• Acta Optica Sinica
  • Vol. 41, Issue 6, 0611004 (2021)
Chao Kang1、2, Wenxiang Li1、2、**, Sheng Huang3, Hengrui Guan1、2, Jinbiao Zhao3, and Qingsheng Zhu1、2、3、*
Author Affiliations
  • 1CAS Nanjing Astronomical Instruments Research Center, Nanjing, Jiangsu 210042, China
  • 2University of Science and Technology of China, Hefei, Anhui 230026, China
  • 3CAS Nanjing Astronomical Instruments Co., LTD., Nanjing, Jiangsu 210042, China
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    DOI: 10.3788/AOS202141.0611004 Cite this Article Set citation alerts
    Chao Kang, Wenxiang Li, Sheng Huang, Hengrui Guan, Jinbiao Zhao, Qingsheng Zhu. Research on Active Optical Correction Algorithm Based on Deep Learning[J]. Acta Optica Sinica, 2021, 41(6): 0611004 Copy Citation Text show less
    Framework of DLCM algorithm
    Fig. 1. Framework of DLCM algorithm
    Framework and parameters of kinetic model
    Fig. 2. Framework and parameters of kinetic model
    Loss decline curve of strategy network
    Fig. 3. Loss decline curve of strategy network
    Framework and parameters of strategy network
    Fig. 4. Framework and parameters of strategy network
    Convergence process of evolutionary strategy algorithm
    Fig. 5. Convergence process of evolutionary strategy algorithm
    Actuator distribution map. (a) Support method of standard spherical mirror; (b) ANSYS simulation model
    Fig. 6. Actuator distribution map. (a) Support method of standard spherical mirror; (b) ANSYS simulation model
    Running time of DLCM algorithm
    Fig. 7. Running time of DLCM algorithm
    Comparison of effect of first correction. (a) Before correction; (b) corrected results
    Fig. 8. Comparison of effect of first correction. (a) Before correction; (b) corrected results
    Number of layers3456
    Accuracy /%91.3595.1497.5497.60
    Table 1. Relationship between model network layers and accuracy
    Dropout density0.30.40.5
    Accuracy /%98.4198.3798.18
    Table 2. Relationship between model network dropout density and accuracy
    FC layerConvolutional layer
    12345
    2/%84.3784.9290.8196.1497.92
    3/%86.4188.5993.0198.4298.51
    4/%85.2088.3892.9798.3498.27
    Table 3. Relationship between correction rate and convolutional layers and FC layers
    NameParameter
    Diameter /mm1000
    Thickness /mm80
    Radius of curvature /mm4000
    MaterialK9 glass
    Mass /kg174.445
    Table 4. Main parameters of spherical mirror
    NameParameter
    CPUIntel Core i7-4790 CPU @3.6 GHz
    Memory16 GB
    Graphics cardNVIDIA GeForce GTX980 Ti
    SystemWindows 7 professional
    EnvironmentPython3.7, PyTorch 1.5.1-GPU
    Table 5. Hardware and software parameters of algorithm training platform
    MethodInitialstateResultNumberof timesSinglepromotionratio /%
    DLS0.27λ0.02λ246.26
    LS0.27λ0.04λ242.60
    DLCM0.27λ0.01λ196.30
    DLS0.56λ0.03λ327.98
    LS0.56λ0.02λ424.11
    DLCM0.56λ0.02λ196.42
    DLS0.86λ0.02λ519.54
    LS0.86λ0.05λ615.70
    DLCM0.86λ0.03λ196.51
    DLS1.21λ0.05λ519.17
    LS1.21λ0.04λ713.81
    DLCM1.21λ0.02λ249.17
    Table 6. Comparison of calibration results of three algorithms
    Chao Kang, Wenxiang Li, Sheng Huang, Hengrui Guan, Jinbiao Zhao, Qingsheng Zhu. Research on Active Optical Correction Algorithm Based on Deep Learning[J]. Acta Optica Sinica, 2021, 41(6): 0611004
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