기계 학습을 이용한 비행 비정상 검출 기법Anomaly Detection Algorithm for Flight Pattern Analysis using Machine Learning

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This paper aims to propose an anomaly detection technic for a vast amount of flight data. The proposed method is based on the fact that the numberof normal flight patterns are enormous compared tothat ofabnormal flight patterns. The proposed method consists of data transformation, data sampling, and feature generation to make anappropriate data format for the analysis.The representation of eachflight parameters isset to be a mean value in a sampling interval. Then,representation values are gathered into a vector to form a feature point of each flightdata. An unsupervised machine learning approach called DBSCAN (clustering method) with the feature vector is used for detecting potential anomalies in the flight data. Finally, the proposed algorithm is verified by utilizing NASA open database.
Publisher
한국군사과학기술학회
Issue Date
2021-11-12
Language
Korean
Citation

2021 한국군사과학기술학회 종합학술대회

URI
http://hdl.handle.net/10203/290226
Appears in Collection
AE-Conference Papers(학술회의논문)
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