The state-of-art image segmentation algorithms can be applied to accurately localize objects by using deep convolutional neural networks (CNN). In this paper, we consider the anomaly detection problem encountered in a train wheel system. We propose a progressive approach to use a multi-target network to segment each component of the considered system sequentially by decoupling the segmentation and the classification task. Moreover, we use the knowledge graph approach to establish a semantic consistency matrix by quantifying the spatial relationship between various components. We show that by establishing a knowledge graph of the normally operating systems, we are able to identify a faulty component effectively.


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    Titel :

    Knowledge-Graph Based Multi-Target Deep-Learning Models for Train Anomaly Detection


    Beteiligte:
    Qin, Zhiliang (Autor:in) / Cen, Chen (Autor:in) / Jie, Wang (Autor:in) / Gee, Teo Sin (Autor:in) / Chandrasekhar, Vijay Ramaseshan (Autor:in) / Peng, Zhongbo (Autor:in) / Zeng, Zeng (Autor:in)


    Erscheinungsdatum :

    01.12.2018


    Format / Umfang :

    1771225 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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