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dc.contributor.authorKollias, Stefanos
dc.contributor.authorYu, Miao
dc.contributor.authorWingate, James
dc.contributor.authorDurrant, Aiden Mark
dc.contributor.authorLeontidis, Georgios
dc.contributor.authorAlexandridis, Georgios
dc.contributor.authorStafylopatis, Andreas
dc.contributor.authorMylonakis, Antonios
dc.contributor.authorVinai, Paolo
dc.contributor.authorDemaziere, Christophe
dc.date.accessioned2022-07-01T19:54:01Z
dc.date.available2022-07-01T19:54:01Z
dc.date.issued2022-07-01
dc.identifier.citationKollias , S , Yu , M , Wingate , J , Durrant , A M , Leontidis , G , Alexandridis , G , Stafylopatis , A , Mylonakis , A , Vinai , P & Demaziere , C 2022 , ' Machine learning for analysis of real nuclear plant data in the frequency domain ' , Annals of Nuclear Energy , vol. 177 , 109293 . https://doi.org/10.1016/j.anucene.2022.109293en
dc.identifier.issn0306-4549
dc.identifier.otherPURE: 216492530
dc.identifier.otherPURE UUID: 19c9db4c-482c-4786-a791-c87dbda8a653
dc.identifier.otherORCID: /0000-0001-6671-5568/work/115465458
dc.identifier.otherScopus: 85133406097
dc.identifier.urihttps://hdl.handle.net/2164/18771
dc.descriptionThe research conducted has been made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No 754316 for the “CORe Monitoring Techniques And EXperimental Validation And Demonstration (CORTEX)” Horizon 2020 project, 2017-2021.en
dc.format.extent29
dc.language.isoeng
dc.relation.ispartofAnnals of Nuclear Energyen
dc.rightsThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en
dc.subjectSDG 7 - Affordable and Clean Energyen
dc.subjectSDG 9 - Industry, Innovation, and Infrastructureen
dc.subject2040 Data and Artificial Intelligenceen
dc.subjectNeutron Noiseen
dc.subjectMachine Learningen
dc.subjectDomain Adaptationen
dc.subjectUnsupervised learningen
dc.subjectClusteringen
dc.subjectSelf-supervised learningen
dc.subjectcore diagnosticsen
dc.subjectcore monitoringen
dc.subjectSimulated Dataen
dc.subjectActual Plant Dataen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectEuropean Commissionen
dc.subject.lccQA75en
dc.titleMachine learning for analysis of real nuclear plant data in the frequency domainen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Vice Principalsen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1016/j.anucene.2022.109293


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