Limits...
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.

Show MeSH
Cycle decomposition of cross level trend at unit section of K449+800–K449+825.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4236969&req=5

fig26: Cycle decomposition of cross level trend at unit section of K449+800–K449+825.

Mentions: It can be considered that the changes of cross level standard deviation and left longitudinal level standard deviation show a periodic growth pattern through the curve geometric features in Figures 24 and 25. Take the changes of cross level standard deviation state at K449+800–K449+825 unit section as the example; the changing trend of track irregularity state characters in 884 days is divided by the two jump models at 268th days and 835th days into three cycles. Among this, it is a complete changing cycle between the 268th days and the 835th days. The cycle is shown in Figure 26 periodically.


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)

Cycle decomposition of cross level trend at unit section of K449+800–K449+825.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig26: Cycle decomposition of cross level trend at unit section of K449+800–K449+825.
Mentions: It can be considered that the changes of cross level standard deviation and left longitudinal level standard deviation show a periodic growth pattern through the curve geometric features in Figures 24 and 25. Take the changes of cross level standard deviation state at K449+800–K449+825 unit section as the example; the changing trend of track irregularity state characters in 884 days is divided by the two jump models at 268th days and 835th days into three cycles. Among this, it is a complete changing cycle between the 268th days and the 835th days. The cycle is shown in Figure 26 periodically.

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