BROWSE
Detail
Journal Article
딥러닝 기반 항공안전 이상치 탐지 기술 동향
- Authors
- 박노삼
- Issue Date
- 2021-10
- Citation
- 전자통신동향분석, v.36, no.5, pp.82-91
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.
- KSP Keywords
- Aviation Safety, Deep model, Hybrid Technique, Learning methods, aviation systems, data-driven anomaly detection, deep learning(DL), deep learning models, detection techniques, machine learning models, pros and cons
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
