Acta Optica Sinica, Volume. 43, Issue 12, 1206001(2023)

Application of Support Vector Machine in Quantitative Analysis of Mixed Gas

Jifang Shan1,2, Kun Liu1,2、*, Junfeng Jiang1,2, Tiegen Liu1,2, and Hui Yin1,2
Author Affiliations
  • 1School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
  • 2Key Laboratory of Opto-Electronics Information Technology, Ministry of Education, Tianjin University, Tianjin 300072, China
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    Figures & Tables(11)
    Schematic of sensing acquisition system based on thulium-doped fiber ring cavity
    Physical map of sensing acquisition system based on thulium-doped fiber ring cavity
    Output spectra of thulium-doped fiber ring cavity system
    Sample diagrams of absorption spectrum data. (a)-(c) Absorption spectrum data after pretreatment of pure NH3 gas with volume fraction of 2%, absorption spectrum data after Lorentz fitting, and absorption spectrum data obtained by simulation according to HITRAN database; (d)-(f) absorption spectrum data after pretreatment of pure CO2 gas with volume fraction of 5%, absorption spectrum data after Lorentz fitting, and absorption spectrum data obtained by simulation according to HITRAN database; (g)-(i) pre-processed absorption spectrum data of mixed gas of NH3 with volume fraction of 2% and CO2 with volume fraction of 2%, absorption spectrum data after Lorentz fitting, and absorption spectrum data obtained by HITRAN database simulation
    Partial principal component distributions after PCA dimensionality reduction on training set. (a) NH3-SVM model; (b) CO2-SVM model
    Flowchart of adaptive mutation particle swarm optimization algorithm
    Error curves of adaptive particle swarm optimization for model train set. (a) NH3-SVM model; (b) CO2-SVM model
    Comparison of results of train set and test set. (a) Comparison of set volume fraction and predicted volume fraction for NH3-SVM model train set; (b) comparison of set volume fraction and predicted volume fraction for CO2-SVM model train set; (c) comparison of set volume fraction and predicted volume fraction for NH3-SVM model test set; (d) comparison of set volume fraction and predicted volume fraction for CO2-SVM model test set
    • Table 1. Data of train set

      View table

      Table 1. Data of train set

      GroupNH3 volume fraction /%CO2 volume fraction /%GroupNH3 volume fraction /%CO2 volume fraction /%
      10.501104.0
      21.001205.0
      31.20131.02.0
      41.40142.02.0
      61.60162.03.0
      71.80171.04.0
      82.00182.04.0
      902.0191.05.0
      1003.0202.05.0
    • Table 2. Comparison of results of three optimization algorithms

      View table

      Table 2. Comparison of results of three optimization algorithms

      Model categoryNH3-SVM modelCO2-SVM model
      Parameter C,gMSEOptimization time /sParameter C,gMSEOptimization time /s
      AMPSO(2.394,2.399)0.02329.367(21.779,0.517)0.01013.181
      Standard PSO(75.899,0.010)0.0312.181(0.100,517.726)0.0521.199
      Grid search(0.732,4.000)0.02439.661(4.000,0.758)0.01218.762
    • Table 3. Test set results based on AMPSO

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      Table 3. Test set results based on AMPSO

      GroupNH3 volume fractionCO2 volume fraction

      Set

      value

      Predicted

      value

      Absolute

      error value

      Relative

      error value

      Set

      value

      Predicted value

      Absolute

      error value

      Relative

      error value

      10.50.5090.0091.8000
      21.01.0100.0101.0000
      31.21.2100.0100.8330
      41.41.400000
      51.51.5100.0100.6670
      61.61.5890.0110.6880
      71.81.7900.0100.5560
      82.01.9900.0100.5000
      902.01.9800.0201.000
      1003.02.9990.0010.033
      1104.04.0010.0010.025
      1205.04.9900.0100.200
      131.01.0100.0101.0002.02.0070.0070.350
      142.01.9990.0010.0502.01.9960.0040.200
      151.01.0080.0080.8003.02.9960.0040.133
      162.01.9910.0090.4503.02.9950.0050.167
      171.01.0080.0080.8004.03.9980.0020.050
      182.01.9910.0090.4504.03.9950.0050.125
      191.01.0090.0090.9005.04.9950.0050.100
      202.01.9900.0100.5005.04.9990.0010.020
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    Jifang Shan, Kun Liu, Junfeng Jiang, Tiegen Liu, Hui Yin. Application of Support Vector Machine in Quantitative Analysis of Mixed Gas[J]. Acta Optica Sinica, 2023, 43(12): 1206001

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Sep. 6, 2022

    Accepted: Oct. 27, 2022

    Published Online: Jun. 20, 2023

    The Author Email: Liu Kun (beiyangkl@tju.edu.cn)

    DOI:10.3788/AOS221681

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