DocumentCode
2179702
Title
Notice of Retraction
Application of Wavelet Threshold De-Noising in Settlement Monitoring for Subway
Author
Donghui Wu ; Linya Tian
Author_Institution
Coll. of Geosci. & Eng., Hohai Univ., Nanjing, China
fYear
2010
fDate
24-26 Aug. 2010
Firstpage
1
Lastpage
4
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Discussed the ideas of wavelet analysis for data reliability testing and its implementation methods, Researched the method of wavelet denoising threshold selection. We applied the method to Guangzhou subway settlement monitoring data reliability testing and analysis, and got a good effect of gross error detection and eliminate, Proved that wavelet threshold de-noising can effectively eliminate gross errors, retention of useful settlement information and help to improve subway settlement analysis and forecasting reliability.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Discussed the ideas of wavelet analysis for data reliability testing and its implementation methods, Researched the method of wavelet denoising threshold selection. We applied the method to Guangzhou subway settlement monitoring data reliability testing and analysis, and got a good effect of gross error detection and eliminate, Proved that wavelet threshold de-noising can effectively eliminate gross errors, retention of useful settlement information and help to improve subway settlement analysis and forecasting reliability.
Keywords
data handling; rail traffic; reliability; traffic engineering computing; wavelet transforms; Guangzhou subway settlement; data reliability testing; gross error detection; gross error elimination; subway settlement monitoring; wavelet threshold denoising; Discrete wavelet transforms; Monitoring; Noise; Noise reduction; Wavelet analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Management and Service Science (MASS), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-5325-2
Type
conf
DOI
10.1109/ICMSS.2010.5577438
Filename
5577438
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