Safety Monitoring Information System of Railway Tunnel Construction Based on KNN


Article Preview

This paper presents a novel approach for railway tunnel deformation data analysis in Safety Monitoring Information System. The proposed work introduces a nonlinear machine learning method, Kernel Principal Component Analysis (KPCA), and K nearest neighbor classification (KNN) classifier for railway tunnel deformation data analysis. Kernel Principal Component Analysis (KPCA) is first applied to 1-dimension signals derived from a sequence of silhouette images to reduce its dimensionality. Then, we performed K nearest neighbor classification (KNN) for railway tunnel deformation data analysis. The experimental results show the KNN based railway tunnel deformation data analysis algorithm is better than that based on KPCA.



Edited by:

Yun-Hae Kim and Prasad Yarlagadda




N. Suo and H. L. Wang, "Safety Monitoring Information System of Railway Tunnel Construction Based on KNN", Applied Mechanics and Materials, Vols. 303-306, pp. 815-818, 2013

Online since:

February 2013




[1] Sunwen Duo. Twelve-lead ECG acquisition and analysis system [D]. Jilin, Jilin University, 2006:7-7.

[2] S. Sarkar, P. J. Phillilps, Z. Liu, I. Robledo, P. Grother, andK. W. Bowyer, The human ID railway tunnel deformation data challenge problem: Datasets, performance, and analysis, IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 2, pp.162-177, Feb. (2005).


[3] M. H. C. Law and A. K. Jain, Incremental nonlinear dimensionality reduction by manifold learning, IEEE Trans. PatternAnal. Machine Intell., vol. 28, no. 3, pp.377-391, Mar. (2006).


[4] F. Nasoz, K. Alvarez, C. L. Lisetti, N. Finkelstein: Emotion Recognition from Physiological Signals for Presence Technologies, International Journal of Cognition, Technology and Work, Special Issue on Presence, Vol 6(1), (2003).


[5] Xinliang Zhang, Yonghong Tan. The adaptive control using BP neural networks for a nonlinear servo-motor [J]. Journal of Control Theory and Applications, 2008, 6(3): 273-276.


[6] Ekinci M, Gedikli E. Background estimation based peopledetection and tracking for video surveillance. Lecture Notesin Computer Science, 2003, 2869: 421-429.


Fetching data from Crossref.
This may take some time to load.