Post-processing Technique for Improving the Odor-identification Performance based on E-Nose System Byun, Hyung-Gi;
In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.
Sensor array;Adaptive radial basis function network;Sensors drift;Post-processing technique;Odor identification;
J.W Gardner, P.N. Bartlett, Electronic Noses Principles and applications, Oxford Univ. Press, Oxford, pp. 1- 5, 1999.
K. Persaud, and P. Travers, "Multielement arrays for sensing volatile chemicals", Intelligent Instruments and Computers, 147, pp. 147-154, 1991.
E. Hines, E. Llobet, and J.W Gardner, "Electronic noses: a review of signal processing techniques", IEE-Proc. -Circuit Devices Syst., Vol. 146, issue 6, pp. 297-310, 1999.
N. Kim, H. Byun, and K. Persaud, "Learning behaviors of stochastic gradient radial basis function network algorithms for odor sensing system", ETRI Journal, Vol. 28, No. 1, pp.59-66, 2006.
Y. Bang, H. Byun, and C. Lee, "Effective gas identification model based on fuzzy logic and hybrid genetic algorithms", , J. Sensor Sci. & Tech., Vol. 21, No. 5, pp. 329-338, 2012.
P. Pelosi, K. Persaud, "Toward an artificial nose", Sensors and Sensory Systems for Advanced Robots, NATO ASI Series, Vol. F43, pp. 49-70, 1988.