Synonyme wurden verwendet für: learning  deep learning
Suche ohne Synonyme: keywords:( deep learning)

1–20 von 36 Ergebnissen
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    Traffic speed prediction for intelligent transportation system based on a deep feature fusion model

    Li, Linchao / Qu, Xu / Zhang, Jian et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: deep learning , machine learning

    GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

    Guo, Jingqiu / Liu, Yangzexi / Yang, Qingyan (Ken) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

    Wang, Hao / Yuan, Yun / Yang, Xianfeng Terry et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep neural network , deep reinforcement learning , Q-learning

    A data-driven lane-changing behavior detection system based on sequence learning

    Gao, Jun / Murphey, Yi Lu / Yi, Jiangang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: sequence learning , deep LSTM

    Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

    Cheng, Yanqiu / Hu, Xianbiao / Chen, Kuanmin et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep Q-learning , reinforcement learning

    Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning

    Xu, Ming / Wu, Jianping / Huang, Ling et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: deep reinforcement learning

    Convolutional neural network for detecting railway fastener defects using a developed 3D laser system

    Zhan, You / Dai, Xianxing / Yang, Enhui et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    A cold-start-free reinforcement learning approach for traffic signal control

    Xiao, Nan / Yu, Liang / Yu, Jinqiang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning , reinforcement learning

    Deep machine learning for structural health monitoring on ship hulls using acoustic emission method

    Karvelis, Petros / Georgoulas, George / Kappatos, Vassilios et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning , deep belief networks

    Graph attention temporal convolutional network for traffic speed forecasting on road networks

    Zhang, Ke / He, Fang / Zhang, Zhengchao et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    The construction of a neural network proxy model for ship hull design based on multi-fidelity datasets and the parameter freezing strategy

    Ao, Yu / Li, Shaofan / Li, Yunbo et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: Deep learning , machine learning

    Real-time traffic incident detection based on a hybrid deep learning model

    Li, Linchao / Lin, Yi / Du, Bowen et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    STD-Yolov5: a ship-type detection model based on improved Yolov5

    Ning, Yue / Zhao, Lining / Zhang, Can et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: deep learning

    Artificial intelligence for traffic signal control based solely on video images

    Jeon, Hyunjeong / Lee, Jincheol / Sohn, Keemin | Taylor & Francis Verlag | 2018
    Schlagwörter: deep learning , reinforcement learning (RL)

    DRL-based adaptive signal control for bus priority service under connected vehicle environment

    Zhang, Xinshao / He, Zhaocheng / Zhu, Yiting et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning

    Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning

    Zhang, Weibin / Yu, Yinghao / Qi, Yong et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: deep learning

    Predicting future locations of moving objects with deep fuzzy-LSTM networks

    Li, Mingxiao / Lu, Feng / Zhang, Hengcai et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: deep learning

    DLW-Net model for traffic flow prediction under adverse weather

    Yao, Ronghan / Zhang, Wensong / Long, Meng | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    Development of a novel engine power model to estimate heavy-duty truck fuel consumption

    Kan, Yuheng / Liu, Hao / Lu, Xiaoyun et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning