Show simple item record

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

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.date.accessioned2022-08-22T11:57:01Z
dc.date.available2022-08-22T11:57:01Z
dc.date.issued2022-12-01
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.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.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.extent10
dc.format.extent2181924
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
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.description.statusPeer revieweden
dc.identifier.doi10.1016/j.anucene.2022.109373


Files in this item

Thumbnail
Name:
Papaoikonomou_etal_Deep_learni ...
Size:
2.080Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record