Flight Anomaly Detection via a Deep Hybrid Model

In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The...

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Veröffentlicht in:	Aerospace Jg. 9; H. 6; S. 329
Hauptverfasser:	Qin, Kun, Wang, Qixin, Lu, Binbin, Sun, Huabo, Shu, Ping
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2022
Schlagworte:	Accident investigation Accident investigations Accidents Aerospace industry Aircraft accidents & safety Aircraft landing Airport security Airports Algorithms Anomalies Aviation Civil aviation Clustering convolutional autoencoder deep hybrid model Deep learning Detectors Feature extraction flight anomaly detection Forensic engineering HDBSCAN clustering algorithm Machine learning Neural networks Object recognition Random variables Risk management Safety and security measures Time series time-feature attention China
ISSN:	2226-4310, 2226-4310
Online-Zugang:	Volltext
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