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Mathematical Problems in Engineering
Volume 2014, Article ID 456818, 6 pages
http://dx.doi.org/10.1155/2014/456818
Research Article

A New Classification Method of Infrasound Events Using Hilbert-Huang Transform and Support Vector Machine

1School of Information Engineering, University of Geosciences (Beijing), Beijing 100083, China
2Comprehensive Nuclear-Test-Ban Treaty Beijing National Data Center, Beijing 100085, China
3School of Water Resources and Environment, University of Geosciences (Beijing), Beijing 100083, China

Received 27 March 2014; Revised 31 May 2014; Accepted 23 June 2014; Published 6 July 2014

Academic Editor: Alkiviadis Paipetis

Copyright © 2014 Xueyong Liu et al. 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.

Linked References

  1. A. Cannata, P. Montalto, M. Aliotta et al., “Clustering and classification of infrasonic events at Mount Etna using pattern recognition techniques,” Geophysical Journal International, vol. 185, no. 1, pp. 253–264, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. A. J. Bedard Jr. and T. M. Georges, “Atmospheric infrasound,” Physics Today, vol. 53, no. 3, pp. 32–37, 2000. View at Google Scholar · View at Scopus
  3. J. D. Assink, L. G. Evers, I. Holleman, and H. Paulssen, “Characterization of infrasound from lightning,” Geophysical Research Letters, vol. 35, no. 15, Article ID L15802, 2008. View at Publisher · View at Google Scholar
  4. J. P. Mutschlecner and R. W. Whitaker, “Infrasound from earthquakes,” Journal of Geophysical Research: Atmospheres, vol. 110, pp. 1108–1118, 2005. View at Google Scholar
  5. R. R. Zhang, “Characterizing and quantifying earthquake-induced site nonlinearity,” Soil Dynamics and Earthquake Engineering, vol. 26, no. 8, pp. 799–812, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Le Pichon, P. Herry, P. Mialle et al., “Infrasound associated with 2004-2005 large Sumatra earthquakes and tsunami,” Geophysical Research Letters, vol. 32, no. 19, Article ID L19802, pp. 1–5, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Cárdenas-Peña, M. Orozco-Alzate, and G. Castellanos-Dominguez, “Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano,” Computers & Geosciences, vol. 51, pp. 293–304, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. J. B. Johnson and M. Ripepe, “Volcano infrasound: a review,” Journal of Volcanology and Geothermal Research, vol. 206, no. 3-4, pp. 61–69, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. T. S. Kim, C. Hayward, and B. Stump, “Local infrasound signals from the Tokachi-Oki earthquake,” Geophysical Research Letters, vol. 31, no. 20, Article ID L20605, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. N. D. Tsybul'skaya, S. N. Kulichkov, and A. I. Chulichkov, “Studying possibilities for the classification of infrasonic signals from different sources,” Izvestiya—Atmospheric and Ocean Physics, vol. 48, no. 4, pp. 384–390, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Wang, The Study of Feature Extraction for Fishes Acoustic Signal, Harbin Egineering University, Harbin, China, 2009.
  12. X. X. Li, A Research of Ships and Whales Acoustic Signal Feature Extraction and Classification Recognition, Harbin Engineering University, Harbin, China, 2012.
  13. F. M. Ham, K. Rekab, R. Acharyya, and Y. C. Lee, “Infrasound signal classification using parallel RBF Neural Networks,” International Journal of Signal and Imaging Systems Engineering, vol. 1, pp. 155–167, 2008. View at Google Scholar
  14. F. M. Ham, I. Iyengar, B. M. Hambebo et al., “A neurocomputing approach for monitoring plinian volcanic eruptions using infrasound,” Procedia Computer Science, vol. 13, pp. 7–17, 2012. View at Publisher · View at Google Scholar
  15. G. E. Deal and F. M. Ham, “Speaker recognition using parallel neural network modules,” Neural, Parallel & Scientific Computations, vol. 17, no. 3-4, pp. 215–238, 2009. View at Google Scholar · View at MathSciNet
  16. S. Park, F. M. Ham, and C. G. Lowrie, “Discrimination of infrasound events using parallel neural network classification banks,” Nonlinear Analysis: Theory, Methods & Applications, vol. 63, no. 5–7, pp. e859–e865, 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Chilo, T. Lindblad, R. Olsson, and S. E. Hansen, “Comparison of three featrue extraction techniques to distinguish between different infrasound signal,” in Progress in Pattern Recognition, chapter 8, pp. 75–82, Springer, 2007. View at Google Scholar
  18. J. Chilo, A. Jabor, L. Liszka et al., “Filtering and extracting features from infrasound data,” in Proceedings of the 14th IEEE-NPSS Real Time Conference, pp. 451–455, Stockholm, Sweden, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. N. E. Huang and Z. Wu, “A review on Hilbert-Huang transform: method and its applications to geophysical studies,” Reviews of Geophysics, vol. 46, no. 2, Article ID RG2006, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. N. E. Huang and S. P. Shen, Hilbert-Huang Transform and Its Applications, World Scientific, Singapore, 2005. View at MathSciNet
  22. C. Cortes and V. Vapnik, “Support—vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  23. N. Saravanan, V. N. S. Kumar Siddabattuni, and K. I. Ramachandran, “A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box,” Expert Systems with Applications, vol. 35, no. 3, pp. 1351–1366, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. R. H. Wang, “AdaBoost for feature selection, classification and its relation with SVM, a review,” Physics Procedia, vol. 25, pp. 800–807, 2012. View at Publisher · View at Google Scholar
  25. S. Li, W. Zhou, Q. Yuan, S. Geng, and D. Cai, “Feature extraction and recognition of ictal EEG using EMD and SVM,” Computers in Biology and Medicine, vol. 43, no. 7, pp. 807–816, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. Q. Liu, C. Chen, Y. Zhang, and Z. Hu, “Feature selection for support vector machines with RBF kernel,” Artificial Intelligence Review, vol. 36, no. 2, pp. 99–115, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. A. J. Smola, Learning with Kernels, Technical University of Berlin, Berlin, Germany, 1998.
  28. C. T. Su and C. H. Yang, “Feature selection for the SVM: an application to hypertension diagnosis,” Expert Systems with Applications, vol. 34, no. 1, pp. 754–763, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. F. Wang, K. He, Y. Liu, L. Li, and X. Hu, “Research on the selection of kernel function in SVM based facial expression recognition,” in Proceedings of the 8th IEEE Conference on Industrial Electronics and Applications (ICIEA '13), pp. 1404–1408, Melbourne, Australia, June 2013. View at Publisher · View at Google Scholar · View at Scopus