DC FieldValueLanguage
dc.contributor.authorVoß, Carlo-
dc.contributor.authorEiteneuer, Benedikt-
dc.contributor.authorNiggemann, Oliver-
dc.date.accessioned2021-05-04T07:28:45Z-
dc.date.available2021-05-04T07:28:45Z-
dc.date.issued2020-06-10-
dc.identifier.citationEnthalten in: 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS). - Piscataway, NJ : IEEE, 2020. - 1 Online-Ressource . - 2020, Seite 475-480de_DE
dc.description.abstract© 2020 IEEE. In the field of Cyber-Physical Systems (CPS), the early detection of anomalies is crucial to avoid future faulty behaviors, e.g. preventing downtimes or decreasing product qualities. As a solution, unsupervised machine learning can be used to learn models of the historic system behavior and consequentially detect deviations from these models. Since CPS data are high dimensional time series, suitable approaches such as Long Short-Term Memory (LSTM) neural networks are good solution candidates.CPSs have specific requirements for such machine learning algorithms. Learned models must be especially useful in closed control loops, i.e. without human supervision. For this, it is essential that the uncertainty about model predictions is also part of the learned models. In order to incorporate such uncertainties, we modify the mean squared error loss function used by LSTM. This paper also analyses the solution on artificial and real data.de_DE
dc.description.sponsorshipInformatik im Maschinenbaude_DE
dc.language.isoende_DE
dc.publisherIEEEde_DE
dc.subjectUniversitätsbibliographiede_DE
dc.subjectEvaluation 2020de_DE
dc.titleIncorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systemsde_DE
dc.typeConference Objectde_DE
dc.relation.conference3rd IEEE International Conference on Industrial Cyber-Physical Systemsde_DE
dc.identifier.doi10.1109/ICPS48405.2020.9274779-
dc.identifier.scopus2-s2.0-85098693473-
hsu.accessrights.dnbblockedde_DE
dcterms.bibliographicCitation.pagestart475de_DE
dcterms.bibliographicCitation.pageend480de_DE
dcterms.bibliographicCitation.originalpublisherplacePiscataway, NJde_DE
dcterms.bibliographicCitation.articlenumber9274779de_DE
dc.relation.pages475 - 480de_DE
dc.identifier.urlhttps://ub.hsu-hh.de/DB=1.8/XMLPRS=N/PPN?PPN=1756961948-
dc.identifier.urlhttps://www.researchgate.net/publication/340502169-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85098693473-
local.submission.typeonly-metadatade_DE
dcterms.bibliographicCitation.isPartOf2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS 2020)de_DE
item.grantfulltextnone-
item.fulltext_sNo Fulltext-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
crisitem.author.deptInformatik im Maschinenbau-
crisitem.author.deptFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.deptDekanat Maschinenbau-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultäten-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
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