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Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurements

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dc.contributor.authorPapaoikonomou, Antonios
dc.contributor.authorWingate, James
dc.contributor.authorVerma, Vasudha
dc.contributor.authorDurrant, Aiden
dc.contributor.authorIoannou, George
dc.contributor.authorPapagiannis, Tasos
dc.contributor.authorYu, Miao
dc.contributor.authorAlexandridis, Georgios
dc.contributor.authorDokhane, Abdelhamid
dc.contributor.authorLeontidis, Georgios
dc.contributor.authorKollias, Stefanos
dc.contributor.authorStafylopatis, Andreas
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.date.accessioned2022-08-22T11:57:01Z
dc.date.available2022-08-22T11:57:01Z
dc.date.issued2022-12-01
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.description.statusPeer revieweden
dc.format.extent10
dc.format.extent2181924
dc.identifier218609288
dc.identifierad825dd9-64ff-4236-8203-babb7c58a794
dc.identifier85136485449
dc.identifier.citationPapaoikonomou, A, Wingate, J, Verma, V, Durrant, A, Ioannou, G, Papagiannis, T, Yu, M, Alexandridis, G, Dokhane, A, Leontidis, G, Kollias, S & Stafylopatis, A 2022, 'Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurements', Annals of Nuclear Energy, vol. 178, 109373. https://doi.org/10.1016/j.anucene.2022.109373en
dc.identifier.doi10.1016/j.anucene.2022.109373
dc.identifier.issn0306-4549
dc.identifier.otherORCID: /0000-0001-6671-5568/work/117768416
dc.identifier.otherORCID: /0000-0002-8375-4523/work/148505719
dc.identifier.urihttps://hdl.handle.net/2164/19087
dc.identifier.vol178en
dc.language.isoeng
dc.relation.ispartofAnnals of Nuclear Energyen
dc.subject2040 Data and Artificial Intelligenceen
dc.subjectConvolutional neural networksen
dc.subjectRecurrent neural networksen
dc.subjectdeep learningen
dc.subjectPerturbation identificationen
dc.subjectperturbation localizationen
dc.subjectself-supervised domain adaptationen
dc.subjectSIMULATE-3Ken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectEuropean Commissionen
dc.subject754316en
dc.subject.lccQA75en
dc.titleDeep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurementsen
dc.typeJournal articleen

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