This application provides an airspace complexity evaluation method based on deep unsupervised learning for air traffic management, which includes the following parts. A stacked autoencoder is used to establish an airspace complexity evaluation model. Input the airspace complexity factors into the stacked autoencoder to obtain the low-dimensional embedded representations of the airspace complexity factors. Cluster the low-dimensional embedded points to capture the centroids of the airspace complexity data. The application utilizes the soft assignment distribution and real assignment distribution of the embedded representations to construct a training loss function which optimizes the airspace complexity evaluation model by gradient descent algorithm. The trained airspace complexity evaluation model and the three obtained cluster centroids describing the airspace complexity level are used to evaluate the current airspace complexity.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    DEEP UNSUPERVISED LEARNING APPROACH , DEVICE AND STORAGE MEDIUM FOR AIRSPACE COMPLEXITY EVALUATION


    Beteiligte:
    DU WENBO (Autor:in) / CAO XIANBIN (Autor:in) / LI BIYUE (Autor:in) / ZHU XI (Autor:in) / LI YUMENG (Autor:in)

    Erscheinungsdatum :

    2021-06-10


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

    Li, Biyue / Du, Wenbo / Zhang, Yu et al. | IEEE | 2022


    An Airspace Complexity Evaluation System Based on Deep Learning

    SUN BO / WEI MING / TAN YINGJIA | Europäisches Patentamt | 2021

    Freier Zugriff

    Defining Airspace Complexity

    Daniel Weckler / Bryan Matthews / Nikunj Oza et al. | NTRS



    Airspace operation complexity evaluation method based on deep convolutional neural network

    XIE HUA / ZHANG MINGHUA / CHEN HAIYAN et al. | Europäisches Patentamt | 2021

    Freier Zugriff