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Computational Intelligence and Neuroscience
Volume 2014 (2014), Article ID 727948, 14 pages
http://dx.doi.org/10.1155/2014/727948
Research Article

Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section

1School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2Chongqing Public Security Bureau, Chongqing 401147, China
3State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
4China National Tendering Center of Mach. & Elec. Equipment, Beijing 100142, China

Received 15 July 2014; Accepted 5 October 2014; Published 4 November 2014

Academic Editor: Xiaobei Jiang

Copyright © 2014 Chaolong Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described.