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Page 174                            Ji et al. Intell Robot 2021;1(2):151-75  https://dx.doi.org/10.20517/ir.2021.14

                    PHM_CONF 2018.  DOI
               83.       Sun Y, Liu Y, Yang C. Railway joint detection using deep convolutional neural networks. Proceedings of 2019 IEEE 15th
                    International Conference on Automation Science and Engineering (CASE); 2019 Aug 22-26; Vancouver, BC, Canada. IEEE; 2019. p.
                    235-40.  DOI
               84.       Yuan H, Chen H, Liu S, Lin J, Luo X. A deep convolutional neural network for detection of rail surface defect. Proceedings of 2019
                    IEEE Vehicle Power and Propulsion Conference (VPPC); 2019 Oct 14-17; Hanoi, Vietnam. IEEE; 2019. p. 1-4.  DOI
               85.       Wang Y, Zhu L, Yu Z, Guo B. An adaptive track segmentation algorithm for a railway intrusion detection system. Sensors (Basel)
                    2019;19:2594.  DOI  PubMed  PMC
               86.       Dong B, Li Q, Wang J, Huang W, Dai P, Wang S. An end-to-end abnormal fastener detection method based on data synthesis.
                    Proceedings of 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI); 2019 Nov 4-6; Portland, OR,
                    USA. IEEE; 2019. p. 149-56.  DOI
               87.       Ma S, Gao L, Liu X, Lin J. Deep learning for track quality evaluation of high-speed railway based on vehicle-body vibration
                    prediction. IEEE Access 2019;7:185099-107.  DOI
               88.       Jin X, Wang Y, Zhang H, et al. DM-RIS: deep multimodel rail inspection system with improved MRF-GMM and CNN. IEEE Trans
                    Instrum Meas 2020;69:1051-65.  DOI
               89.       Li Z, Yin Z, Tang T, Gao C. Fault diagnosis of railway point machines using the locally connected autoencoder. Appl Sci
                    2019;9:5139.  DOI
               90.       Guo B, Shi J, Zhu L, Yu Z. High-speed railway clearance intrusion detection with improved SSD network. Appl Sci 2019;9:2981.
                    DOI
               91.       Liu J, Huang Y, Zou Q, et al. Learning visual similarity for inspecting defective railway fasteners. IEEE Sensors J 2019;19:6844-57.
                    DOI
               92.       Cui H, Li J, Hu Q, Mao Q. Real-time inspection system for ballast railway fasteners based on point cloud deep learning. IEEE Access
                    2020;8:61604-14.  DOI
               93.       Haseeb M, Ristić-Durrant D, Gräser A. A deep learning based autonomous distance estimation and tracking of multiple objects for
                    i m  p r o v e m  e n t   i n   s a f e t y   a n d   s e c u r i t y   i n   r a i l w a y s .   A v a i l a b l e   f r o m  :   h t t p s : / / w w w . b m  v c 2 0 1 9 . o r g / w p -
                    content/ODRSS2019/ODRSS2019_P_5_Haseeb.pdf [Last accessed on 5 Jan 2022].
               94.       Chen SX, Ni YQ, Liu JC, Yao N. Deep learning-based data anomaly detection in rail track inspection. Proceedings of 12th
                    International Workshop on Structural Health Monitoring: Enabling Intelligent Life-Cycle Health Management for Industry Internet of
                    Things (IIOT); 2019; Stanford, USA. DEStech Publications Inc.; 2019. p. 3235-42.  DOI
               95.       Jang J, Shin M, Lim S, Park J, Kim J, Paik J. Intelligent image-based railway inspection system using deep learning-based object
                    detection and weber contrast-based image comparison. Sensors 2019;19:4738.  DOI  PubMed  PMC
               96.       Kuzmin EV, Gorbunov OE, Plotnikov PO, Tyukin VA, Bashkin VA. Application of neural networks for recognizing rail structural
                    elements in magnetic and eddy current defectograms. Aut Control Comp Sci 2019;53:628-37.  DOI
               97.       Pahwa RS, Chao J, Paul J, et al. Faultnet: faulty rail-valves detection using deep learning and computer vision. Proceedings of 2019
                    IEEE Intelligent Transportation Systems Conference (ITSC); 2019 Oct 27-30; Auckland, New Zealand. IEEE; 2019. p. 559-66.  DOI
               98.       Liu J, Wei Y, Bergés M, Bielak J, Garrett Jr JH, Noh H. Detecting anomalies in longitudinal elevation of track geometry using train
                    dynamic responses via a variational autoencoder. Proceedings of Sensors and Smart Structures Technologies for Civil, Mechanical,
                    and Aerospace Systems 2019; 2019 Mar 27; Denver, CO, USA. International Society for Optics and Photonics; 2019. p. 109701B.
                    DOI
               99.       Wang Q, Bu S, He Z. Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN.
                    IEEE Trans Ind Inf 2020;16:6509-17.  DOI
               100.      Li D, Wang Y, Yan W, Ren W. Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet
                    transform and multi-branch convolutional neural network. Struct Health Monit 2021;20:1563-82.  DOI
               101.      Guo Z, Wan Y, Ye H. An unsupervised fault-detection method for railway turnouts. IEEE Trans Instrum Meas 2020;69:8881-901.
                    DOI
               102.      Zhan Y, Dai X, Yang E, Wang KC. Convolutional neural network for detecting railway fastener defects using a developed 3D laser
                    system. Int J Rail Transp 2021;9:424-44.  DOI
               103.      Li Z, Zhang J, Wang M, Zhong Y, Peng F. Fiber distributed acoustic sensing using convolutional long short-term memory network: a
                    field test on high-speed railway intrusion detection. Opt Express 2020;28:2925-38.  DOI  PubMed
               104.      Wei X, Wei D, Suo D, Jia L, Li Y. Multi-target defect identification for railway track line based on image processing and improved
                    YOLOv3 model. IEEE Access 2020;8:61973-88.  DOI
               105.      Lu J, Liang B, Lei Q, et al. SCueU-Net: efficient damage detection method for railway rail. IEEE Access 2020;8:125109-20.  DOI
               106.      Zheng Y, Wu S, Liu D, Wei R, Li S, Tu Z. Sleeper defect detection based on improved YOLO V3 algorithm. Proceedings of 2020
                    15th IEEE Conference on Industrial Electronics and Applications (ICIEA); 2020 Nov 9-13; Kristiansand, Norway. IEEE; 2020. p.
                    955-60.  DOI
               107.      Zhang D, Song K, Wang Q, He Y, Wen X, Yan Y. Two deep learning networks for rail surface defect inspection of limited samples
                    with line-level label. IEEE Trans Ind Inf 2021;17:6731-41.  DOI
               108.      Chen S, Zhou L, Ni Y, Liu X. An acoustic-homologous transfer learning approach for acoustic emission-based rail condition
                    evaluation. Struct Health Monit 2021;20:2161-81.  DOI
               109.      Kuzmin EV, Gorbunov OE, Plotnikov PO, Tyukin VA, Bashkin VA. Application of convolutional neural networks for recognizing
                    long structural elements of rails in eddy-current defectograms. Model anal inf sist 2020;27:316-29.  DOI
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