{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:59:19Z","timestamp":1764053959822,"version":"3.40.4"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L241078"],"award-info":[{"award-number":["L241078"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015959","name":"State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University","doi-asserted-by":"publisher","award":["RAO2023ZZ003","RAO2025ZR001"],"award-info":[{"award-number":["RAO2023ZZ003","RAO2025ZR001"]}],"id":[{"id":"10.13039\/501100015959","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tim.2025.3556915","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T21:12:35Z","timestamp":1743541955000},"page":"1-12","source":"Crossref","is-referenced-by-count":1,"title":["Segmented-Aggregated Framework With Internal\u2013External Constraint Mechanism for Unsupervised Track Anomaly Detection"],"prefix":"10.1109","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2456-3850","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Advanced Rail Autonomous Operation, the School of Traffic and Transportation, and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1611-9703","authenticated-orcid":false,"given":"Zhiwei","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Rail Autonomous Operation, the School of Traffic and Transportation, and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6519-8316","authenticated-orcid":false,"given":"Yong","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Rail Autonomous Operation, the School of Traffic and Transportation, and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5091-4318","authenticated-orcid":false,"given":"Lirong","family":"Lian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Rail Autonomous Operation, the School of Traffic and Transportation, and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2296-6239","authenticated-orcid":false,"given":"Linlin","family":"Kou","sequence":"additional","affiliation":[{"name":"Beijing Subway Operation Company Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7710-5861","authenticated-orcid":false,"given":"Yaguan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Subway Operation Company Ltd., Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2022.06.025"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3402730"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3253895"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12710"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSI55536.2022.9970633"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109742"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IAEAC54830.2022.9929911"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3336962"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3417537"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDIIME59043.2023.00040"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/APARM49247.2020.9209351"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3341113"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3038008"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3307181"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3233654"},{"key":"ref16","first-page":"263","article-title":"Autoencoders, minimum description length, and Helmholtz free energy","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hinton"},{"key":"ref17","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/s20123336"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref22","first-page":"1","article-title":"Student\u2013teacher feature pyramid matching for anomaly detection","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Wang"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"ref25","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"Cohen","year":"2020","journal-title":"arXiv:2005.02357"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00189"},{"key":"ref29","article-title":"FastFlow: Unsupervised anomaly detection and localization via 2D normalizing flows","author":"Yu","year":"2021","journal-title":"arXiv:2111.07677"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110684"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ISCID.2018.00087"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICRIS.2019.00033"},{"issue":"10","key":"ref33","first-page":"3066","article-title":"Background differential rail surface defect detection method based on defect proportion limitation","volume":"40","author":"Cao","year":"2020","journal-title":"Comput. Appl."},{"issue":"10","key":"ref34","first-page":"123","article-title":"Automatic positioning method of railway sleeper based on improved Sobel operator","volume":"59","author":"Zhang","year":"2019","journal-title":"Railway Construct."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3125987"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3112698"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3348118"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"ref40","first-page":"77","article-title":"Research on lightweight model training technology of federated learning for railway defect detection","volume":"45","author":"Ren","year":"2023","journal-title":"J. China Railway Soc."},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897283"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/19\/10764799\/10947216.pdf?arnumber=10947216","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T05:15:48Z","timestamp":1744694148000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10947216\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/tim.2025.3556915","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"type":"print","value":"0018-9456"},{"type":"electronic","value":"1557-9662"}],"subject":[],"published":{"date-parts":[[2025]]}}}