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Sensors 2018, 18(1), 157; https://doi.org/10.3390/s18010157

Gas Classification Using Deep Convolutional Neural Networks

1
School of Electronic Science and Technology, Shenzhen University, Shenzhen 518060, China
2
School of Information Engineering, Shenzhen University, Shenzhen 518060, China
3
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Received: 8 November 2017 / Revised: 21 December 2017 / Accepted: 30 December 2017 / Published: 8 January 2018
(This article belongs to the Special Issue Signal and Information Processing in Chemical Sensing)
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Abstract

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). View Full-Text
Keywords: gas classification; deep convolutional neural networks; electronic nose gas classification; deep convolutional neural networks; electronic nose
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Peng, P.; Zhao, X.; Pan, X.; Ye, W. Gas Classification Using Deep Convolutional Neural Networks. Sensors 2018, 18, 157.

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