Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Bouillaut, L.
Weber, P.
Salem, A.B.
and
Aknin, P.
2004.
Use of causal probabilistic networks for the improvement of the maintenance of railway infrastructure.
Vol. 7,
Issue. ,
p.
6243.
Come, E.
Bouillaut, L.
Aknin, P.
and
Oukhellou, L.
2007.
HIDDEN MARKOV RANDOM FIELD, AN APPLICATION TO RAILWAY INFRASTRUCTURE DIAGNOSIS.
IFAC Proceedings Volumes,
Vol. 40,
Issue. 6,
p.
13.
Oukhellou, L.
Côme, E.
Bouillaut, L.
and
Aknin, P.
2008.
Combined use of sensor data and structural knowledge processed by Bayesian network: Application to a railway diagnosis aid scheme.
Transportation Research Part C: Emerging Technologies,
Vol. 16,
Issue. 6,
p.
755.
Real, J
Salvador, P
Montalbán, L
and
Bueno, M
2011.
Determination of Rail Vertical Profile through Inertial Methods.
Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit,
Vol. 225,
Issue. 1,
p.
14.
Li, Qingyong
and
Ren, Shengwei
2012.
A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads.
IEEE Transactions on Instrumentation and Measurement,
Vol. 61,
Issue. 8,
p.
2189.
Rizzo, P.
2014.
Sensor Technologies for Civil Infrastructures.
p.
497.
Rocha, Tiago J.
Ramos, Helena G.
Lopes Ribeiro, A.
Pasadas, Dário J.
and
Angani, Chandra S.
2015.
Studies to optimize the probe response for velocity induced eddy current testing in aluminium.
Measurement,
Vol. 67,
Issue. ,
p.
108.
Hu, Zhixin
Zhu, Hongtao
Hu, Ming
and
Ma, Yong
2018.
Rail surface spalling detection based on visual saliency.
IEEJ Transactions on Electrical and Electronic Engineering,
Vol. 13,
Issue. 3,
p.
505.
James, Ashish
Jie, Wang
Xulei, Yang
Chenghao, Ye
Ngan, Nguyen Bao
Yuxin, Lou
Yi, Su
Chandrasekhar, Vijay
and
Zeng, Zeng
2018.
TrackNet - A Deep Learning Based Fault Detection for Railway Track Inspection.
p.
1.
Li, Yong
Liu, Ze
Liu, Xianglong
Zhao, Pengfei
and
Liu, Tianbaige
2019.
High-Speed Electromagnetic Train Wheel Inspection Using a Kalman-Model-Based Demodulation Algorithm.
IEEE Sensors Journal,
Vol. 19,
Issue. 16,
p.
6833.
Yu, Haomin
Li, Qingyong
Tan, Yunqiang
Gan, Jinrui
Wang, Jianzhu
Geng, Yangli-ao
and
Jia, Lei
2019.
A Coarse-to-Fine Model for Rail Surface Defect Detection.
IEEE Transactions on Instrumentation and Measurement,
Vol. 68,
Issue. 3,
p.
656.
Pahwa, Ramanpreet Singh
Chandrasekhar, Vijay Ramaseshan
Chao, Jin
Paul, Jestine
Li, Yiqun
Lay Nwe, Ma Tin
Xie, Shudong
James, Ashish
Ambikapathi, Arulmurugan
and
Zeng, Zeng
2019.
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision.
p.
559.
Xu, Peng
Zhu, ChenLu
Zeng, HongMing
and
Wang, Ping
2020.
Rail crack detection and evaluation at high speed based on differential ECT system.
Measurement,
Vol. 166,
Issue. ,
p.
108152.
Tang, Renjie
and
Mao, Keming
2020.
An Improved GANs Model for Steel Plate Defect Detection.
IOP Conference Series: Materials Science and Engineering,
Vol. 790,
Issue. 1,
p.
012110.
Hu, Qian
Tang, Bo
Jiang, Lin
Zhu, Faxun
and
Zhao, Xiaoke
2022.
Rail Surface Defects Detection Based on Yolo v5 Integrated with Transformer.
p.
1131.
Shiao, Yaojung
and
Huynh, Tan-Linh
2022.
Suspension Control and Characterization of a Variable Damping Magneto-Rheological Mount for a Micro Autonomous Railway Inspection Car.
Applied Sciences,
Vol. 12,
Issue. 14,
p.
7336.
Lang, Ning
Wang, Decheng
Cheng, Peng
and
Liu, Lingxiao
2022.
Rail surface defect inspection via a self-reference template and similarity evaluation.
Measurement Science and Technology,
Vol. 33,
Issue. 1,
p.
015401.
Liu, Ping
Su, Sheng
Gao, Xiaobing
Zheng, Hongfei
Ma, Zilin
and
Jan, Naeem
2022.
Defect Detection for Mechanical Design Products with Faster R-CNN Network.
Mathematical Problems in Engineering,
Vol. 2022,
Issue. ,
p.
1.
Chen, Zhengxing
Wang, Qihang
He, Qing
Yu, Tianle
Zhang, Min
and
Wang, Ping
2022.
CUFuse: Camera and Ultrasound Data Fusion for Rail Defect Detection.
IEEE Transactions on Intelligent Transportation Systems,
Vol. 23,
Issue. 11,
p.
21971.
Ji, Albert
Thee, Quek Yang
Woo, Wai Lok
and
Wong, Eugene
2023.
Experimental Investigations of a Convolutional Neural Network Model for Detecting Railway Track Anomalies.
p.
1.