Kollias, StefanosYu, MiaoWingate, JamesDurrant, AidenLeontidis, GeorgiosAlexandridis, GeorgiosStafylopatis, AndreasMylonakis, AntoniosVinai, PaoloDemaziere, Christophe2022-07-012022-07-012022-07-01Kollias, 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.2022.1092930306-4549ORCID: /0000-0001-6671-5568/work/115465458ORCID: /0000-0002-8375-4523/work/148505720https://hdl.handle.net/2164/18771The 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, 2017-2021.293383691engSDG 7 - Affordable and Clean EnergySDG 9 - Industry, Innovation, and Infrastructure2040 Data and Artificial IntelligenceNeutron NoiseMachine LearningDomain AdaptationUnsupervised learningClusteringSelf-supervised learningcore diagnosticscore monitoringSimulated DataActual Plant DataQA75 Electronic computers. Computer scienceEuropean CommissionQA75Journal article10.1016/j.anucene.2022.109293177