{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:09:23Z","timestamp":1774418963376,"version":"3.50.1"},"reference-count":27,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/ijcnn.2018.8489130","type":"proceedings-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T18:25:09Z","timestamp":1539973509000},"page":"1-8","source":"Crossref","is-referenced-by-count":51,"title":["A Deep Learning Approach to Anomaly Detection in Nuclear Reactors"],"prefix":"10.1109","author":[{"given":"Francesco","family":"Caliva","sequence":"first","affiliation":[]},{"given":"Fabio Sousa","family":"De Ribeiro","sequence":"additional","affiliation":[]},{"given":"Antonios","family":"Mylonakis","sequence":"additional","affiliation":[]},{"given":"Christophe","family":"Demazirere","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Vinai","sequence":"additional","affiliation":[]},{"given":"Georgios","family":"Leontidis","sequence":"additional","affiliation":[]},{"given":"Stefanos","family":"Kollias","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/EAIS.2018.8397178"},{"key":"ref11","first-page":"3320","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-017-0064-6"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2764844"},{"key":"ref14","article-title":"Validation of casmo5\/simulate-3k using the special power excursion test reactor iii e-core. cold start-up, hot start-up, hot standby and full power conditions","author":"grandi","year":"2015","journal-title":"Technical Report"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-98149-9_14"},{"key":"ref16","article-title":"User&#x2019;s manual of the core sim neutronic tool","author":"demaziere","year":"2011","journal-title":"Technical Report Chalmers University of Technology"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref19","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNS.2010.2088138"},{"key":"ref27","article-title":"Noise-based nuclear plant core monitoring and diagnostics","author":"demaziere","year":"2017","journal-title":"Proceedings of Advances in Reactor Physics Mumbai India"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2017.05.024"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2008.08.005"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2010.02.019"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2017.8280975"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2015.12.023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.anucene.2011.06.010"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref1","volume":"2","author":"cacuci","year":"2010","journal-title":"Handbook of Nuclear Engineering Vol 1 Nuclear Engineering Fundamentals Vol 2 Reactor Design Vol 3 Reactor Analysis Vol 4 Reactors of Generations III and IV Vol 5 Fuel Cycles Decommissioning Waste Disposal and Safeguards"},{"key":"ref20","first-page":"1027","article-title":"k-means++: The advantages of careful seeding","author":"arthur","year":"2007","journal-title":"Proceedings of the eighteenth annual ACMSIAM symposium on Discrete algorithms"},{"key":"ref22","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv preprint arXiv 1412 6980"},{"key":"ref21","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"van der maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref24","article-title":"Keras","author":"chollet","year":"2015"},{"key":"ref23","article-title":"The mathworks","author":"guide matlab","year":"1992","journal-title":"Inc Natick MA"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2007.06.004"},{"key":"ref25","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","volume":"16","author":"abadi","year":"2016","journal-title":"OSDI"}],"event":{"name":"2018 International Joint Conference on Neural Networks (IJCNN)","location":"Rio de Janeiro","start":{"date-parts":[[2018,7,8]]},"end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8465565\/8488986\/08489130.pdf?arnumber=8489130","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,23]],"date-time":"2020-08-23T20:37:34Z","timestamp":1598215054000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8489130\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2018.8489130","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}