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Unsupervised Data Anomaly Detection Based on Spatio-Temporal Sparse Integration Framework

dc.contributor.authorWan, Keyu
dc.contributor.authorZhang, Weiming
dc.contributor.authorWang, Yutong
dc.contributor.authorWang, Jinfeng
dc.contributor.authorRen, Jiawei
dc.date.accessioned2026-08-20T21:26:43Z
dc.date.available2026-08-20T21:26:43Z
dc.date.copyright2025/09/30
dc.date.issued2026
dc.identifier.otherJCCEE5.CPENG-7002.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314463
dc.description.abstractAbstractData anomaly detection plays a pivotal role in structural health monitoring (SHM), enabling reliable and accurate assessment of structural condition. This paper proposes an unsupervised anomaly detection method based on the spatio-temporal sparse ...
dc.publisherAmerican Society of Civil Engineers
dc.titleUnsupervised Data Anomaly Detection Based on Spatio-Temporal Sparse Integration Framework
dc.typeJournal Article
dc.journal.volume40
dc.journal.issue1
dc.journal.titleJournal of Computing in Civil Engineering
dc.identifier.doi10.1061/JCCEE5.CPENG-7002
dc.journal.fristpage04025126-1
dc.journal.lastpage04025126-18
dc.page18
dc.treeJournal of Computing in Civil Engineering:;2026:;Volume ( 040 ):;issue: 001
dc.contenttypeFulltext


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