1. bookVolume 73 (2022): Issue 2 (April 2022)
Journal Details
License
Format
Journal
eISSN
1339-309X
First Published
07 Jun 2011
Publication timeframe
6 times per year
Languages
English
access type Open Access

A novel principal component-based virtual sensor approach for efficient classification of gases/odors

Published Online: 14 May 2022
Volume & Issue: Volume 73 (2022) - Issue 2 (April 2022)
Page range: 108 - 115
Received: 01 Mar 2022
Journal Details
License
Format
Journal
eISSN
1339-309X
First Published
07 Jun 2011
Publication timeframe
6 times per year
Languages
English
Abstract

High-performance detection and estimation of gases/odors are challenging, especially in real-time gas sensing applications. Recently, efficient electronic noses (e-noses) are being developed using convolutional neural networks (CNNs). Further, CNNs perform better when they operate on a minimal size of vector response. In this paper, dimensions of the operational vectors have been augmented by using virtual sensor responses. These virtual responses are obtained from the principal components of the physical sensor responses. Accordingly, two sets of data are upscaled as a one-dimensional one. Another level of upscaling is further obtained by using the mirror mosaicking technique. Hence, with our proposed novel approach, the final vector size for CNN operations achieves a new dimension. With this upscaled hybrid dataset, consisting of physical and virtual sensor responses, a simpler CNN has achieved 100 percent correct classification in two different experimental settings. To the best of authors information, it is for the first time that an e-nose has been designed using a principal component-based hybrid, upscaled dataset and achieves 100 percent correct classification of the considered gases/odors.

Keywords

[1] X. Zhai, A. A. S. Ali, A. Amira, and F. Bensaali, “MLP Neural Network Based Gas Classification System on Zynq SoC”, IEEE Access, vol. 4, pp. 8138–8146, doi: 10.1109/ACCESS.2016.2619181, 2016.10.1109/ACCESS.2016.2619181 Search in Google Scholar

[2] A. Vanarse, A. Osseiran, and A. Rassau, “Real-Time Classification of Multivariate Olfaction Data Using Spiking Neural Networks”, Sensors, vol. 19, o. 8, pp. 1841, doi: 10.3390/s19081841, 2019.10.3390/s19081841651539231003417 Search in Google Scholar

[3] U. Yaqoob, and M. I. Younis, “Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning: A Review”, Sensors, vol. 21, no. 8, p. 2877, doi: 10.3390/s21082877, 2021.10.3390/s21082877807353733923937 Search in Google Scholar

[4] W. S. Al-Dayyeni et al, “A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets”, IEEE Access, vol. 9, pp. 88535–88551, doi: 10.1109/ACCESS.2021.3090165, 2021.10.1109/ACCESS.2021.3090165 Search in Google Scholar

[5] N. S. Rajput, R. R. Das, V. N. Mishra, K. P. Singh, and R. Dwivedi, “A neural net implementation of SPCA pre-processor for gas/odor classification using the responses of thick film gas sensor array”, Sensors and Actuators B: Chemical, vol. 148, pp. 550–558, May 2010.10.1016/j.snb.2010.05.051 Search in Google Scholar

[6] P. Peng, X. Zhao, X. Pan, and W. Ye, “Gas Classification Using Deep Convolutional Neural Networks”, Sensors, vol. 18, no. 1, p. 157, Jan 2018.10.3390/s18010157579548129316723 Search in Google Scholar

[7] S. Lekha, and M. Sucheta, “Real-Time Non-Invasive Detection and Classification of Diabetes Using Modified Convolution Neural Network”, IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1630–1636, doi: 10.1109/JBHI.2017.27575 10, Sep 2018. Search in Google Scholar

[8] S. N. Chaudhri, and N. S. Rajput, “Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network”, Proceedings of the 10th International Conference on Sensor Networks, SENSOR-NETS, vol. 1, pp. 86–91, 2021.10.5220/0010251500860091 Search in Google Scholar

[9] K. A. Ngo, P. Lauque, and K. Aguir, “Identification of Toxic Gases Using Steady-State and Transient Responses of Gas Sensor Array”, Sensors and Materials, vol. 18, no. 5, pp. 251–260, 2006. Search in Google Scholar

[10] X. Zhao, Z. Wen, X. Pan, W. Ye, and A. Bermak, “Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network”, IEEE Access, vol. 7, pp. 12630-12637, doi: 10.1109/ACCESS.2019.2892754 2019.10.1109/ACCESS.2019.2892754 Search in Google Scholar

[11] S. I. Choi, T. Eom, and G.-M. Jeong, “Gas classification using combined features based on a discriminant analysis for an electronic nose”, J. Sensors, Mar 2016.10.1155/2016/9634387 Search in Google Scholar

[12] H. Li, D. Luo, Y. Sun, and H. G. Hosseini, “Classification and Identification of Industrial Gases Based on Electronic Nose Technology”, Sensors, Basel 19, pp. 5033, 2019.10.3390/s19225033689133431752238 Search in Google Scholar

[13] L. Han, C. Yu, K. Xiao, and X. Zhao, “A new method of mixed gas identification based on a convolutional neural network for time series classification”, Sensors, vol. 19, no. 9, pp. 1960–1982, 2019. Search in Google Scholar

[14] G. Wei, G. Li, J. Zhao, and A. He, “Development of a LeNet-5 gas identification CNN structure for electronic noses”, Sensors, vol. 19, no. 1, p. 217, 2019.10.3390/s19010217 Search in Google Scholar

[15] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition”, Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, doi: 10.1109/5.726791, Nov 1998.10.1109/5.726791 Search in Google Scholar

[16] A. U. Rehman, S. B. Belhaouari, M. Ijaz, A. Bermak, and M. Hamdi, “Multi-Classifier Tree with Transient Features for Drift Compensation in Electronic Nose”, IEEE Sensors Journal, vol. 21, no. 5, pp. 6564–6574, doi: 10.1109/JSEN.2020.3041949,.10.1109/JSEN.2020.3041949 Search in Google Scholar

[17] E. Llobet, J. Brezmes, X. Vilanova, J. E. Sueiras, and X. Correig, “Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array”, Sens. Actuators B Chem, vol. 41, no. 1, pp. 13–21, Jun 1997.10.1016/S0925-4005(97)80272-9 Search in Google Scholar

[18] S. N. Chaudhri, and N. S. Rajput, “Multidimensional Multiconvolution Based Feature Extraction Approach for Drift Tolerant Robust Classifier for Gases/Odors”, IEEE Sensors Letters, doi: 10.1109/LSENS.3153832, (Accepted) 2022. Search in Google Scholar

[19] T. Wang, M. Sun, and K. Hu, “Dilated Deep Residual Network for Image Denoising”, IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI,), Boston, MA, pp. 1272-1279, doi: 10.1109/ICTAI.2017.00192, 2017.10.1109/ICTAI.2017.00192 Search in Google Scholar

[20] A. Wu, W. Zheng, H. Yu, S. Gong, and J. Lai, “RGB-Infrared Cross-Modality Person Re-identification”, IEEE International Conference on Computer Vision (ICCV), Venice, pp. 5390–5399, doi: 10.1109/ICCV.2017.575, 2017.10.1109/ICCV.2017.575 Search in Google Scholar

[21] K. O’Shea, and R. Nash, “An introduction to convolutional neural networks”, CoRR, abs/1511.08458, 2015. Search in Google Scholar

[22] A. Nguyen, S. Choi, W. Kim, S. Ahn, J. Kim, and S. Lee, “Distribution Padding in Convolutional Neural Networks”, IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, pp. 4275–4279, doi: 10.1109/ICIP.2019.8803537, 2019.10.1109/ICIP.2019.8803537 Search in Google Scholar

[23] A. Mishra, N. S. Rajput, and G. Han, “NDSRT: An Efficient Virtual Multi-Sensor Response Transformation for Classification of Gases/Odors”, IEEE Sensors Journal, vol. 17, no. 11, pp. 3416-3421, doi: 10.1109/JSEN. 2017.2690536, Jun 2017. Search in Google Scholar

[24] Y. G. Zhang, C. H. Zhang, Y. Zhao, and S. Gao, “Wind speed prediction of RBF neural network based on PCA and ICA”, J. Elect. Eng. Slovak, vol. 69, no. 2, pp. 148–155, Mar 2018.10.2478/jee-2018-0018 Search in Google Scholar

[25] S. N. Chaudhri, N. S. Rajput, K. P. Singh, and D. Singh, “Mirror Mosaicking Based Reduced Complexity Approach for the Classification of Hyperspectral Images”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3657–3660, doi: 10.1109/IGARSS47720.2021.9554276, 2021.10.1109/IGARSS47720.2021.9554276 Search in Google Scholar

[26] D. Dua, and C. Graff, “UCI Machine Learning Repository”, http://archive.ics.uci.edu/ml, Irvine, CA: University of California, School of Information and Computer Science, 2019. Search in Google Scholar

[27] J. Fonollosa, L. Fernández, A. Gutiaérrez-Gálvez, R. Huerta, and S. Marco, “Calibration transfer and drift counteraction in chemical sensor arrays using direct standardization”, Sensors Actuators B Chem, vol. 236, pp. 1044–1053, Nov 2016. Search in Google Scholar

[28] A. Ziyatdinov, J. Fonollosa, L. Fernndez, A. Gutierrez-Glvez, S. Marco, and A. Perera, “Bioinspired early detection through gas flow modulation in chemo-sensory systems”, Sens Actuators B Chem, vol. 206, pp. 5r38–547, 2015.10.1016/j.snb.2014.09.001 Search in Google Scholar

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