Synonyme wurden verwendet für: learning
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1–20 von 37 Ergebnissen
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    Automatic Obstructive Sleep Apnea Identification Using First Order Statistics Features of Electrocardiogram and Machine Learning

    Indrawati, Aida Noor / Nuryani, Nuryani / Wiharto, Wiharto et al. | Springer Verlag | 2024
    Schlagwörter: Machine learning

    Railway Bogie Diagnostics Using Machine Learning and Bayesian Net Reasoning Approaches

    Girstmair, Bernhard / Moshammer, Thomas | Springer Verlag | 2022
    Schlagwörter: Machine learning

    Low-Cost Surface Classification System Supported by Deep Neural Models

    Sánchez, Ignacio / Velasco, Juan M. / Castillo, Juan J. et al. | Springer Verlag | 2022
    Schlagwörter: Deep learning

    Identification of Fault Severity of Rolling Element Bearing Using Image Augmentation and Mobile Net V_2 Convolutional Neural Network

    Akhenia, P. / Jamani, H. / Vakharia, V. | Springer Verlag | 2022
    Schlagwörter: Deep transfer learning

    Data-Driven Robust Control for Railway Driven Independently Rotating Wheelsets Using Deep Deterministic Policy Gradient

    Wei, Juyao / Lu, Zhenggang / Yang, Zhe et al. | Springer Verlag | 2022
    Schlagwörter: Reinforcement learning

    Acoustic Monitoring of Rail Faults in the German Railway Network

    Pieringer, Astrid / Stangl, Matthias / Rothhämel, Jörg et al. | Springer Verlag | 2021
    Schlagwörter: Machine learning

    Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems

    Borah, Kaustav Jyoti / Talukdar, Rajashree | Springer Verlag | 2020
    Schlagwörter: Machine learning , Q learning

    A Reinforcement Learning Enhanced Fuzzy Control for Real-Time Off-Road Traction System

    Vantsevich, Vladimir / Gorsich, David / Lozynskyy, Andriy et al. | Springer Verlag | 2020
    Schlagwörter: Reinforcement learning

    Lateral Control Design for Autonomous Vehicles Using a Big Data-Based Approach

    Fényes, Dániel / Németh, Balázs / Gáspár, Péter | Springer Verlag | 2020
    Schlagwörter: Machine learning

    Condition Monitoring of Rail Vehicle Suspension Elements: A Machine Learning Approach

    Karlsson, Henrik / Qazizadeh, Alireza / Stichel, Sebastian et al. | Springer Verlag | 2020
    Schlagwörter: Machine learning

    A Grey Box Model Approach for the Prediction of Tire Energy Loss

    Burger, Michael / Steidel, Stefan | Springer Verlag | 2020
    Schlagwörter: Machine learning

    Spacecraft Anomaly Detection via Transformer Reconstruction Error

    Meng, Hengyu / Zhang, Yuxuan / Li, Yuanxiang et al. | Springer Verlag | 2020
    Schlagwörter: Deep learning

    Deep Fundamental Diagram Network for Real-Time Pedestrian Dynamics Analysis

    Ma, Qing / Kang, Yu / Song, Weiguo et al. | Springer Verlag | 2020
    Schlagwörter: Deep learning

    Experimental Setups to Observe Evasion Maneuvers in Low and High Densities

    Kleinmeier, Benedikt / Köster, Gerta | Springer Verlag | 2020
    Schlagwörter: Learning effects

    Network Control Systems for Large-Scale Constellations

    Cappaert, Jeroen / Nag, Sreeja | Springer Verlag | 2020
    Schlagwörter: Machine learning

    Small Satellites and Innovations in Terminal and Teleport Design, Deployment, and Operation

    Jarrold, Martin / Meltzer, David | Springer Verlag | 2020
    Schlagwörter: Machine learning

    Effective Durability and Damage Tolerance Training: New Methods for Modern Learners

    Chapman, Brandon D. | Springer Verlag | 2019
    Schlagwörter: Learning

    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

    Multi Agent Reinforcement Learning for Gridworld Soccer Leadingpass

    Sari, Safreni Candra / Prihatmanto, Ary Setijadi / Kuspriyanto | Springer Verlag | 2013
    Schlagwörter: Multiagent learning system , reinforcement learning