a significant challenge. This paper addresses the above challenge by introducing a novel framework, termed Multi-Objective Incremental Density-Based Clustering using deep learning (MIDBC-DL). It leverages Pareto front generation by utilizing pseudo labels that captures non-linear relationships between objective functions. Furthermore, a novel evaluation metric, the Score Index (SI), is introduced to achieve a robust and balanced consideration of both compactness and separation between clusters. To validate the effectiveness of the proposed approach, experiments are conducted using five bench mark datasets — Iris, Glass, Wine, Pendigits and Shuttle. Experimental results demonstrate that MIDBC-DL achieves superior performance compared to the state-of-the-art methods. The source code is available at <span><span>https://github.com/BinuJoseA/MIDBC-DL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110109"},"PeriodicalIF":4.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yunan Wang, Chuxiong Hu, Zeyang Li, Yujie Lin, Shize Lin, Suqin He
{"title":"Chattering Phenomena in Time-Optimal Control for High-Order Chain-of-Integrator Systems With Full State Constraints","authors":"Yunan Wang, Chuxiong Hu, Zeyang Li, Yujie Lin, Shize Lin, Suqin He","doi":"10.1109/tac.2025.3538775","DOIUrl":"https://doi.org/10.1109/tac.2025.3538775","url":null,"abstract":"","PeriodicalId":13201,"journal":{"name":"IEEE Transactions on Automatic Control","volume":"1 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191920","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}
Le Xia, Yao Sun, Chengsi Liang, Lei Zhang, Muhammad Ali Imran, Dusit Niyato
{"title":"Generative AI for Semantic Communication: Architecture, Challenges, and Outlook","authors":"Le Xia, Yao Sun, Chengsi Liang, Lei Zhang, Muhammad Ali Imran, Dusit Niyato","doi":"10.1109/mwc.003.2300351","DOIUrl":"https://doi.org/10.1109/mwc.003.2300351","url":null,"abstract":"","PeriodicalId":13342,"journal":{"name":"IEEE Wireless Communications","volume":"29 1","pages":""},"PeriodicalIF":12.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191987","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":"Neural Codebook Design for MIMO Network Beam Management","authors":"Ryan M. Dreifuerst, Robert W. Heath","doi":"10.1109/twc.2025.3536290","DOIUrl":"https://doi.org/10.1109/twc.2025.3536290","url":null,"abstract":"","PeriodicalId":13431,"journal":{"name":"IEEE Transactions on Wireless Communications","volume":"15 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191990","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":"Numerical and Analytical Methods for Complex Electromagnetic Media","authors":"","doi":"10.1109/TAP.2025.3534960","DOIUrl":"https://doi.org/10.1109/TAP.2025.3534960","url":null,"abstract":"","PeriodicalId":13102,"journal":{"name":"IEEE Transactions on Antennas and Propagation","volume":"73 2","pages":"1293-1293"},"PeriodicalIF":4.6,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10874834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184470","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}