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A Kernel Based Multi-resolution Time Series Analysis for Screening Deficiencies in Paper Production

  • Marcus Ejnarsson
  • Carl Magnus Nilsson
  • Antanas Verikas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions.

Keywords

Discrete Wavelet Transform Detection Accuracy Paper Mill Paper Structure Novelty Detector 
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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    Timberlake, A., Strom, E.: Do You Know What Causes the Variability in the Paper You Produce? In: TAPPI Proceedings of 2004 Paper Summit, Spring Technical & International Environmental Conference (2004)Google Scholar
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    Daubechies, I.: The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Trans Information Theory 36(5), 961–967 (1990)MATHCrossRefMathSciNetGoogle Scholar
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    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcus Ejnarsson
    • 1
  • Carl Magnus Nilsson
    • 1
  • Antanas Verikas
    • 1
    • 2
  1. 1.Intelligent Systems LaboratoryHalmstad UniversityHalmstadSweden
  2. 2.Department of Applied ElectronicsKaunas University of TechnologyKaunasLithuania

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