Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network

Xiaojin Zhao, Zhihuang Wen, Xiaofang Pan, Wenbin Ye, Amine Bermak

Research output: Contribution to journalArticle

4 Citations (Scopus)


In this paper, we present a novel one-dimensional deep convolutional neural network (1D-DCNN) with a multi-label-way-based algorithm for comprehensively and automatically extracting features and classifying mixture gases. Although a number of pattern recognition methods have been used to analyze the mixed gases, the performances of these methods highly depend on the hand-crafted feature engineering. By contrast, the proposed implementation, based on one-dimensional convolution, is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene, CO, and methane. To the best of our knowledge, the proposed 1D-DCNN algorithm is first applied in the mixture gases' recognition. In addition, the proposed 1D-DCNN with multi-label way not only significantly reduces the label dimension but also quantifies the probability of each component in mixed gases. Compared with the conventional pattern recognition algorithms including support vector machine, artificial neural network, k-nearest neighbor, and random forest, the proposed 1D-DCNN exhibits a higher recognition accuracy (96.30%) based on our extensive experimental results using ten-fold cross validation.

Original languageEnglish
Article number8611207
Pages (from-to)12630-12637
Number of pages8
JournalIEEE Access
Publication statusPublished - 1 Jan 2019



  • deep convolutional neural network
  • Mixture gases recognition
  • multi-label classification

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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