dc.contributor.author | Kollias, Stefanos | |
dc.contributor.author | Yu, Miao | |
dc.contributor.author | Wingate, James | |
dc.contributor.author | Durrant, Aiden | |
dc.contributor.author | Leontidis, Georgios | |
dc.contributor.author | Alexandridis, Georgios | |
dc.contributor.author | Stafylopatis, Andreas | |
dc.contributor.author | Mylonakis, Antonios | |
dc.contributor.author | Vinai, Paolo | |
dc.contributor.author | Demaziere, Christophe | |
dc.date.accessioned | 2022-07-01T19:54:01Z | |
dc.date.available | 2022-07-01T19:54:01Z | |
dc.date.issued | 2022-07-01 | |
dc.identifier | 216492530 | |
dc.identifier | 19c9db4c-482c-4786-a791-c87dbda8a653 | |
dc.identifier | 85133406097 | |
dc.identifier.citation | Kollias , S , Yu , M , Wingate , J , Durrant , A , 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.2022.109293 | en |
dc.identifier.issn | 0306-4549 | |
dc.identifier.other | ORCID: /0000-0001-6671-5568/work/115465458 | |
dc.identifier.other | ORCID: /0000-0002-8375-4523/work/148505720 | |
dc.identifier.uri | https://hdl.handle.net/2164/18771 | |
dc.description | The 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.extent | 29 | |
dc.format.extent | 3383691 | |
dc.language.iso | eng | |
dc.relation.ispartof | Annals of Nuclear Energy | en |
dc.subject | SDG 7 - Affordable and Clean Energy | en |
dc.subject | SDG 9 - Industry, Innovation, and Infrastructure | en |
dc.subject | 2040 Data and Artificial Intelligence | en |
dc.subject | Neutron Noise | en |
dc.subject | Machine Learning | en |
dc.subject | Domain Adaptation | en |
dc.subject | Unsupervised learning | en |
dc.subject | Clustering | en |
dc.subject | Self-supervised learning | en |
dc.subject | core diagnostics | en |
dc.subject | core monitoring | en |
dc.subject | Simulated Data | en |
dc.subject | Actual Plant Data | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | European Commission | en |
dc.subject.lcc | QA75 | en |
dc.title | Machine learning for analysis of real nuclear plant data in the frequency domain | en |
dc.type | Journal article | en |
dc.contributor.institution | University of Aberdeen.Computing Science | en |
dc.contributor.institution | University of Aberdeen.Machine Learning | en |
dc.contributor.institution | University of Aberdeen.Centre for Energy Transition | en |
dc.description.status | Peer reviewed | en |
dc.identifier.doi | 10.1016/j.anucene.2022.109293 | |