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1–20 von 520 Ergebnissen
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    Accelerating In-Vehicle Network Intrusion Detection System Using Binarized Neural Network

    Ma, Di / Zhang, Linxi / Yan, Xuke | SAE Technical Papers | 2022
    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 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

    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

    A Deep Learning-Based Strategy to Initiate Diesel Particle Filter Regeneration

    Roettger, Daniel / De Smet, Frederik / Fan, Ke et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    A Diagnostic Technology of Powertrain Parts that Cause Abnormal Noises Using Artificial Intelligence

    Lee, Dongchul / Yoo, Dongkyu / Jung, Insoo et al. | SAE Technical Papers | 2020
    Schlagwörter: Machine learning

    A Digital Forensic Method to Detect Object based Video Forgery Security Attacks on Surround View ADAS Camera System

    Karki, Maya / S M, Sarala | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    A Digital Twin Based Approach for Simulation and Emulation of an Automotive Paint Workshop

    Ruperez lng, Adrián / Martinez, Aitor / Lopez, Blanca et al. | SAE Technical Papers | 2021
    Schlagwörter: Machine learning

    Adjoint-Based Model Tuning and Machine Learning Strategy for Turbulence Model Improvement

    Ren, Chao / Wu, Haibo / Zhou, Hua et al. | SAE Technical Papers | 2022
    Schlagwörter: Machine learning