• Journal of Infrared and Millimeter Waves
  • Vol. 39, Issue 3, 311 (2020)
Shuai SHEN, Jian-Jun HE*, and Qi-Wu LUO
Author Affiliations
  • School of automation, central south university ,Changsha40083, China
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    DOI: 10.11972/j.issn.1001-9014.2020.03.008 Cite this Article
    Shuai SHEN, Jian-Jun HE, Qi-Wu LUO. Inversion of oxygen residual concentration in vials based on near-infrared absorption spectroscopy[J]. Journal of Infrared and Millimeter Waves, 2020, 39(3): 311 Copy Citation Text show less
    The process of gas concentration measurement
    Fig. 1. The process of gas concentration measurement
    The second harmonic and the feature points after Principal Component Analysis
    Fig. 2. The second harmonic and the feature points after Principal Component Analysis
    The contribution rate of Main feature
    Fig. 3. The contribution rate of Main feature
    The flow chart of BP neural network Optimized by genetic algorithm
    Fig. 4. The flow chart of BP neural network Optimized by genetic algorithm
    Neural network model structure
    Fig. 5. Neural network model structure
    (a)The hardware platform structure of residual oxygen detection system,(b)Overall equipment
    Fig. 6. (a)The hardware platform structure of residual oxygen detection system,(b)Overall equipment
    Prediction by least squares normal fitting
    Fig. 7. Prediction by least squares normal fitting
    Prediction by PCA-BP neural network
    Fig. 8. Prediction by PCA-BP neural network
    Prediction by PCA-GA-BP neural network
    Fig. 9. Prediction by PCA-GA-BP neural network
    Results for different concentrations of oxygen
    Fig. 10. Results for different concentrations of oxygen
    Running time picture
    Fig. 11. Running time picture
    拟合方法平均相对误差E决定系数R2
    最小二乘0.08320.910
    PCA-BP0.03800.962
    PCA-GA-BP0.01120.989
    Table 1. 三种模型预测精度对比
    Shuai SHEN, Jian-Jun HE, Qi-Wu LUO. Inversion of oxygen residual concentration in vials based on near-infrared absorption spectroscopy[J]. Journal of Infrared and Millimeter Waves, 2020, 39(3): 311
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