Abstract
Gas chemical sensors are strongly affected by drift, i.e., changes in sensors’ response with time, that may turn statistical models commonly used for classification completely useless after a period of time. This paper presents a new classifier that embeds an adaptive stage able to reduce drift effects. The proposed system exploits a state-of-the-art evolutionary strategy to iteratively tweak the coefficients of a linear transformation able to transparently transform raw measures in order to mitigate the negative effects of the drift. The system operates continuously. The optimal correction strategy is learnt without a-priori models or other hypothesis on the behavior of physical-chemical sensors. Experimental results demonstrate the efficacy of the approach on a real problem.
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References
Pearce, T.C., Shiffman, S.S., Nagle, H.T., Gardner, J.W.: Handbook of machine olfaction. Wiley-VHC Ed., Weinheim (2003)
Pardo, M., Sberveglieri, G.: Electronic olfactory systems based on metal oxide semiconductor sensor arrays. MRS Bulletin 29(10), 703–708 (2004)
Gutierrez-Osuna, R.: Pattern Analysis for Machine Olfaction: A Review, June 2002, vol. 2, pp. 189–202 (2002)
Polster, A., Fabian, M., Villinger, H.: Effective resolution and drift of Paroscientific pressure sensors derived from long-term seafloor measurements. Geochem. Geophys. Geosyst. 10(Q08008) (2009)
Chen, D.Y., Chan, P.K.: An Intelligent ISFET Sensory System With Temperature and Drift Compensation for Long-Term Monitoring. IEEE Sensor Journal 8(11-12), 1948–1959 (2008)
Owens, W.B., Wong, A.P.S.: An improved calibration method for the drift of the conductivity sensor on autonomous CTD profiling floats by theta–S climatology. Deep-Sea Research Part I-Oceanographic Research Papers 56(3), 450–457 (2009)
Aliwell, S.R., et al.: Ozone sensors based on WO3: a model for sensor drift and a measurement correction method. Measurement Science & Technology 12(6), 684–690 (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)
Jain, A.K., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)
Dasarathy, B.V. (ed.): Nearest neighbor (NN) norms: Nn pattern classification
Pardo, M., Sberveglieri, G.: Classification of electronic nose data with support vector machines. Sensors and Actuators B: Chemical, 730–737, June 29 (2005)
Pardo, M., Sberveglieri, G.: Random forests and nearest shrunken centroids for the classification of sensor array data. Sensors And Actuators B-Chemical 131, 93–99 (2008)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford Univ. Press, Oxford (1995)
Sisk, B.C., Lewis, N.S.: Comparison of analytical methods and calibration methods for correction of detector response drift in arrays of carbon black-polymer composite vapor detector. Sensors and Actuators B: Chemical, 249–268, January 24 (2005)
Artursson, T., et al.: Drift correction for gas sensors using multivariate methods. Journal of Chemometrics 14, 711–723 (1999); Special Issue: Proceedings of the SSC6, HiT/TF, Norway (August 1999)
Di Natale, C., Martinelli, E., D’Amico, A.: Counteraction of environmental disturbances of electronic nose data by independent component analysis. Sensors and actuators. B, Chemical 82(2-3), 158–165 (2002)
Marco, S., Ortega, A., Pardo, A., Samitier, J.: Gas Identification with Tin Oxide Sensor Array and Self-Organizing Maps: Adaptive Correction of Sensor Drifts. IEEE Transactions on Instrumentation and Measurement 47, 316–321 (1998)
Vlachos, D.S., Fragoulis, D.K., Avaritsiotis, J.N.: An adaptive neural network topology for degradation compensation of thin film tin oxide gas sensors. Sensors and Actuators B: Chemical, 223–228, December 15 (1997)
Hansen, N.: The CMA evolution strategy: a comparing review, towards a new evolutionary computation. In: Lozano, J.A., Larranaga, P., Inza, I., Bengoetxea, E. (eds.) Advances on estimation of distribution algorithms, pp. 75–102. Springer, Heidelberg (2006)
Kuhn, K.: Building Predictive Models in R Using the caret Package. Journal of Statistical Software 28(5), 1–26 (2008)
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Di Carlo, S., Falasconi, M., Sánchez, E., Scionti, A., Squillero, G., Tonda, A. (2010). Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_43
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DOI: https://doi.org/10.1007/978-3-642-12239-2_43
Publisher Name: Springer, Berlin, Heidelberg
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