Nuclear Fusion Pattern Recognition by Ensemble Learning
Nuclear fusion is the process by which two or more atomic nuclei join together to form a single heavier nucleus. This is usually accompanied by the release of large quantities of energy. This energy could be cheaper, cleaner, and safer than other technology currently in use. Experiments in nuclear f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/1207167 |
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| author | G. Farias E. Fabregas I. Martínez J. Vega S. Dormido-Canto H. Vargas |
| author_facet | G. Farias E. Fabregas I. Martínez J. Vega S. Dormido-Canto H. Vargas |
| author_sort | G. Farias |
| collection | DOAJ |
| description | Nuclear fusion is the process by which two or more atomic nuclei join together to form a single heavier nucleus. This is usually accompanied by the release of large quantities of energy. This energy could be cheaper, cleaner, and safer than other technology currently in use. Experiments in nuclear fusion generate a large number of signals that are stored in huge databases. It is impossible to do a complete analysis of this data manually, and it is essential to automate this process. That is why machine learning models have been used to this end in previous years. In the literature, several popular algorithms can be found to carry out the automatic classification of signals. Among these, ensemble methods provide a good balance between success rate and internal information about models. Specifically, AdaBoost algorithm will allow obtaining an explicit set of rules that explains the class for each input data, adding interpretability to the models. In this paper, an innovative approach to perform an online classification, that is, to identify the discharge before it actually ends, using interpretable models is presented. In order to evaluate and reveal the benefits of rule-based models, an illustrative example has been implemented to perform an online classification of five different signals of the TJ-II stellarator fusion device located in Madrid, Spain. |
| format | Article |
| id | doaj-art-4b517cf9b1f24c4ab10fe0240d205ded |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-4b517cf9b1f24c4ab10fe0240d205ded2025-02-03T06:12:49ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/12071671207167Nuclear Fusion Pattern Recognition by Ensemble LearningG. Farias0E. Fabregas1I. Martínez2J. Vega3S. Dormido-Canto4H. Vargas5Pontificia Universidad Catolica de Valparaiso, Avenida Brasil 2147, Valparaiso 2362804, ChileDepartamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan Del Rosal 16, Madrid 28040, SpainPontificia Universidad Catolica de Valparaiso, Avenida Brasil 2147, Valparaiso 2362804, ChileLaboratorio Nacional de Fusión, CIEMAT, Avenida Complutense 40, Madrid 28040, SpainDepartamento de Informática y Automática, Universidad Nacional de Educación a Distancia, Juan Del Rosal 16, Madrid 28040, SpainPontificia Universidad Catolica de Valparaiso, Avenida Brasil 2147, Valparaiso 2362804, ChileNuclear fusion is the process by which two or more atomic nuclei join together to form a single heavier nucleus. This is usually accompanied by the release of large quantities of energy. This energy could be cheaper, cleaner, and safer than other technology currently in use. Experiments in nuclear fusion generate a large number of signals that are stored in huge databases. It is impossible to do a complete analysis of this data manually, and it is essential to automate this process. That is why machine learning models have been used to this end in previous years. In the literature, several popular algorithms can be found to carry out the automatic classification of signals. Among these, ensemble methods provide a good balance between success rate and internal information about models. Specifically, AdaBoost algorithm will allow obtaining an explicit set of rules that explains the class for each input data, adding interpretability to the models. In this paper, an innovative approach to perform an online classification, that is, to identify the discharge before it actually ends, using interpretable models is presented. In order to evaluate and reveal the benefits of rule-based models, an illustrative example has been implemented to perform an online classification of five different signals of the TJ-II stellarator fusion device located in Madrid, Spain.http://dx.doi.org/10.1155/2021/1207167 |
| spellingShingle | G. Farias E. Fabregas I. Martínez J. Vega S. Dormido-Canto H. Vargas Nuclear Fusion Pattern Recognition by Ensemble Learning Complexity |
| title | Nuclear Fusion Pattern Recognition by Ensemble Learning |
| title_full | Nuclear Fusion Pattern Recognition by Ensemble Learning |
| title_fullStr | Nuclear Fusion Pattern Recognition by Ensemble Learning |
| title_full_unstemmed | Nuclear Fusion Pattern Recognition by Ensemble Learning |
| title_short | Nuclear Fusion Pattern Recognition by Ensemble Learning |
| title_sort | nuclear fusion pattern recognition by ensemble learning |
| url | http://dx.doi.org/10.1155/2021/1207167 |
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