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Study of track irregularity time series calibration and variation pattern at unit section.

Jia C, Wei L, Wang H, Yang J - Comput Intell Neurosci (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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.

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Details of the correction data.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig6: Details of the correction data.

Mentions: Local details of correction data are shown in Figure 6.


Study of track irregularity time series calibration and variation pattern at unit section.

Jia C, Wei L, Wang H, Yang J - Comput Intell Neurosci (2014)

Details of the correction data.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4236969&req=5

fig6: Details of the correction data.
Mentions: Local details of correction data are shown in Figure 6.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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.

Show MeSH