Synonyme wurden verwendet für: Machine learning
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61–80 von 109 Ergebnissen
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    Safety Assurance Concepts for Automated Driving Systems

    Sarvi, Majid / Sweatman, Peter / Ballingall, Stuart | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    Machine Learning Algorithm for the Prediction of Idle Combustion Uniformity

    Zouani, Abdelkrim / Li, Xiaoqi | SAE Technical Papers | 2019
    Schlagwörter: Machine learning

    UUV teams, control from a biological perspective

    McDowell, P. / Chen, J. / Bourgeois, B. | Tema Archiv | 2002
    Schlagwörter: maschinelles Lernen

    High Altitude Ice Crystal Detection with Aircraft X-band Weather Radar

    Lukas, Jan / Badin, Pavel | SAE Technical Papers | 2019
    Schlagwörter: Machine learning

    Clustering-Based Trajectory Prediction of Vehicles Interacting with Vulnerable Road Users

    Sonka, Adrian / Henze, Roman / Thal, Silvia | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    EARLY INFORMATION PARAMETER-SET ANALYSIS FOR SATELLITE CLOSE APPROACHES USING MACHINE LEARNING

    B I Robertson / A Mashiku | NTRS
    Schlagwörter: machine learning

    Developing Prediction Based Algorithms for Energy and Exergy Flow

    Kim, CDT Tae / James, LTC Corey / Jane, Robert | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Artificial Intelligence for Damage Detection in Automotive Composite Parts: A Use Case

    Ciampaglia, Alberto / De Gregorio, Alessandro / Mastropietro, Antonio et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Development of Coated Gasoline Particulate Filter Design Method Combining Simulation and Multi-Objective Optimization

    Takahasi, Hiroaki / Maekawa, Ryosuke / Ota, Yuki | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    A New Optimal Design of Stable Feedback Control of Two-Wheel System Based on Reinforcement Learning

    Zhu, Xuebin / Yu, Zhenghong | SAE Technical Papers | 2023
    Schlagwörter: Machine learning

    Prediction of Aircraft Estimated Time of Arrival Using A Supervised Learning Approach

    James Z Wells / Tejas G Puranik / Krishna M Kalyanam et al. | NTRS
    Schlagwörter: Machine learning

    Machine Learning Based Parameter Calibration for Multi-Scale Material Modeling of Laser Powder Bed Fusion (L-PBF) AlSi10Mg

    Xu, Hongyi / Lai, Wei-Jen / Su, Xuming et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

    Esaid, Danial / Ye, Fei / Wu, Guoyuan et al. | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    Prediction of Vehicle Cabin Occupant Thermal Comfort Using Deep Learning and Computational Fluid Dynamics

    Warey, Alok / Khalighi, Bahram / Venkatesan, Ganesh et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Evolutionary Computation Methods for Helicopter Loads Estimation

    Valdes, Julio J. / Cheung, Catherine / Wang, Weichao | Tema Archiv | 2011
    Schlagwörter: maschinelles Lernen

    RouteE: A Vehicle Energy Consumption Prediction Engine

    Holden, Jacob / Cappellucci, Jeff / Reinicke, Nicholas | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    Model-Free Intelligent Control for Antilock Braking Systems on Rough Roads

    Hamersma, Herman A. / Abreu, Ricardo / Botha, Theunis R. | SAE Technical Papers | 2023
    Schlagwörter: Machine learning

    Physics-Based Misbehavior Detection System for V2X Communications

    Andrade Salazar, Alejandro Antonio / Petit, Jonathan / McDaniel, Patrick Drew et al. | SAE Technical Papers | 2022
    Schlagwörter: Machine learning

    A Review and Outlook on Energy Consumption Estimation Models for Electric Vehicles

    Dubey, Abhishek / Laszka, Aron / Wu, Guoyuan et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Ride Comfort Improvement with Preview Control Semi-active Suspension System Based on Supervised Deep Learning

    Gu, Cansong / Shi, Chenlu / Su, Lili et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning