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DC poleHodnotaJazyk
dc.contributor.authorMurakami, Naoki
dc.contributor.authorHiramatsu, Naoto
dc.contributor.authorKobayashi, Hiroki
dc.contributor.authorAkizuki, Shuichi
dc.contributor.authorHashimoto, Manabu
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-21T09:22:08Z-
dc.date.available2024-07-21T09:22:08Z-
dc.date.issued2024-
dc.identifier.citationJournal of WSCG. 2024, vol. 32, no. 1-2, p. 91-100.en
dc.identifier.issn1213 – 6972
dc.identifier.issn1213 – 6980 (CD-ROM)
dc.identifier.issn1213 – 6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/57348
dc.format10 s.cs_CZ
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs_CZ
dc.rights© Václav Skala - UNION Agencyen
dc.subjectdetekce anomáliícs
dc.subjectstrojové učenícs
dc.subjectgenerování obrazucs
dc.subjectaugmentace datcs
dc.subjectanalýza hlavních komponentcs
dc.subjectvlastní prostorcs
dc.titleA proposal of anomaly detection method based on natural data augmentation in the Eigenspaceen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersion-
dc.description.abstract-translatedThis paper proposes a natural data augmentation method and an anomaly removal artificial neural network for accurate anomaly detection. Anomaly detection is important because the provision of high-quality products is vital in the manufacturing industry. However, it is difficult to obtain a sufficient number of anomaly samples for the detection, which represents a significant challenge when it comes to achieving accurate anomaly detection by machine learning. General data augmentation methods generate new anomaly images by combining normal images and anomaly images. As an alternative, this paper describes a method that generates new anomaly images by using the Eigenspace. More natural anomaly images are generated than with general data augmentation methods. This paper also proposes an anomaly removal neural network that utilizes this natural data augmentation. The results of an anomaly detection experiment showed that the AUC of 94.7% was achieved for the capsule dataset when using anomaly images generated by the proposed data augmentation for training the anomaly removal neural network. This is 1.3% higher than the state-of-the-art data augmentation method that has been utilized for training the neural network. In the case of the pill dataset, AUC of 99.4% was achieved by proposed method. This is 3.0% higher than the state-of-the-art data augmentation method that has been utilized for training the neural network. The results of a series of experiments demonstrated that anomaly images generated by the proposed data augmentation are effective for training the neural network.en
dc.subject.translatedanomaly detectionen
dc.subject.translatedmachine learningen
dc.subject.translatedimage generationen
dc.subject.translateddata augmentationen
dc.subject.translatedprincipal component analysisen
dc.subject.translatedeigenspaceen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2024.10
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 32, number 1-2 (2024)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 32 (2024)
  6. Volume 32, number 1-2 (2024)