| dc.contributor.author | Durrant, Aiden | |
| dc.contributor.author | Leontidis, Georgios | |
| dc.contributor.author | Kollias, Stefanos | |
| dc.date.accessioned | 2020-04-27T10:00:00Z | |
| dc.date.available | 2020-04-27T10:00:00Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Durrant , 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/2019047 | en |
| dc.identifier.other | PURE: 158425807 | |
| dc.identifier.other | PURE UUID: 760f1c40-5549-47a3-a2ce-d73834a1c13f | |
| dc.identifier.other | ORCID: /0000-0001-6671-5568/work/65277638 | |
| dc.identifier.uri | https://hdl.handle.net/2164/14170 | |
| dc.description | The 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.extent | 9 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | EPJ Nuclear Sciences & Technologies | en |
| dc.rights | This 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.subject | Machine Learning | en |
| dc.subject | Deep Learning | en |
| dc.subject | nuclear reactors | en |
| dc.subject | signal processing | en |
| dc.subject | QA75 Electronic computers. Computer science | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject.lcc | QA75 | en |
| dc.title | 3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection | en |
| dc.type | Journal article | en |
| dc.contributor.institution | University of Aberdeen.Computing Science | en |
| dc.contributor.institution | University of Aberdeen.Centre for Energy Transition | en |
| dc.contributor.institution | University of Aberdeen.Machine Learning | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Publisher PDF | en |
| dc.identifier.doi | https://doi.org/10.1051/epjn/2019047 | |
| dc.identifier.vol | 5 | en |
