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dc.contributor.authorDurrant, Aiden
dc.contributor.authorLeontidis, Georgios
dc.contributor.authorKollias, Stefanos
dc.date.accessioned2020-04-27T10:00:00Z
dc.date.available2020-04-27T10:00:00Z
dc.date.issued2019
dc.identifier.citationDurrant , A , Leontidis , G & Kollias , S 2019 , ' 3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection ' , EPJ Nuclear Sciences & Technologies , vol. 5 , 20 . https://doi.org/10.1051/epjn/2019047en
dc.identifier.otherPURE: 158425807
dc.identifier.otherPURE UUID: 760f1c40-5549-47a3-a2ce-d73834a1c13f
dc.identifier.otherORCID: /0000-0001-6671-5568/work/65277638
dc.identifier.urihttps://hdl.handle.net/2164/14170
dc.descriptionThe research conducted was 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. We would like to thank the Chalmers University of Technology, particularly Dr C. Demaziere, Dr P. Vinai, Dr A. Milonakis and the Paul Scherrer Institute, particularly Dr A. Dokhane and Dr V. Verma for providing the frequency and domain data respectively, for assisting us with their understanding and for collaborating with us in the analysis process.en
dc.format.extent9
dc.language.isoeng
dc.relation.ispartofEPJ Nuclear Sciences & Technologiesen
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectMachine Learningen
dc.subjectDeep Learningen
dc.subjectnuclear reactorsen
dc.subjectsignal processingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectArtificial Intelligenceen
dc.subject.lccQA75en
dc.title3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detectionen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1051/epjn/2019047
dc.identifier.vol5en


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