QAR (quick access recorder) data exceedance and statistical analyses is used to flight safety event reasoning. Machine learning algorithms are applied to fully automatically classify pilot behaviors into classes. Both classifications (supervised) and clustering (non-supervised) methods are tried, for instance, gentle Adaboost (Adaptive Boosting) classification, K-means and Gustafson-Kessel (G-K) clustering. Routine exceedance events are assigned to gentle adaboost labels for training model and evaluating testing result, while K-means and G-K clustering are conducted in free-style. All results are compared, and findout unveils that each method emphasizes different series of pilot performances given by a method named Minimum Redundancy Maximum Relevance (mRMR). After theoretical study, we realize in our cases, G-K clustering is much more accurate than K-means in clustering methods.


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    Titel :

    Application of Machine Learning in Flight Safety Event Reasoning


    Beteiligte:
    Li, Tong (Autor:in) / Chi, Ying (Autor:in) / Yang, Rui (Autor:in) / Li, Yan (Autor:in)


    Erscheinungsdatum :

    2021-10-20


    Format / Umfang :

    1314190 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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