1–19 von 19 Ergebnissen
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    Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit

    Freier Zugriff
    Zhang, Jinlei / Chen, Feng / Guo, Yinan et al. | IET | 2020
    Schlagwörter: convolutional neural nets , three-dimensional convolutional neural network , deep-learning technologies , graph convolutional network , multigraph convolutional network

    Short-term FFBS demand prediction with multi-source data in a hybrid deep learning framework

    Freier Zugriff
    Bao, Jie / Yu, Hao / Wu, Jiaming | IET | 2019
    Schlagwörter: convolutional neural nets , hybrid deep learning framework , artificial neural network , hybrid deep learning neural network , convolutional neural network , deep learning approach , recurrent neural nets

    Scale-aware limited deformable convolutional neural networks for traffic sign detection and classification

    Freier Zugriff
    Liu, Zhanwen / Shen, Chao / Fan, Xing et al. | IET | 2020
    Schlagwörter: scale-aware multitask region proposal network module , neural nets , deformable convolutional neural networks , region-based deep convolutional neural network framework

    Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units

    Freier Zugriff
    Xiao, Zhitao / Hu, Zhiqiang / Geng, Lei et al. | IET | 2019
    Schlagwörter: deep convolutional layers , fatigue driving recognition network , end-to-end trainable convolutional neural network , neural nets , deep cascaded multitask framework

    Deep learning-based real-time fine-grained pedestrian recognition using stream processing

    Freier Zugriff
    Zhang, Weishan / Wang, Zhichao / Liu, Xin et al. | IET | 2018
    Schlagwörter: deep learning-based real-time fine-grained pedestrian recognition , improved single-shot detector , improved convolutional neural network , neural nets

    Lateral distance detection model based on convolutional neural network

    Freier Zugriff
    Zhang, Xiang / Yang, Wei / Tang, Xiaolin et al. | IET | 2018
    Schlagwörter: deep learning model , neural nets , convolutional neural network , improved image quilting algorithm

    Automated visual inspection of target parts for train safety based on deep learning

    Freier Zugriff
    Zhou, Fuqiang / Song, Ya / Liu, Liu et al. | IET | 2018
    Schlagwörter: neural nets , composite neural network , deep learning , stacked auto-encoder convolutional neural network

    Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy

    Freier Zugriff
    Yan, Zhuo / Feng, Youji / Cheng, Cheng et al. | IET | 2017
    Schlagwörter: feedforward neural nets , deep convolutional neural network

    Deep learning-based vehicle detection with synthetic image data

    Freier Zugriff
    Wang, Ye / Deng, Weiwen / Liu, Zhenyi et al. | IET | 2019
    Schlagwörter: convolutional neural network-based object detectors , neural nets , deep learning-based vehicle detection

    Vision-based vehicle behaviour analysis: a structured learning approach via convolutional neural networks

    Freier Zugriff
    Mou, Luntian / Xie, Haitao / Mao, Shasha et al. | IET | 2020
    Schlagwörter: convolutional neural nets , structured convolutional neural networks model , overfitting-preventing deep neural network

    Innovative method for traffic data imputation based on convolutional neural network

    Freier Zugriff
    Zhuang, Yifan / Ke, Ruimin / Wang, Yinhai | IET | 2018
    Schlagwörter: feedforward neural nets , convolutional neural network , deep-learning method

    Driver identification using 1D convolutional neural networks with vehicular CAN signals

    Freier Zugriff
    Hu, Hongyu / Liu, Jiarui / Gao, Zhenhai et al. | IET | 2021
    Schlagwörter: convolutional neural nets , convolutional-pooling layers , one-dimensional convolutional neural network , 1D convolutional neural networks , vehicular controller area network bus signals , deep learning framework

    Hyper-parameters optimisation of deep CNN architecture for vehicle logo recognition

    Freier Zugriff
    Soon, Foo Chong / Khaw, Hui Ying / Chuah, Joon Huang et al. | IET | 2018
    Schlagwörter: deep convolutional neural network architecture , network convergence , feedforward neural nets , deep CNN architecture

    Short-term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation

    Freier Zugriff
    Fukuda, Shota / Uchida, Hideaki / Fujii, Hideki et al. | IET | 2020
    Schlagwörter: deep learning model , graph convolutional recurrent neural network , neural nets , recurrent neural nets

    Smart parking sensors, technologies and applications for open parking lots: a review

    Freier Zugriff
    Paidi, Vijay / Fleyeh, Hasan / Håkansson, Johan et al. | IET | 2018
    Schlagwörter: feedforward neural nets , convolutional neural network , deep learning

    Depth estimation for advancing intelligent transport systems based on self-improving pyramid stereo network

    Freier Zugriff
    Tian, Yanling / Du, Yubo / Zhang, Qieshi et al. | IET | 2020
    Schlagwörter: deep learning model , pyramid stereo network , neural nets , convolutional neural networks

    Real-time detection of distracted driving based on deep learning

    Freier Zugriff
    Tran, Duy / Manh Do, Ha / Sheng, Weihua et al. | IET | 2018
    Schlagwörter: deep convolutional neural networks , neural nets , deep learning , residual network

    Deep learning-based helmet wear analysis of a motorcycle rider for intelligent surveillance system

    Freier Zugriff
    Yogameena, B. / Menaka, K. / Saravana Perumaal, S. | IET | 2019
    Schlagwörter: convolutional neural nets , region-based convolutional neural network , deep learning-based helmet wear analysis

    Joint vehicle detection and distance prediction via monocular depth estimation

    Freier Zugriff
    Shen, Chao / Zhao, Xiangmo / Liu, Zhanwen et al. | IET | 2020
    Schlagwörter: convolutional neural nets , end-to-end deep convolutional neural network framework