Safety Monitoring Information System of Railway Tunnel Construction Based on KNN

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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.

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Periodical:

Edited by:

Yun-Hae Kim and Prasad Yarlagadda

Pages:

815-818

DOI:

10.4028/www.scientific.net/AMM.303-306.815

Citation:

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

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$35.00

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