This paper presents an approach to detect electronic components attached to Integrated Modular Avionics modules without any smart or additional interface based on their electrical properties using neural networks. Different LEDs and electric motors are classified, neural networks with different hyperparameters trained and accuracies compared. The neural network approach is compared to a simpler least-square approach. The best performing network is exported into C code and successfully tested on an IMA module. Based on the detected peripherals, the IMA module executes the corresponding application. The approach is validated using a fictive aircraft lighting system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Using Neural Networks to Identify Wired Peripherals Connected to Integrated Modular Avionics Hardware


    Beteiligte:


    Erscheinungsdatum :

    03.10.2021


    Format / Umfang :

    2868125 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Integrated modular avionics

    Prisaznuk, P.J. | IEEE | 1992



    Hardware-Independent Self-Discovery of Peripherals and Modules of a Self-Adaptive Avionics Platform

    Brunner, Matthias / Reinhart, Johannes / Schulz, Bernd et al. | IEEE | 2022



    Autonomous peripherals integration for an adaptive avionics platform

    Marquardt, Oliver / Riedlinger, Marc / Ahmadi, Reza et al. | IEEE | 2016