3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection

Durrant, AidenLeontidis, GeorgiosKollias, Stefanos2020-04-272020-04-272019Durrant, 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/2019047ORCID: /0000-0001-6671-5568/work/65277638ORCID: /0000-0002-8375-4523/work/148505710https://hdl.handle.net/2164/14170The 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.91070970engMachine LearningDeep Learningnuclear reactorssignal processingQA75 Electronic computers. Computer scienceArtificial IntelligenceQA753D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detectionJournal article10.1051/epjn/20190475