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1–20 von 623 Ergebnissen
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    1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

    Koushik Chennakesavan / Magnus A. Haw / Alexandre Quintart | NTRS
    Schlagwörter: Machine learning

      1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

      Koushik Chennakesavan / Magnus A. Haw / Alexandre Quintart | NTRS
      Schlagwörter: Machine learning

    3D vehicle sensor based on monocular vision

    Ponsa, D. / Lopez, A. / Lumbreras, F. et al. | Tema Archiv | 2005
    Schlagwörter: maschinelles Sehen , Lernen

    Accelerating In-Vehicle Network Intrusion Detection System Using Binarized Neural Network

    Ma, Di / Zhang, Linxi / Yan, Xuke | SAE Technical Papers | 2022
    Schlagwörter: Machine learning

    Acceptance of Smart Automated Comfort Functionalities in Vehicles

    Guinea, Maria / Stang, Marco / Nitsche, Irma et al. | TIBKAT | 2021
    Schlagwörter: Machine learning.

    A Combined Markov Chain and Reinforcement Learning Approach for Powertrain-Specific Driving Cycle Generation

    Dietrich, Maximilian / Sarkar, Mouktik / Chen, Xi | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    A Comparative Study of Longitudinal Vehicle Control Systems in Vehicle-to-Infrastructure Connected Corridor

    Fitzpatrick, Benjamin / King, Brian / Yoon, Hwan-Sik et al. | SAE Technical Papers | 2023
    Schlagwörter: Machine learning

    A Comparative Study of Recurrent Neural Network Architectures for Battery Voltage Prediction

    ZHU, DI / Cho, Gyouho / Campbell, Jeffrey | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    A Comparative Study with J48 and Random Tree Classifier for Predicting the State of Hydraulic Braking System through Vibration Signals

    Gopalan, Anitha / Arockia Dhanraj, Joshuva / Subramaniam, Mohankumar et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    A Comparison Study of Font Reconstruction Using Differential Evolution

    Yahya, N. Roslan. Z. R. / Muhamad, W. Z. A. W. / Rusdi, N. A. | TIBKAT | 2021
    Schlagwörter: Machine learning.

    A Comparison Study on Control Strategies for Optimization of an Anti-Lock Brake System Algorithm Based on Tire Force Measurement in Pure and Combined Slip Conditions of an Automobile

    Mehta, Harshal Piyush / Maurya, Mithilesh / Shaikh, Parvez Shagir et al. | SAE Technical Papers | 2023
    Schlagwörter: Machine learning

    A Comprehensive Analysis of Methods to Write Requirements for Machine Learning Components used in Autonomous Vehicles

    Gil Batres, Andrea / Avalos Gonzalez, Carlos / Krishnamoorthy, Jayalekshmi et al. | SAE Technical Papers | 2023
    Schlagwörter: Machine learning

    Acoustic Model Reduction for the Design of Acoustic Treatments

    Poulos, Athanasios / Jacqmot, Jonathan / Kayvantash, Kambiz et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Acoustic sleepiness analysis - using speech input from driver assistance systems for the phonetic detection of critical driver states

    Krajewski, J. / Sommer, D. / Schupp, T. et al. | Tema Archiv | 2009
    Schlagwörter: maschinelles Lernen

    A Crack Detection Method for Self-Piercing Riveting Button Images through Machine Learning

    Freis, Amanda / Huff, Garret / Huang, Li et al. | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    Active-Learning Method: An Effective Way to Generate Ground Truth Data to Test & Validate ADAS Function Development

    Kumari, Anita / Katariya, Rashmi | SAE Technical Papers | 2024
    Schlagwörter: Machine learning

    A Decentralized Multi-agent Energy Management Strategy Based on a Look-Ahead Reinforcement Learning Approach

    Khalatbarisoltani, Arash / Boulon, Loic / Kandidayeni, Mohsen et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning

    Zhang, Mingheng / Lv, Xinfei / Wan, Xing et al. | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    A Deep Ensemble Network Model for Refined Traffic Volume Prediction Considering Spatial-Temporal Features

    Tao, Lu / Zhou, Tian / Liu, Wan et al. | SAE Technical Papers | 2020
    Schlagwörter: Machine learning