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

Papaoikonomou, AntoniosWingate, JamesVerma, VasudhaDurrant, AidenIoannou, GeorgePapagiannis, TasosYu, MiaoAlexandridis, GeorgiosDokhane, AbdelhamidLeontidis, GeorgiosKollias, StefanosStafylopatis, Andreas2022-08-222022-08-222022-12-01Papaoikonomou, 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.2022.1093730306-4549ORCID: /0000-0001-6671-5568/work/117768416ORCID: /0000-0002-8375-4523/work/148505719https://hdl.handle.net/2164/19087The 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, 2017-2021.102181924eng2040 Data and Artificial IntelligenceConvolutional neural networksRecurrent neural networksdeep learningPerturbation identificationperturbation localizationself-supervised domain adaptationSIMULATE-3KQA75 Electronic computers. Computer scienceEuropean Commission754316QA75Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurementsJournal article10.1016/j.anucene.2022.109373178