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Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236969/ https://www.ncbi.nlm.nih.gov/pubmed/25435869 http://dx.doi.org/10.1155/2014/727948 |
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author | Jia, Chaolong Wei, Lili Wang, Hanning Yang, Jiulin |
author_facet | Jia, Chaolong Wei, Lili Wang, Hanning Yang, Jiulin |
author_sort | Jia, Chaolong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4236969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-42369692014-11-30 Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section Jia, Chaolong Wei, Lili Wang, Hanning Yang, Jiulin Comput Intell Neurosci Research Article 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. Hindawi Publishing Corporation 2014 2014-11-04 /pmc/articles/PMC4236969/ /pubmed/25435869 http://dx.doi.org/10.1155/2014/727948 Text en Copyright © 2014 Chaolong Jia et al. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Research Article Jia, Chaolong Wei, Lili Wang, Hanning Yang, Jiulin Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section |
title | Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section |
title_full | Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section |
title_fullStr | Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section |
title_full_unstemmed | Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section |
title_short | Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section |
title_sort | study of track irregularity time series calibration and variation pattern at unit section |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4236969/ https://www.ncbi.nlm.nih.gov/pubmed/25435869 http://dx.doi.org/10.1155/2014/727948 |
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