Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section. (1st December 2014)

• Record Type: Journal Article
• Authors: Jia, Chaolong; Wei, Lili; Wang, Hanning; Yang, Jiulin
• Other Names: Jiang Xiaobei Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2014(2014)
• Publication Date: 2014-12-01
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2014/727948
• Languages: English
• ISSNs: 1687-5265

Abstract

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.