Development of deep autoencoder-based anomaly detection system for HANARO
The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and indust...
Saved in:
| Published in | Nuclear engineering and technology Vol. 55; no. 2; pp. 475 - 483 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.02.2023
Elsevier 한국원자력학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1738-5733 2234-358X |
| DOI | 10.1016/j.net.2022.10.009 |
Cover
Loading…
| Abstract | The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model. |
|---|---|
| AbstractList | The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model KCI Citation Count: 1 The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model. |
| Author | Yu, Yonggyun Shin, Jin-Won Jeon, Byoungil Seo, Hogeon Ryu, Seunghyoung Lee, Minwoo |
| Author_xml | – sequence: 1 givenname: Seunghyoung orcidid: 0000-0001-7969-2924 surname: Ryu fullname: Ryu, Seunghyoung email: ashryu@kaeri.re.kr organization: Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Daejeon, South Korea – sequence: 2 givenname: Byoungil orcidid: 0000-0001-9038-1668 surname: Jeon fullname: Jeon, Byoungil email: bijeon@kaeri.re.kr organization: Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Daejeon, South Korea – sequence: 3 givenname: Hogeon orcidid: 0000-0002-7655-6203 surname: Seo fullname: Seo, Hogeon email: hogeony@kaeri.re.kr organization: Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Daejeon, South Korea – sequence: 4 givenname: Minwoo surname: Lee fullname: Lee, Minwoo email: leemw@kaeri.re.kr organization: HANARO Management Division, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Daejeon, South Korea – sequence: 5 givenname: Jin-Won surname: Shin fullname: Shin, Jin-Won email: jwshin@kaeri.re.kr organization: HANARO Management Division, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Daejeon, South Korea – sequence: 6 givenname: Yonggyun orcidid: 0000-0003-2863-9650 surname: Yu fullname: Yu, Yonggyun email: ygyu@kaeri.re.kr organization: Artificial Intelligence Application & Strategy Team, Korea Atomic Energy Research Institute, 111, Daedeok-daero 989 beon-gil, Daejeon, South Korea |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002929635$$DAccess content in National Research Foundation of Korea (NRF) |
| BookMark | eNp9UU1LYzEUDaJgdfwB7t564NV89SUPV8UZtSAjiIK7cJvcSOrrS0kyQv-9aetsZuHqJjf3nHtyzhk5HuOIhFwyOmWUdVer6Yhlyinn9T6ltD8iE86FbMVMvx6TCVNCtzMlxCk5y3lFaSelohOy-IUfOMTNGsfSRN84xE0Df0vE0UaHqV1CRtfAGNcwbOtzQVtCHJu8zQXXjY-puZ__mT89_iAnHoaMF1_1nLzc_n6-uW8fHu8WN_OH1krJSsu111LJrus1MtD1RJdVDVWOeebROtYtmdPUC7BSoeOsV1JqB0rPelt_cE5-HnjH5M27DSZC2Ne3aN6TmT89L0y1hMmZFnV4cRh2EVZmk8Ia0naP2DdiejOQSrADGqt7Jh2jwKWUQnAALb1fKue847TDyqUOXDbFnBN6Y0OBnRklQRjq0t3ezqxMjcLsoti1ahQVyf5D_lPyHeb6gMHq5UfAZLINNRR0IdUIqv7wDfoTFhih4Q |
| CitedBy_id | crossref_primary_10_1016_j_net_2023_11_033 crossref_primary_10_1016_j_net_2025_103524 crossref_primary_10_1109_TIM_2023_3302386 crossref_primary_10_1016_j_ascom_2025_100929 crossref_primary_10_1016_j_rineng_2025_104158 crossref_primary_10_1016_j_nucengdes_2023_112792 crossref_primary_10_1155_stni_9455897 crossref_primary_10_1007_s11814_024_00246_7 crossref_primary_10_1016_j_nucengdes_2024_113493 crossref_primary_10_1109_TII_2023_3345461 |
| Cites_doi | 10.1016/j.ress.2022.108446 10.1016/S0378-4754(02)00018-6 10.1016/j.pnucene.2014.10.013 10.1016/j.anucene.2020.107410 10.1016/j.ress.2008.08.005 10.1016/j.anucene.2019.05.039 10.1016/j.nucengdes.2022.111949 10.1016/j.eswa.2022.116966 10.1109/ACCESS.2020.3034218 10.1016/S0893-6080(05)80023-1 10.1109/ACCESS.2022.3187426 10.1016/j.asoc.2020.106874 10.1016/j.nucengdes.2020.110699 10.5516/NET.07.2014.006 10.1007/BF00058655 10.1080/18811248.1998.9733829 10.1016/j.anucene.2020.108113 10.1109/ACCESS.2020.3026104 10.1080/00223131.2019.1611501 10.1016/j.anucene.2018.05.020 10.1016/j.eswa.2020.113699 10.1016/j.eswa.2021.115848 10.1016/j.anucene.2021.108661 |
| ContentType | Journal Article |
| Copyright | 2023 Korean Nuclear Society |
| Copyright_xml | – notice: 2023 Korean Nuclear Society |
| DBID | 6I. AAFTH AAYXX CITATION DOA ACYCR |
| DOI | 10.1016/j.net.2022.10.009 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef DOAJ Directory of Open Access Journals Korean Citation Index |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2234-358X |
| EndPage | 483 |
| ExternalDocumentID | oai_kci_go_kr_ARTI_10114583 oai_doaj_org_article_c8914d10a2444332aa84ffb7ddfd206e 10_1016_j_net_2022_10_009 S1738573322004909 |
| GroupedDBID | .UV 0R~ 0SF 123 4.4 457 5VS 6I. 9ZL AACTN AAEDW AAFTH AALRI AAXUO ABMAC ACGFS ACYCR ADBBV ADEZE AENEX AEXQZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ BCNDV EBS EJD FDB GROUPED_DOAJ IPNFZ JDI KQ8 KVFHK M41 NCXOZ O9- OK1 RIG ROL SSZ AAYWO AAYXX ACVFH ADCNI ADVLN AEUPX AFPUW AIGII AKBMS AKRWK AKYEP CITATION |
| ID | FETCH-LOGICAL-c441t-28f84746698e1a87460b00607d1f1fecd16b1d80f3ac47ed2197448da7859c573 |
| IEDL.DBID | DOA |
| ISSN | 1738-5733 |
| IngestDate | Fri Feb 23 03:15:42 EST 2024 Wed Aug 27 01:19:18 EDT 2025 Tue Jul 01 01:28:32 EDT 2025 Thu Apr 24 22:57:25 EDT 2025 Tue May 16 23:59:53 EDT 2023 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Deep learning Autoencoder Anomaly detection Research reactor Nuclear reactor |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c441t-28f84746698e1a87460b00607d1f1fecd16b1d80f3ac47ed2197448da7859c573 |
| ORCID | 0000-0001-7969-2924 0000-0001-9038-1668 0000-0002-7655-6203 0000-0003-2863-9650 |
| OpenAccessLink | https://doaj.org/article/c8914d10a2444332aa84ffb7ddfd206e |
| PageCount | 9 |
| ParticipantIDs | nrf_kci_oai_kci_go_kr_ARTI_10114583 doaj_primary_oai_doaj_org_article_c8914d10a2444332aa84ffb7ddfd206e crossref_citationtrail_10_1016_j_net_2022_10_009 crossref_primary_10_1016_j_net_2022_10_009 elsevier_sciencedirect_doi_10_1016_j_net_2022_10_009 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | February 2023 2023-02-00 2023-02-01 2023-02 |
| PublicationDateYYYYMMDD | 2023-02-01 |
| PublicationDate_xml | – month: 02 year: 2023 text: February 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Nuclear engineering and technology |
| PublicationYear | 2023 |
| Publisher | Elsevier B.V Elsevier 한국원자력학회 |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier – name: 한국원자력학회 |
| References | Jin, Kim, Ryu, Kim, Park (bib13) 2022; 222 Bae, Kim, Lee (bib2) 2021; 186 Schapire (bib39) 2003 dos Santos, Pinheiro, do Desterro, de Avellar, Schirru, dos Santos Nicolau, de Lima (bib11) 2019; 133 Lee, Arigi, Kim (bib7) 2020; 8 Messai, Mellit, Abdellani, Pavan (bib23) 2015; 79 Bergstra, Bengio (bib31) 2012; 13 Breiman (bib38) 1996; 24 Ryu, Kim, Kim, Jin, Cho, Park (bib5) 2022; 200 Radaideh, Pigg, Kozlowski, Deng, Qu (bib12) 2020; 160 Zong, Song, Min, Cheng, Lumezanu, Cho, Chen (bib36) 2018 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg (bib29) 2011; 12 Kim, Ou, Loh, Allen, Agasie, Liu (bib24) 2020; 364 Kingma, Ba (bib25) 2014 Kim, Cho, Park (bib4) 2020; 8 Kim, Chae, Seong (bib6) 2020; 142 Nabeshima, Suzudo, Ohno, Kudo (bib18) 2002; 60 Ryu, Yim, Seo, Yu, Seo (bib34) 2022; 10 Nabeshima, Suzudo, Suzuki, Türkcan (bib21) 1998; 35 Caliva, De Ribeiro, Mylonakis, Demazi’ere, Vinai, Leontidis, Kollias (bib14) 2018 Lee, Lee, Lee (bib20) 2021; 99 Ryu, Jeon, Lee, Yu (bib28) 2021; 300 Choo, Cho, Yang, Park (bib1) 2014; 46 Kim, Jo, Kim, Park, Kim (bib3) 2021 Paszke, Gross, Massa, Lerer, Bradbury, Chanan, Killeen, Lin, Gimelshein, Antiga (bib27) 2019; 32 Tasakos, Ioannou, Verma, Alexandridis, Dokhane, Stafylopatis (bib22) 2021 Wolpert (bib37) 1992; 5 Santosh, Srivastava, Rao, Ghosh, Kushwaha (bib19) 2009; 94 Kim, Shim, Lim, Jeon, Choi, Kim, Yoon (bib33) 2019 Hou, Ni, Hawari (bib15) 2019; 120 Akcay, Atapour-Abarghouei, Breckon (bib35) 2018 Lee, Kim (bib16) 2022; 165 Loshchilov, Hutter (bib26) 2016 Ahn, Bae, Min, Lee (bib17) 2022; 397 Bergstra, Bardenet, Bengio, Kégl (bib30) 2011; 24 Lee, Seong, Kim (bib8) 2018; 119 Chen, Demachi (bib9) 2019; 56 Lin, Wang, Wu (bib10) 2021; 154 Snoek, Larochelle, Adams (bib32) 2012; 25 Lee (10.1016/j.net.2022.10.009_bib16) 2022; 165 Zong (10.1016/j.net.2022.10.009_bib36) 2018 Bae (10.1016/j.net.2022.10.009_bib2) 2021; 186 Kim (10.1016/j.net.2022.10.009_bib4) 2020; 8 Ryu (10.1016/j.net.2022.10.009_bib34) 2022; 10 Kim (10.1016/j.net.2022.10.009_bib6) 2020; 142 Choo (10.1016/j.net.2022.10.009_bib1) 2014; 46 Paszke (10.1016/j.net.2022.10.009_bib27) 2019; 32 Chen (10.1016/j.net.2022.10.009_bib9) 2019; 56 Nabeshima (10.1016/j.net.2022.10.009_bib18) 2002; 60 Hou (10.1016/j.net.2022.10.009_bib15) 2019; 120 Nabeshima (10.1016/j.net.2022.10.009_bib21) 1998; 35 Jin (10.1016/j.net.2022.10.009_bib13) 2022; 222 Tasakos (10.1016/j.net.2022.10.009_bib22) 2021 dos Santos (10.1016/j.net.2022.10.009_bib11) 2019; 133 Bergstra (10.1016/j.net.2022.10.009_bib31) 2012; 13 Ryu (10.1016/j.net.2022.10.009_bib28) 2021; 300 Caliva (10.1016/j.net.2022.10.009_bib14) 2018 Wolpert (10.1016/j.net.2022.10.009_bib37) 1992; 5 Ahn (10.1016/j.net.2022.10.009_bib17) 2022; 397 Radaideh (10.1016/j.net.2022.10.009_bib12) 2020; 160 Santosh (10.1016/j.net.2022.10.009_bib19) 2009; 94 Messai (10.1016/j.net.2022.10.009_bib23) 2015; 79 Kim (10.1016/j.net.2022.10.009_bib3) 2021 Loshchilov (10.1016/j.net.2022.10.009_bib26) 2016 Ryu (10.1016/j.net.2022.10.009_bib5) 2022; 200 Lee (10.1016/j.net.2022.10.009_bib7) 2020; 8 Kim (10.1016/j.net.2022.10.009_bib24) 2020; 364 Akcay (10.1016/j.net.2022.10.009_bib35) 2018 Lin (10.1016/j.net.2022.10.009_bib10) 2021; 154 Lee (10.1016/j.net.2022.10.009_bib8) 2018; 119 Pedregosa (10.1016/j.net.2022.10.009_bib29) 2011; 12 Kim (10.1016/j.net.2022.10.009_bib33) 2019 Schapire (10.1016/j.net.2022.10.009_bib39) 2003 Bergstra (10.1016/j.net.2022.10.009_bib30) 2011; 24 Kingma (10.1016/j.net.2022.10.009_bib25) 2014 Breiman (10.1016/j.net.2022.10.009_bib38) 1996; 24 Snoek (10.1016/j.net.2022.10.009_bib32) 2012; 25 Lee (10.1016/j.net.2022.10.009_bib20) 2021; 99 |
| References_xml | – volume: 8 start-page: 196727 year: 2020 end-page: 196746 ident: bib7 article-title: Algorithm for autonomous power-increase operation using deep reinforcement learning and a rule-based system publication-title: IEEE Access – year: 2016 ident: bib26 article-title: SGDR: Stochastic Gradient Descent with Warm Restarts – volume: 99 year: 2021 ident: bib20 article-title: A convolutional neural network model for abnormality diagnosis in a nuclear power plant publication-title: Appl. Soft Comput. – volume: 160 year: 2020 ident: bib12 article-title: Neural-based time series forecasting of loss of coolant accidents in nuclear power plants publication-title: Expert Syst. Appl. – volume: 397 year: 2022 ident: bib17 article-title: Operation validation system to prevent human errors in nuclear power plants publication-title: Nucl. Eng. Des. – volume: 154 year: 2021 ident: bib10 article-title: Deep learning schemes for event identification and signal reconstruction in nuclear power plants with sensor faults publication-title: Ann. Nucl. Energy – year: 2014 ident: bib25 article-title: Adam: A Method for Stochastic Optimization – volume: 24 year: 2011 ident: bib30 article-title: Algorithms for hyperparameter optimization publication-title: Adv. Neural Inf. Process. Syst. – volume: 165 year: 2022 ident: bib16 article-title: Design of computerized operator support system for technical specification monitoring publication-title: Ann. Nucl. Energy – volume: 94 start-page: 759 year: 2009 end-page: 762 ident: bib19 article-title: Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks publication-title: Reliab. Eng. Syst. Saf. – volume: 8 start-page: 177363 year: 2020 end-page: 177373 ident: bib4 article-title: Application of a deep learning technique to the development of a fast accident scenario identifier publication-title: IEEE Access – volume: 120 start-page: 219 year: 2019 end-page: 222 ident: bib15 article-title: An artificial neural network based anomaly detection algorithm for nuclear power plants publication-title: Transactions – start-page: 1 year: 2018 end-page: 8 ident: bib14 article-title: A deep learning approach to anomaly detection in nuclear reactors publication-title: 2018 International Joint Conference on Neural Networks (IJCNN) – volume: 13 year: 2012 ident: bib31 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – volume: 300 start-page: 89 year: 2021 ident: bib28 article-title: Evaluation of deep autoencoder based anomaly detection with cold neutron source facility in HANARO publication-title: Transact. Korean Nuclear Soc. Virtual Autumn Meeting – volume: 25 year: 2012 ident: bib32 article-title: Practical Bayesian optimization of machine learning algorithms publication-title: Adv. Neural Inf. Process. Syst. – volume: 200 year: 2022 ident: bib5 article-title: Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code publication-title: Expert Syst. Appl. – year: 2018 ident: bib36 article-title: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection publication-title: International Conference on Learning Representations – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: bib38 article-title: Bagging predictors publication-title: Mach. Learn. – start-page: 258 year: 2021 end-page: 264 ident: bib3 article-title: Development of Long-Term prediction algorithm based on component states using BiLSTM and attention mechanism publication-title: 2021 5th International Conference on System Reliability and Safety (ICSRS) – volume: 32 year: 2019 ident: bib27 article-title: Pytorch: an imperative style, high-performance deep learning library publication-title: Adv. Neural Inf. Process. Syst. – year: 2019 ident: bib33 article-title: Rapp: novelty detection with reconstruction along projection pathway publication-title: International Conference on Learning Representations – volume: 5 start-page: 241 year: 1992 end-page: 259 ident: bib37 article-title: Stacked generalization publication-title: Neural Network. – start-page: 622 year: 2018 end-page: 637 ident: bib35 article-title: Ganomaly: semisupervised anomaly detection via adversarial training publication-title: Asian Conference on Computer Vision – volume: 364 year: 2020 ident: bib24 article-title: RNN-Based online anomaly detection in nuclear reactors for highly imbalanced datasets with uncertainty publication-title: Nucl. Eng. Des. – volume: 10 start-page: 70428 year: 2022 end-page: 70439 ident: bib34 article-title: Quantile autoencoder with abnormality accumulation for anomaly detection of multivariate sensor data publication-title: IEEE Access – volume: 186 year: 2021 ident: bib2 article-title: Real-time prediction of nuclear power plant parameter trends following operator actions publication-title: Expert Syst. Appl. – volume: 142 year: 2020 ident: bib6 article-title: Development of a generative adversarial-network-based signal reconstruction method for nuclear power plants publication-title: Ann. Nucl. Energy – volume: 46 start-page: 501 year: 2014 end-page: 512 ident: bib1 article-title: Contribution of HANARO irradiation technologies to national nuclear R&D publication-title: Nucl. Eng. Technol. – volume: 119 start-page: 287 year: 2018 end-page: 299 ident: bib8 article-title: Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework publication-title: Ann. Nucl. Energy – volume: 79 start-page: 8 year: 2015 end-page: 21 ident: bib23 article-title: On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs publication-title: Prog. Nucl. Energy – volume: 133 start-page: 400 year: 2019 end-page: 408 ident: bib11 article-title: Deep rectifier neural network applied to the accident identification problem in a PWR nuclear power plant publication-title: Ann. Nucl. Energy – volume: 35 start-page: 93 year: 1998 end-page: 100 ident: bib21 article-title: Real-time nuclear power plant monitoring with neural network publication-title: J. Nucl. Sci. Technol. – volume: 56 start-page: 599 year: 2019 end-page: 607 ident: bib9 article-title: Proposal of an insider sabotage detection method for nuclear security using deep learning publication-title: J. Nucl. Sci. Technol. – start-page: 3 year: 2021 end-page: 7 ident: bib22 article-title: Deep learning-based anomaly detection in nuclear reactor cores publication-title: Proceedings of the International Conference on Mathematics & Computational Methods Applied to Nuclear Science & Engineering – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bib29 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 222 year: 2022 ident: bib13 article-title: An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system publication-title: Reliab. Eng. Syst. Saf. – volume: 60 start-page: 233 year: 2002 end-page: 244 ident: bib18 article-title: Nuclear reactor monitoring with the combination of neural network and expert system publication-title: Math. Comput. Simulat. – start-page: 149 year: 2003 end-page: 171 ident: bib39 article-title: The Boosting Approach to Machine Learning: an Overview, Nonlinear Estimation and Classification – start-page: 1 year: 2018 ident: 10.1016/j.net.2022.10.009_bib14 article-title: A deep learning approach to anomaly detection in nuclear reactors – volume: 222 year: 2022 ident: 10.1016/j.net.2022.10.009_bib13 article-title: An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2022.108446 – volume: 24 year: 2011 ident: 10.1016/j.net.2022.10.009_bib30 article-title: Algorithms for hyperparameter optimization publication-title: Adv. Neural Inf. Process. Syst. – start-page: 149 year: 2003 ident: 10.1016/j.net.2022.10.009_bib39 – start-page: 258 year: 2021 ident: 10.1016/j.net.2022.10.009_bib3 article-title: Development of Long-Term prediction algorithm based on component states using BiLSTM and attention mechanism – volume: 60 start-page: 233 issue: 3–5 year: 2002 ident: 10.1016/j.net.2022.10.009_bib18 article-title: Nuclear reactor monitoring with the combination of neural network and expert system publication-title: Math. Comput. Simulat. doi: 10.1016/S0378-4754(02)00018-6 – volume: 79 start-page: 8 year: 2015 ident: 10.1016/j.net.2022.10.009_bib23 article-title: On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs publication-title: Prog. Nucl. Energy doi: 10.1016/j.pnucene.2014.10.013 – year: 2018 ident: 10.1016/j.net.2022.10.009_bib36 article-title: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection – year: 2014 ident: 10.1016/j.net.2022.10.009_bib25 – volume: 142 year: 2020 ident: 10.1016/j.net.2022.10.009_bib6 article-title: Development of a generative adversarial-network-based signal reconstruction method for nuclear power plants publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2020.107410 – volume: 94 start-page: 759 issue: 3 year: 2009 ident: 10.1016/j.net.2022.10.009_bib19 article-title: Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2008.08.005 – year: 2016 ident: 10.1016/j.net.2022.10.009_bib26 – start-page: 622 year: 2018 ident: 10.1016/j.net.2022.10.009_bib35 article-title: Ganomaly: semisupervised anomaly detection via adversarial training – start-page: 3 year: 2021 ident: 10.1016/j.net.2022.10.009_bib22 article-title: Deep learning-based anomaly detection in nuclear reactor cores – volume: 133 start-page: 400 year: 2019 ident: 10.1016/j.net.2022.10.009_bib11 article-title: Deep rectifier neural network applied to the accident identification problem in a PWR nuclear power plant publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2019.05.039 – volume: 397 year: 2022 ident: 10.1016/j.net.2022.10.009_bib17 article-title: Operation validation system to prevent human errors in nuclear power plants publication-title: Nucl. Eng. Des. doi: 10.1016/j.nucengdes.2022.111949 – volume: 200 year: 2022 ident: 10.1016/j.net.2022.10.009_bib5 article-title: Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.116966 – volume: 8 start-page: 196727 year: 2020 ident: 10.1016/j.net.2022.10.009_bib7 article-title: Algorithm for autonomous power-increase operation using deep reinforcement learning and a rule-based system publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3034218 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.net.2022.10.009_bib29 article-title: Scikit-learn: machine learning in python publication-title: J. Mach. Learn. Res. – volume: 5 start-page: 241 issue: 2 year: 1992 ident: 10.1016/j.net.2022.10.009_bib37 article-title: Stacked generalization publication-title: Neural Network. doi: 10.1016/S0893-6080(05)80023-1 – volume: 10 start-page: 70428 year: 2022 ident: 10.1016/j.net.2022.10.009_bib34 article-title: Quantile autoencoder with abnormality accumulation for anomaly detection of multivariate sensor data publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3187426 – volume: 13 issue: 2 year: 2012 ident: 10.1016/j.net.2022.10.009_bib31 article-title: Random search for hyper-parameter optimization publication-title: J. Mach. Learn. Res. – volume: 99 year: 2021 ident: 10.1016/j.net.2022.10.009_bib20 article-title: A convolutional neural network model for abnormality diagnosis in a nuclear power plant publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106874 – volume: 364 year: 2020 ident: 10.1016/j.net.2022.10.009_bib24 article-title: RNN-Based online anomaly detection in nuclear reactors for highly imbalanced datasets with uncertainty publication-title: Nucl. Eng. Des. doi: 10.1016/j.nucengdes.2020.110699 – volume: 46 start-page: 501 issue: 4 year: 2014 ident: 10.1016/j.net.2022.10.009_bib1 article-title: Contribution of HANARO irradiation technologies to national nuclear R&D publication-title: Nucl. Eng. Technol. doi: 10.5516/NET.07.2014.006 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.net.2022.10.009_bib38 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1007/BF00058655 – volume: 35 start-page: 93 issue: 2 year: 1998 ident: 10.1016/j.net.2022.10.009_bib21 article-title: Real-time nuclear power plant monitoring with neural network publication-title: J. Nucl. Sci. Technol. doi: 10.1080/18811248.1998.9733829 – volume: 120 start-page: 219 issue: 1 year: 2019 ident: 10.1016/j.net.2022.10.009_bib15 article-title: An artificial neural network based anomaly detection algorithm for nuclear power plants publication-title: Transactions – volume: 154 year: 2021 ident: 10.1016/j.net.2022.10.009_bib10 article-title: Deep learning schemes for event identification and signal reconstruction in nuclear power plants with sensor faults publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2020.108113 – volume: 8 start-page: 177363 year: 2020 ident: 10.1016/j.net.2022.10.009_bib4 article-title: Application of a deep learning technique to the development of a fast accident scenario identifier publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3026104 – volume: 56 start-page: 599 issue: 7 year: 2019 ident: 10.1016/j.net.2022.10.009_bib9 article-title: Proposal of an insider sabotage detection method for nuclear security using deep learning publication-title: J. Nucl. Sci. Technol. doi: 10.1080/00223131.2019.1611501 – volume: 119 start-page: 287 year: 2018 ident: 10.1016/j.net.2022.10.009_bib8 article-title: Autonomous operation algorithm for safety systems of nuclear power plants by using long-short term memory and function-based hierarchical framework publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2018.05.020 – volume: 160 year: 2020 ident: 10.1016/j.net.2022.10.009_bib12 article-title: Neural-based time series forecasting of loss of coolant accidents in nuclear power plants publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113699 – year: 2019 ident: 10.1016/j.net.2022.10.009_bib33 article-title: Rapp: novelty detection with reconstruction along projection pathway – volume: 186 year: 2021 ident: 10.1016/j.net.2022.10.009_bib2 article-title: Real-time prediction of nuclear power plant parameter trends following operator actions publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115848 – volume: 300 start-page: 89 issue: 69 year: 2021 ident: 10.1016/j.net.2022.10.009_bib28 article-title: Evaluation of deep autoencoder based anomaly detection with cold neutron source facility in HANARO publication-title: Transact. Korean Nuclear Soc. Virtual Autumn Meeting – volume: 25 year: 2012 ident: 10.1016/j.net.2022.10.009_bib32 article-title: Practical Bayesian optimization of machine learning algorithms publication-title: Adv. Neural Inf. Process. Syst. – volume: 32 year: 2019 ident: 10.1016/j.net.2022.10.009_bib27 article-title: Pytorch: an imperative style, high-performance deep learning library publication-title: Adv. Neural Inf. Process. Syst. – volume: 165 year: 2022 ident: 10.1016/j.net.2022.10.009_bib16 article-title: Design of computerized operator support system for technical specification monitoring publication-title: Ann. Nucl. Energy doi: 10.1016/j.anucene.2021.108661 |
| SSID | ssj0064470 |
| Score | 2.3301249 |
| Snippet | The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO... |
| SourceID | nrf doaj crossref elsevier |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| StartPage | 475 |
| SubjectTerms | Anomaly detection Autoencoder Deep learning Nuclear reactor Research reactor 원자력공학 |
| Title | Development of deep autoencoder-based anomaly detection system for HANARO |
| URI | https://dx.doi.org/10.1016/j.net.2022.10.009 https://doaj.org/article/c8914d10a2444332aa84ffb7ddfd206e https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002929635 |
| Volume | 55 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | Nuclear Engineering and Technology, 2023, 55(2), , pp.475-483 |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELagEwyIpygvWYIJKZCkeThjQVQtiCIQlbpZjh8IWpIqSgf-PXdxUrVLWViSyHFi63z2dyefvyPkKgBM1MbEjlC-BAelEztJCrosTMd0Ig0WSIjnnZ-HUX8UPI7D8VKqL4wJs_TAVnC3kiVeoDxXAA4h15YQLDAmjZUyyncjjasvYF7jTNk1GEA-tkchYToj41-zn1lFdoHPD46h799UYV3JCiJVxP0rwLSZFWYJcnq7ZKe2FWnX9nGPbOhsn2wvMQgekMFS0A_NDVVaz6iYlznyUypdOIhSioos_xbTH3hdVqFXGbUMzhRMVtrvDrtvL4dk1Ht4v-87dXYER4IJUzo-M4AsQRQlTHuCwZOLU8iNlWc8o6XyotRTzDUdIYNYK1iaYvDFlIhZmEgQyhFpZXmmjwkFceKGXipDpcCBiYQvpUwC5QsW-kLqNnEbCXFZU4djBospb2LEvjgIlaNQsQiE2ibXi09mljdjXeU7FPuiIlJeVwWgCLxWBP6XIrRJ0Awar60HaxXArz7XtX0JA8wn8rNqFu8fOZ8UHPyJAX7j4c7yyX_08JRsYbJ6G_N9RlplMdfnYNKU6UWlvXB9emW_fufxbw |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Development+of+deep+autoencoder-based+anomaly+detection+system+for+HANARO&rft.jtitle=Nuclear+engineering+and+technology&rft.au=Ryu%2C+Seunghyoung&rft.au=Jeon%2C+Byoungil&rft.au=Seo%2C+Hogeon&rft.au=Lee%2C+Minwoo&rft.date=2023-02-01&rft.pub=Elsevier+B.V&rft.issn=1738-5733&rft.volume=55&rft.issue=2&rft.spage=475&rft.epage=483&rft_id=info:doi/10.1016%2Fj.net.2022.10.009&rft.externalDocID=S1738573322004909 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1738-5733&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1738-5733&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1738-5733&client=summon |