Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Hierarchical Clustering Method Based on SVM for Real-time Gas Mixture Classification

  • Kim, Guk-Hee (School of Electronics Engineering, College of IT Engineering Kyungpook National University) ;
  • Kim, Young-Wung (School of Electronics Engineering, College of IT Engineering Kyungpook National University) ;
  • Lee, Sang-Jin (School of Electronics Engineering, College of IT Engineering Kyungpook National University) ;
  • Jeon, Gi-Joon (School of Electronics Engineering, College of IT Engineering Kyungpook National University)
  • Received : 2010.07.07
  • Accepted : 2010.10.04
  • Published : 2010.10.25
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Abstract

In this work we address the use of support vector machine (SVM) in the multi-class gas classification system. The objective is to classify single gases and their mixture with a semiconductor-type electronic nose. The SVM has some typical multi-class classification models; One vs. One (OVO) and One vs. All (OVA). However, studies on those models show weaknesses on calculation time, decision time and the reject region. We propose a hierarchical clustering method (HCM) based on the SVM for real-time gas mixture classification. Experimental results show that the proposed method has better performance than the typical multi-class systems based on the SVM, and that the proposed method can classify single gases and their mixture easily and fast in the embedded system compared with BP-MLP and Fuzzy ARTMAP.

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References

  1. J. H. Cho, Y. W. Kim, K. J. Na, and G. J. Jeon, "Wireless electronic nose system for real-time quantitative analysis of gas mixtures using micro-gas sensor array and neuro-fuzzy network," Sens. Actuators B, vol. 134, pp. 104-111, 2008. https://doi.org/10.1016/j.snb.2008.04.019
  2. Y. W. Kim, S. J. Lee, G. H. Kim, and G. J. Jeon, “Wireless electronic nose network for real-time gas monitoring system,” Proc. of the 2009 IEEE International Workshop on Robotic and Sensors Environments, pp. 169-172, Nov. 2009.
  3. E. Llobet, E. L. Hines, J. W. Gardner, P. N. Bartlett, and T. T. Mottram, “Fuzzy ARTMAP based electronic nose data analysis,” Sens. Actuators B, vol. 61, pp. 183-190, Dec. 1999. https://doi.org/10.1016/S0925-4005(99)00288-9
  4. A. Gulbag, and F. Temurtas, “An intelligent gas concentration estimation system using neural network implemented microcontroller,” Lect. Notes Comput. Sci., vol. 3339, pp. 1206-1212, Nov. 2004. https://doi.org/10.1007/978-3-540-30549-1_124
  5. A. Gulbag, and F. Temurtas, “A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference system,” Sens. Actuators B, vol. 115, pp. 252-262, May 2006. https://doi.org/10.1016/j.snb.2005.09.009
  6. X. D. Wang, H. R . Zhang, and C. J. Zhang, “Signals recognition nose based on Support vector machines,” Proc. of the 2005 IEEE the Fourth International Conference on Machine Learning and Cybernetics, pp. 3394-3398, Aug. 2005.
  7. S. Jiang, W. Gui, C. Yang, and Y. Xie, “Fault Diagnosis of Lead-Zinc Smelting Furnace based on Multi-Class Support Vector Machines,” Proc. of the 2007 IEEE International Conference on Control and Automation, pp. 1643-1648, June, 2007.
  8. J. W. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “Face recognition using feature optimization and support vector learning. Neural Networks for Signal Processing XI,” Proc. of the 2001 IEEE Signal Processing Society Workshop, pp. 373-382, Sep. 2001.
  9. V. N. Vapnik, The Nature of Statistical Learnig Theory, John Wiley & Sons Inc., NewYork, 1999.
  10. C. Distante, N. Ancona, and P. Siciliano, “Support vector machines for olfactory signals recognition”, Sens. Actuators B, vol. 88, pp. 30-39, 2003. https://doi.org/10.1016/S0925-4005(02)00306-4
  11. I. S. Oh, Pattern Recognition ,Kyobo Book Co., 2008.(in Korean)
  12. C. W. Hsu, C. J. Lin, “A comparison of methods for multi-class support machines,” IEEE Trans. Neural Net., vol. 13, pp. 415-425, 2002 https://doi.org/10.1109/72.991427
  13. J. Berezmes, P. Cabre, S. Rojo, E. Llobet, and X. Correig, “Discrimination between diffefent samples of olive oil using variable selection techniques and modified fuzzy ARTMAP neural networks,” IEEE Sens. J., vol. 5, pp. 463-470, 2005. https://doi.org/10.1109/JSEN.2005.846186

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