Deep learning models for classification of gases detected by sensor arrays of artificial nose

  • Ismael Araujo Universidade Federal Rural de Pernambuco
  • Juan Gamboa Universidade Federal Rural de Pernambuco
  • Adenilton Silva UFPE

Resumo


To recognize patterns that are usually imperceptible by human beings has been one of the main advantages of using machine learning algorithms The use of Deep Learning techniques has been promising to the classification problems, especially the ones related to image classification. The classification of gases detected by an artificial nose is one other area where Deep Learning techniques can be used to seek classification improvements. Succeeding in a classification task can result in many advantages to quality control, as well as to preventing accidents. In this work, it is presented some Deep Learning models specifically created to the task of gas classification.

Palavras-chave: Applications of Artificial Intelligence, Machine Learning, Computational Intelligence, Artificial Neural Networks, Deep Learning

Referências

Chollet, F. et al. (2015). Keras. https://keras.io.

Fonollosa, J., Fernandez, L., Gutiérrez-Gálvez, A., Huerta, R., and Marco, S. (2016). Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization. Sensors and Actuators B: Chemical, 236:1044–1053.

Fonollosa, J., Rodrı́guez-Luján, I., Trincavelli, M., and Huerta, R. (2015). Data set from chemical sensor array exposed to turbulent gas mixtures. Data in brief, 3:216–220.

Fonollosa, J., Rodrı́guez-Luján, I., Trincavelli, M., Vergara, A., and Huerta, R. (2014). Chemical discrimination in turbulent gas mixtures with mox sensors validated by gas chromatography-mass spectrometry. Sensors, 14(10):19336–19353.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Vergara, A., Fonollosa, J., Mahiques, J., Trincavelli, M., Rulkov, N., and Huerta, R. (2013). On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines. Sensors and Actuators B: Chemical, 185:462–477.

Zanchettin, C. and Ludermir, T. B. (2007). Wavelet filter for noise reduction and signal compression in an artificial nose. Applied Soft Computing, 7(1):246–256.

Chollet, F. et al. (2015). Keras. https://keras.io.

Fonollosa, J., Fernandez, L., Gutiérrez-Gálvez, A., Huerta, R., and Marco, S. (2016). Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization. Sensors and Actuators B: Chemical, 236:1044–1053.

Fonollosa, J., Rodrı́guez-Luján, I., Trincavelli, M., and Huerta, R. (2015). Data set from chemical sensor array exposed to turbulent gas mixtures. Data in brief, 3:216–220.

Fonollosa, J., Rodrı́guez-Luján, I., Trincavelli, M., Vergara, A., and Huerta, R. (2014). Chemical discrimination in turbulent gas mixtures with mox sensors validated by gas chromatography-mass spectrometry. Sensors, 14(10):19336–19353.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Vergara, A., Fonollosa, J., Mahiques, J., Trincavelli, M., Rulkov, N., and Huerta, R. (2013). On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines. Sensors and Actuators B: Chemical, 185:462–477.

Zanchettin, C. and Ludermir, T. B. (2007). Wavelet filter for noise reduction and signal compression in an artificial nose. Applied Soft Computing, 7(1):246–256.
Publicado
15/10/2019
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ARAUJO, Ismael; GAMBOA, Juan; SILVA, Adenilton. Deep learning models for classification of gases detected by sensor arrays of artificial nose. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 844-855. DOI: https://doi.org/10.5753/eniac.2019.9339.