A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification

This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series...

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Vydané v:Sensors (Basel, Switzerland) Ročník 19; číslo 9; s. 1960
Hlavní autori: Han, Lu, Yu, Chongchong, Xiao, Kaitai, Zhao, Xia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 26.04.2019
MDPI
Predmet:
Accuracy
analogous-image matrix data
Artificial intelligence
Chromatography
Classification
Computer vision
convolutional neural networks
Deep learning
Gases
Identification
Information processing
International conferences
Machine learning
Mass spectrometry
Metal oxides
mixed gas identification
MOX gas sensors
Neural networks
Pattern recognition
Scientific imaging
Sensors
Time series
time series classification
Beijing China
Chile
United States > US
China
analogous-image matrix data
convolutional neural networks
mixed gas identification
MOX gas sensors
time series classification
ISSN:1424-8220, 1424-8220
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Shrnutí:This paper proposes a new method of mixed gas identification based on a convolutional neural network for time series classification. In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array of eight MOX gas sensors. Existing convolutional neural networks are mostly used for processing visual data, and are rarely used in gas data classification and have great limitations. Therefore, the idea of mapping time series data into an analogous-image matrix data is proposed. Then, five kinds of convolutional neural networks—VGG-16, VGG-19, ResNet18, ResNet34 and ResNet50—were used to classify and compare five kinds of mixed gases. By adjusting the parameters of the convolutional neural networks, the final gas recognition rate is 96.67%. The experimental results show that the method can classify the gas data quickly and effectively, and effectively combine the gas time series data with classical convolutional neural networks, which provides a new idea for the identification of mixed gases.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19091960