Page 27 - Read Online
P. 27

Ji et al. Intell Robot 2021;1(2):151-75  https://dx.doi.org/10.20517/ir.2021.14     Page 171

               Performed critical review, commentary and revision, and provided administrative, technical, and material
               support: Quek YT

               Availability of data and materials
               Not applicable.

               Financial support and sponsorship
               None.


               Conflicts of interest
               All authors declared that there are no conflicts of interest.


               Ethical approval and consent to participate
               Not applicable.


               Consent for publication
               Not applicable.


               Copyright
               © The Author(s) 2021.


               REFERENCES
               1.       Cannon DF, Edel K, Grassie SL, Sawley K. Rail defects: an overview. Fatigue Fract Eng M 2003;26:865-86.  DOI
               2.       Track circuit monitoring tool: standardization and deployment at CTA. Available from: http://www.trb.org/Main/Blurbs/177054.aspx
                    [Last accessed on 5 Jan 2022].
               3.       Rail Defects Handbook. Available from: https://extranet.artc.com.au/docs/eng/track-civil/guidelines/rail/RC2400.pdf [Last accessed
                    on 5 Jan 2022].
               4.       Dey A, Kurz J, Tenczynski L. Detection and evaluation of rail defects with non-destructive testing methods. Available from:
                    https://www.ndt.net/article/wcndt2016/papers/we1g4.pdf [Last accessed on 5 Jan 2022].
               5.       Min Y, Xiao B, Dang J, Yue B, Cheng T. Real time detection system for rail surface defects based on machine vision. J Image Video
                    Proc 2018.  DOI
               6.       Serin G, Sener B, Ozbayoglu AM, Unver HO. Review of tool condition monitoring in machining and opportunities for deep learning.
                    Int J Adv Manuf Technol 2020;109:953-74.  DOI
               7.       Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX. Deep learning and its applications to machine health monitoring. Mech Syst
                    Signal Process 2019;115:213-37.  DOI
               8.       Fu J, Chu J, Guo P, Chen Z. Condition monitoring of wind turbine gearbox bearing based on deep learning model. IEEE Access
                    2019;7:57078-87.  DOI
               9.       LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.  DOI  PubMed
               10.       Mcculloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol 1943;5:115-33.  PubMed
               11.       Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958;65:386-
                    408.  DOI  PubMed
               12.       Newell A. A step toward the understanding of information processes. Science 1969;165:780-2.  DOI
               13.       Rodan A, Faris H, Alqatawna J. Optimizing feedforward neural networks using biogeography based optimization for E-mail spam
                    identification. IJCNS 2016;9:19-28.  DOI
               14.       Robert HN. Theory of the backpropagation neural network. Proc 1989 IEEE IJCNN 1989;1:593-605.
               15.       Lecun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Comput 1989;1:541-51.
                    DOI
               16.       Hochreiter S. Untersuchungen zu dynamischen neuronalen Netzen. Diploma: Technische Universität München 1991.  DOI
               17.       Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9:1735-80.  DOI  PubMed
               18.       Quinlan JR. Induction of decision trees. Mach Learn 1986;1:81-106.  DOI
               19.       Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97.  DOI
               20.       Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci
                    1997;55:119-39.  DOI
               21.       Cristianini N, Scholkopf B. Support vector machines and kernel methods: the new generation of learning machines. Ai Magazine
                    2002;23:31.  DOI
               22.       Breiman L. Random forests. Mach Learn 2001;45:5-32.  DOI
   22   23   24   25   26   27   28   29   30   31   32