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      Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data

      1 , 1 , 1 , 2 , 3 , 2

      Journal of Advanced Transportation

      Hindawi Limited

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          Abstract

          This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.

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          Deep learning for time series classification: a review

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            Real-time human activity recognition from accelerometer data using Convolutional Neural Networks

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                Author and article information

                Contributors
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                Journal
                Journal of Advanced Transportation
                Journal of Advanced Transportation
                Hindawi Limited
                2042-3195
                0197-6729
                November 24 2020
                November 24 2020
                : 2020
                : 1-10
                Affiliations
                [1 ]Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, Brno 602 00, Czech Republic
                [2 ]Institute of Railway Structures and Constructions, Faculty of Civil Engineering, Brno University of Technology, Brno 602 00, Czech Republic
                [3 ]Výzkumný Ústav Železniční, a.s. (VUZ), Prague 142 00, Czech Republic
                Article
                10.1155/2020/8841810
                af5e1ed7-70e0-415b-9ad4-b193a500e785
                © 2020

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