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An Evolving Wavelet-Based De-noising Method for the Weigh-In-Motion System

  • Xie Chao
  • Huang Jie
  • Wei Chengjian
  • Xu Jun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

In order to enhance the roads maintenance, a number of weigh-in-motion (WIM) systems are used in the high way to collect the weight of every vehicle pass by, but normally the outcome is rough. The one important reason of this problem is that lots of complex low-frequency noises exist in the weighing signal, which can not be filtered by the Fourier series analysis. In this paper, firstly we apply the de-noising method via thresholding of wavelet coefficients to purify the signal, and then we propose an evolving wavelet thresholding method for the WIM system using evolutionary programming (EP), further we evolves the best threshold by this method and improve the weighing signal processing. Finally, we use our thoughts to model a simulated WIM signal de-nosing system and experimental results shows that this novel system provides more accurate data compared to other existing analyzing measures for weighing signal.

Keywords

Wavelet Coefficient Unit Commitment Good Threshold Unit Commitment Problem Wavelet Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Jacob, B., Newton, B.: COST 232 project final report. In: Proceedings of the Second European Conference on Weigh-in-Motion of Road Vehicles (1999) ISBN 92-828-6786-2.Google Scholar
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    States’ Successful Practices Weigh-in-Motion Handbook. Federal Highway Administration, U.S. Department of Transportation (1997)Google Scholar
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    Rajan, C.C.A., Mohan, M.R.: An evolutionary programming method for solving the unit commitment problem. In: IEEE Power Engineering Society General Meeting, p. 11149 (2004)Google Scholar
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    Donoho, D.L., Vetterli, M., DeVore, R.A., Daubechies, I.: Data compression and harmonic analysis. IEEE Trans. Inform. Theory 44(6), 2435–2476 (1998)MATHCrossRefMathSciNetGoogle Scholar
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    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inform. Theory 41, 613–627 (1995)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xie Chao
    • 1
    • 2
  • Huang Jie
    • 2
  • Wei Chengjian
    • 1
  • Xu Jun
    • 1
    • 2
  1. 1.College of Information Science and EngineeringNanjing University of TechnologyNanjingP.R. China
  2. 2.Research Center of Information Security, Dept. of Radio EngineeringSoutheast UniversityNanjingP.R. China

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