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pro vyhledávání: '"Adam Lundstrom"'
Publikováno v:
IEEE Access, Vol 11, Pp 93898-93907 (2023)
Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets.
Externí odkaz:
https://doaj.org/article/acb66f44623242e3a986dd47c36b04a8
Publikováno v:
IEEE Access, Vol 10, Pp 108194-108204 (2022)
The importance of anomaly detection in multivariate time series has led to the development of several prominent deep learning solutions. As a part of the anomaly detection process, the scoring method has shown to be of significant importance when sep
Externí odkaz:
https://doaj.org/article/fa60373f1c2b40059b6636209392f47f
Autor:
Adam Lundström, Mattias O’Nils
Publikováno v:
Data, Vol 8, Iss 7, p 115 (2023)
The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of dat
Externí odkaz:
https://doaj.org/article/7ce882bb796c4641bfa71045ea7eb6d0