Advanced machine learning for the detection of single event effects
| Autor: | Dorise, Adrien, Subias, Audine, Travé-Massuyès, Louise, Alonso, Corinne |
|---|---|
| Přispěvatelé: | Centre National d'Études Spatiales [Toulouse] (CNES), Équipe DIagnostic, Supervision et COnduite (LAAS-DISCO), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Équipe Intégration de Systèmes de Gestion de l'Énergie (LAAS-ISGE), Dorise, Adrien |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
Artificial intelligence [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] Electronic applications [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] Single event effects Anomaly detection [SPI.TRON] Engineering Sciences [physics]/Electronics [INFO.INFO-ES] Computer Science [cs]/Embedded Systems [SPI.TRON]Engineering Sciences [physics]/Electronics [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Space radiations Machine learning [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB] [INFO.INFO-ES]Computer Science [cs]/Embedded Systems |
| Zdroj: | RADECS 2022 RADECS 2022, Oct 2022, Venice, Italy |
| Popis: | International audience; With the increase of component complexity, protection against single event effects becomes a critical point for the disponibility and reliability of space systems. In this paper, machine learning is investigated to improve the detection of radiation faults. An algorithm named DYD² that meets space application requirements is proposed. In addition, a study to improve the characterisation of single event effects through feature extraction is described. Finally, results of experimentation based on a heavy-ion campaign test are discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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