Abstract
Sensor drift and noise handling in electronic noses (E-noses) are two different challenging problems. Sensor noise is caused by many factors such as temperature, pressure, humidity and cross interference. Noise can occur at any time, producing irrelevant or meaningless data. On the other hand, drift is appeared as a long term signal variation caused by unknown dynamic physical and chemical complex processes. Because sensor drift is not purely deterministic, it is very hard, if not impossible, to distinguish it from noise and vice versa. With respect to this property of E-nose, we propose a new approach to handle noise and sensor drift simultaneously. Our approach is based on kernel Fuzzy C-Mean clustering and fuzzy SVM (K-FSVM). The proposed method is compared to other currently used approaches, SVM, F-FSVM and KNN. Experiments are conducted on publicly available datasets. As the experimental results demonstrate, the performance of our proposed K-FSVM is superior than all other baseline methods in handling sensor drift and noise.
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References
AL-Maskari, S., Saini, D.O.W.: Cyber infrastructure and data quality for environmental pollution control in Oman. In: Proceedings of the 2010 DAMD International Conference on Data Analysis, Data Quality and Metada, p. 71 (2010)
Nimsuk, N., Nakamoto, T.: Study on the odor classification in dynamical concentration robust against humidity and temperature changes. Sensors and Actuators B: Chemical 134(1), 252–257 (2008)
Pan, L., Yang, S.X.: A new electronic nose for downwind livestock farm odour measurement. In: Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control, ICNSC 2006, pp. 410–415 (2006)
Vergara, A., Vembu, S., Ayhan, T., Ryan, M.A., Homer, M.L., Huerta, R.: Chemical gas sensor drift compensation using classifier ensembles. Sensors and Actuators B: Chemical 166-167, 320–329 (2012)
Zuppa, M., Distante, C., Siciliano, P., Persaud, K.C.: Drift counteraction with multiple self-organising maps for an electronic nose. Sensors and Actuators B: Chemical 98(2-3), 305–317 (2004)
Sharma, R., Chan, P., Tang, Z., Yan, G., Hsing, I., Sin, J.: Investigation of stability and reliability of tin oxide thin-film for integrated micro-machined gas sensor devices. Sensors and Actuators B: Chemical 81(1), 9–16 (2001)
Goodner, K.L., Dreher, J., Rouseff, R.L.: The dangers of creating false classifications due to noise in electronic nose and similar multivariate analyses. Sensors and Actuators B: Chemical 80(3), 261–266 (2001)
Tian, F., Yang, S.X., Dong, K.: Circuit and noise analysis of odorant gas sensors in an e-nose. Sensors 5(1), 85–96 (2005)
Romain, A., Nicolas, J.: Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview. Sensors and Actuators B: Chemical 146(2), 502–506 (2010); Selected papers from the 13th International Symposium on Olfaction and Electronic Nose ISOEN 2009
Padilla, M., Perera, A., Montoliu, I., Chaudry, A., Persaud, K., Marco, S.: Drift compensation of gas sensor array data by orthogonal signal correction. Chemometrics and Intelligent Laboratory Systems 100(1), 28–35 (2010)
Zhang, L., Tian, F., Nie, H., Dang, L., Li, G., Ye, Q., Kadri, C.: Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine. Sensors and Actuators B: Chemical 174, 114–125 (2012)
Marco, S., Pardo, A., Ortega, A., Samitier, J.: Gas identification with tin oxide sensor array and self organizing maps: Adaptive correction of sensor drifts. In: Instrumentation and Measurement Technology Conference, IMTC 1997, Proceedings of the Sensing, Processing, Networking, vol. 2, pp. 904–907. IEEE (May 1997)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Inc., New York (1995)
Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine 32(1), 37–50 (2004) (in China)
Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1(2), 98–110 (1993)
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Al-Maskari, S., Li, X., Liu, Q. (2014). An Effective Approach to Handling Noise and Drift in Electronic Noses. In: Wang, H., Sharaf, M.A. (eds) Databases Theory and Applications. ADC 2014. Lecture Notes in Computer Science, vol 8506. Springer, Cham. https://doi.org/10.1007/978-3-319-08608-8_21
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DOI: https://doi.org/10.1007/978-3-319-08608-8_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08607-1
Online ISBN: 978-3-319-08608-8
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