모델 지식 및 신경망 기반 모델 불확실성 실시간 추정 기법Model Uncertainty Estimation Using Domain Knowledge and Neural Networks

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In a design process of a missile system, Monte-Carlo simulations are conducted for analysis of uncertainty that affects performance of the missile. Time-series data obtained from simulations not only can improve the understanding of the model, but also aid to estimate permanent model uncertainties. Therefore, this research focuses on the estimation of model uncertainty such as a location of center of pressure, fin bias that exist consistently during flight. Complex nonlinear relationships between the model uncertainty and states in flight can be approximated by 1-Dimensional convolutional neural networks(1D CNN) which are known as a proper model for adapting to time-series data. Features used for input in 1D CNN are extracted considering domain knowledge about missile dynamics and data which can be obtained from sensors. 1D CNN models estimate the uncertainty from the entire length of time-series data. As a result, the accurate model for the missile system is attained and the real-time application helps to continuously observe and manage the missile system.
Publisher
한국군사과학기술학회
Issue Date
2022-06-09
Language
Korean
Citation

2022 한국군사과학기술학회 종합학술대회

URI
http://hdl.handle.net/10203/297821
Appears in Collection
AE-Conference Papers(학술회의논문)
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