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    A Machine Learning Approach to Load Tracking and Usage Monitoring for Legacy Fleets

    Cheung, Catherine / Sehgal, Srishti / Valdés, Julio J. | Springer Verlag | 2019
    Schlagwörter: Machine learning

    Changing the Philosophy of Full-Scale-Fatigue-Tests Derived from 50 Years of IABG Experience Towards a Virtual Environment

    Hilfer, Gerhard / Tusch, Olaf / Wu, Don et al. | Springer Verlag | 2019
    Schlagwörter: Machine learning

    Modeling of aircraft fuel consumption using machine learning algorithms

    Baumann, S. / Klingauf, U. | Springer Verlag | 2020
    Schlagwörter: Machine learning

    Sensitivity and calibration of turbulence model in the presence of epistemic uncertainties

    Freier Zugriff
    Da Ronch, Andrea / Panzeri, Marco / Drofelnik, Jernej et al. | Springer Verlag | 2020
    Schlagwörter: Machine-learning

    Review of Advanced Driver Assistance Systems and Their Applications for Collision Avoidance in Urban Driving Scenario

    Narkhede, Manish M. / Chopade, Nilkanth B. | Springer Verlag | 2021
    Schlagwörter: Machine learning , Machine Learning

    Evaluation of the aircraft fuel economy using advanced statistics and machine learning

    Freier Zugriff
    Baumann, S. / Neidhardt, T. / Klingauf, U. | Springer Verlag | 2021
    Schlagwörter: Machine learning

    WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation

    Freier Zugriff
    Andersson, Olov / Doherty, Patrick / Lager, Mårten et al. | Springer Verlag | 2021
    Schlagwörter: Machine Learning

    Sensitivity Analysis and Optimization for a Trainer Aircraft Configuration

    Subrahmanya, M. B. / Suman, V. K. / Kaushik, K. N. et al. | Springer Verlag | 2022
    Schlagwörter: Machine Learning

    Optimal Impulsive Maneuver Planning for Station Acquisition of Geosynchronous Satellites

    Srivatsa, M. R. / Singh, Gurpreet | Springer Verlag | 2022
    Schlagwörter: Machine Learning

    Smart Monitoring of Drunk Driver Using IOT and Machine Learning Based Anomaly Detection

    Karimisetty, Sujatha / Arjuna Rao, A. / Dinesh Kumar, Y. et al. | Springer Verlag | 2022
    Schlagwörter: Smart Monitoring of Drunk Driver using IOT and Machine Learning , Support Vector Machine Learning Based Anomaly Detection (SVMAD)

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