Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks


    Beteiligte:
    Newton Campbell (Autor:in) / Hari Ilangovan (Autor:in) / Irene Gregory (Autor:in) / Sarkis Mikaelian (Autor:in)

    Kongress:

    AIAA 2022 SciTech Forum and Exposition ; 2022 ; San Diego, CA, US


    Medientyp :

    Sonstige


    Format :

    Keine Angabe


    Sprache :

    Englisch




    Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

    Newton H Campbell, Jr / Hari S Ilangovan / Irene Gregory et al. | NTRS


    Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Networks

    Campbell, Newton H. / Ilangovan, Hari S. / Gregory, Irene M. et al. | AIAA | 2022


    Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Networks

    Campbell, Newton H. / Ilangovan, Hari S. / Gregory, Irene M. et al. | TIBKAT | 2022


    Utilizing Generative Adversarial Networks for Medical Data Synthesis and Augmentation to Enhance Model Training

    Jovanovic, Luka / Antonijevic, Milos / Bacanin, Nebojsa et al. | Springer Verlag | 2024


    Augmentation von Kameradaten mit Generative Adversarial Networks (GANs) zur Absicherung automatisierter Fahrfunktionen

    Rigoll, P. / Petersen, P. / Ries, L. et al. | British Library Conference Proceedings | 2022