• Spectroscopy and Spectral Analysis
  • Vol. 41, Issue 1, 94 (2021)
Qi-feng HU* and Jian CAI
Author Affiliations
  • Brainware Terahertz Information Technology Co., Ltd., Hefei 230088, China
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    DOI: 10.3964/j.issn.1000-0593(2021)01-0094-06 Cite this Article
    Qi-feng HU, Jian CAI. Research of Terahertz Time-Domain Spectral Identification Based on Deep Learning[J]. Spectroscopy and Spectral Analysis, 2021, 41(1): 94 Copy Citation Text show less
    Absorption spectra of five acids
    Fig. 1. Absorption spectra of five acids
    (a) Absorption spectra of Vitamin B2 of different concentrations; (b) Absorption spectra of Vitamin B2 in different humidity
    Fig. 2. (a) Absorption spectra of Vitamin B2 of different concentrations; (b) Absorption spectra of Vitamin B2 in different humidity
    Result of S-G filtering
    Fig. 3. Result of S-G filtering
    RNN classification network structure
    Fig. 4. RNN classification network structure
    CNN classification network structure
    Fig. 5. CNN classification network structure
    Spectral images of 12 materials
    Fig. 6. Spectral images of 12 materials
    The training process of RNN network
    Fig. 7. The training process of RNN network
    The training process of CNN network
    Fig. 8. The training process of CNN network
    (a) Comparative experiment of concentrations of samples; (b) Comparative experiment of data dimensions
    Fig. 9. (a) Comparative experiment of concentrations of samples; (b) Comparative experiment of data dimensions
    算法训练准确率/%测试准确率/%测试耗时/ms时间复杂度测试环境
    RNN97.697.5<1O(1)GPU: Nvidia GeForce RTX2080Ti
    CNN99.999.6<1O(1)GPU: Nvidia GeForce RTX2080Ti
    k-NN10087.6>100O(n)CPU
    Table 1. Performance comparison of three algorithms
    数据预处理RNN/%CNN/%
    78.783.4
    S-G滤波95.199.6
    Table 2. The effect of data preprocessing on performance of RNN and CNN
    Qi-feng HU, Jian CAI. Research of Terahertz Time-Domain Spectral Identification Based on Deep Learning[J]. Spectroscopy and Spectral Analysis, 2021, 41(1): 94
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