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

1–38 von 38 Ergebnissen
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    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

    Traffic congestion forecasting using multilayered deep neural network

    Kumar, Kranti / Kumar, Manoj / Das, Pritikana | Taylor & Francis Verlag | 2024
    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

    Price incentive strategy for the E-scooter sharing service using deep reinforcement learning

    Yun, Hyunsoo / Kim, Eui-Jin / Ham, Seung Woo et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: Deep reinforcement learning

    Fusion attention mechanism bidirectional LSTM for short-term traffic flow prediction

    Li, Zhihong / Xu, Han / Gao, Xiuli et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: deep learning

    Two-stage procedure for transportation mode detection based on sighting data

    Chen, Huey-Kuo / Ho, Hsiao-Ching / Wu, Luo-Yu et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: deep learning

    Fast prediction of turbine energy acquisition capacity under combined action of wave and current based on digital twin method

    Cao, Yu / Tang, Xiaobo / Zhang, Tao et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: deep learning

    PLDA in i-vector based underwater acoustic signals classification

    Song, Yongqiang / Liu, Feng / Shen, Tongsheng | Taylor & Francis Verlag | 2024
    Schlagwörter: Deep learning

    Hybrid deep learning models for short-term demand forecasting of online car-hailing considering multiple factors

    Li, Siteng / Yang, Hang / Cheng, Rongjun et al. | Taylor & Francis Verlag | 2024
    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

    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

    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

    Development of LSTM-MLR hybrid model for radar detector missing and outlier traffic volume correction

    Kim, Dohoon / Kim, Eungcheol | Taylor & Francis Verlag | 2023
    Schlagwörter: deep-learning

    Traffic sign extraction using deep hierarchical feature learning and mobile light detection and ranging (LiDAR) data on rural highways

    Gouda, Maged / Epp, Alexander / Tilroe, Rowan et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Deep learning

    Parameter tuning of EV drivers' charging behavioural model using machine learning techniques

    Fotouhi, Zohreh / Narimani, Hamed / Hashemi, Massoud Reza | Taylor & Francis Verlag | 2023
    Schlagwörter: Deep reinforcement learning (DRL)

    Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

    Khattak, Zulqarnain H. / Rios-Torres, Jackeline / Fontaine, Michael D. et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep learning

    Discharge control policy based on density and speed for deep Q-learning adaptive traffic signal

    Ahmed, Muaid Abdulkareem Alnazir / Khoo, Hooi Ling / Ng, Oon-Ee | Taylor & Francis Verlag | 2023
    Schlagwörter: deep 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

    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

    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

    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

    Electric vehicle charging demand forecasting using deep learning model

    Yi, Zhiyan / Liu, Xiaoyue Cathy / Wei, Ran et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    Joint learning of video images and physiological signals for lane-changing behavior prediction

    Gao, Jun / Yi, Jiangang / Murphey, Yi Lu | Taylor & Francis Verlag | 2022
    Schlagwörter: deep 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

    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

    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

    Deep Architecture for Citywide Travel Time Estimation Incorporating Contextual Information

    Tang, Kun / Chen, Shuyan / Khattak, Aemal J. et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Characterizing parking systems from sensor data through a data-driven approach

    Arjona Martinez, Jamie / Linares, Maria Paz / Casanovas, Josep | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    A deep learning traffic flow prediction framework based on multi-channel graph convolution

    Zhao, Yuanmeng / Cao, Jie / Zhang, Hong et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep 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

    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

    Collision-avoidance under COLREGS for unmanned surface vehicles via deep reinforcement learning

    Ma, Yong / Zhao, Yujiao / Wang, Yulong et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: deep reinforcement learning

    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

    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

    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)