Quick Access Recorder (QAR) data contains a large number of flight parameters used to record flight information during flight, and is increasingly utilized to analyze and evaluate flight safety. Feature extraction of QAR data is a prerequisite for further analysis in many applications and studies and therefore an important research topic. In this study, a sequence-parameter attention based convolutional autoencoder (SPA-CAE) model is proposed for feature extraction of QAR data. Data landed at Changshui Airport in Kunming were used as experimental data and the results show that our model is better able to perform feature extraction of QAR data and discover the distribution pattern of extracted features.


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    Titel :

    Feature Extraction of QAR Data via Sequence-Parameter Attention Based Convolutional Autoencoder Model


    Beteiligte:
    Wang, Qixin (Autor:in) / Qin, Kun (Autor:in) / Lu, Binbin (Autor:in) / Huang, Rongshun (Autor:in)


    Erscheinungsdatum :

    20.10.2021


    Format / Umfang :

    1710625 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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