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Driven by machine learning to intelligent damage recognition of terminal optical components

  • S.I. : DPTA Conference 2019
  • Published:
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Abstract

In order to realize the terminal optical element online detection system in the Shenguang III system, each optical element in each terminal optical component in the target room is detected. The research on the optical damage of terminal optical components focuses on the search for damage points, the extraction of damage information, and the classification of damage types. In addition, damage classification and identification of terminal optical components are performed through machine learning, and infrared nondestructive testing is used as technical support to improve the identification model and reduce the complexity of the spectral model. After studying the preprocessing and dimensionality reduction methods of near-infrared spectroscopy, this paper compares the effects of different preprocessing methods and screening feature methods and combines different modeling methods to conduct experiments. The research results show that the method proposed in this paper has certain effects.

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References

  1. Pan S, Luo Y, Shalmany SH et al (2017) A resistor-based temperature Sensor With a 0.13 pJ $\cdot $ K2 Resolution FoM. IEEE J Solid-State Circuits 53(1):164–173

    Article  Google Scholar 

  2. Yang Y, Gao A, Lu R, et al. (2017) 5 GHz lithium niobate MEMS resonators with high FoM of 153[C]//2017 IEEE 30th International Conference on Micro Electro Mechanical Systems (MEMS). IEEE, p 942–945.

  3. Li SK, Huang TZ (2018) Global FOM and GMRES algorithms for a class of complex matrix equations. J Comput Appl Math 335:227–241

    Article  MathSciNet  Google Scholar 

  4. Mecca M, Todaro L, D'Auria M (2017) Extractives fom cedar deodara and alnus cordata in the presence of molybdenum catalysts. ChemistrySelect 2(8):2536–2538

    Article  Google Scholar 

  5. Jiao Z, Zhang Y, Sun M et al (2017) Analysis of hot images in final optics assembly[C]//pacific rim laser damage 2017: optical materials for high-power lasers. Int Soc Opt Photonics 10339:103392G

    Google Scholar 

  6. Liu XY, Zhang JX, Qiao B et al (2017) Research on supporting technology of lens applied in cold optics assembly. Guangxue Jingmi Gongcheng/Opt Precis Eng 25(7):1850–1856

    Google Scholar 

  7. Xusong Q, Changchun L, Haiping C et al (2017) 3D reconstructing technique and application of high-power laser facility's optics assembly building. J Comp Aided Des Comp Gr 29(5):854–859

    Google Scholar 

  8. Xiao X, Le Berre S, Fobar DG et al (2018) Measurement of chlorine concentration on steel surfaces via fiber-optic laser-induced breakdown spectroscopy in double-pulse configuration. Spectrochim Acta 141:44–52

    Article  Google Scholar 

  9. Ghaeli I, Hosseinidoust Z, Zolfagharnasab H et al (2018) A new label-free technique for analysing evaporation induced self-assembly of viral nanoparticles based on enhanced dark-field optical imaging. Nanomaterials 8(1):1

    Article  Google Scholar 

  10. Burwitz V, Willingale R, Pellilciari C et al (2017) Testing and calibrating the ATHENA optics at PANTER[C]//optics for EUV, X-ray, and gamma-ray astronomy VIII. Int Soc Opt Photonics 10399:103990O

    Google Scholar 

  11. Gao J, Lan C, Zhao Q et al (2018) Experimental realization of Mie-resonance terahertz absorber by self-assembly method. Opt Express 26(10):13001–13011

    Article  Google Scholar 

  12. Chen P, Shu X, Shen F et al (2017) Sensitive refractive index sensor based on an assembly-free fiber multi-mode interferometer fabricated by femtosecond laser. Opt Express 25(24):29896–29905

    Article  Google Scholar 

  13. Kushare M, Jhaveri A, Bhargav A. 2017 Radiation heat transfer analysis of spectrometer's Dewar Cooling Assembly[C]//2017 16th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm). IEEE, p 793–799

  14. Kim Y, Hong J, Choi B et al (2017) Assembly and alignment method for optimized spatial resolution of off-axis three-mirror fore optics of hyperspectral imager. Opt Express 25(17):20817–20828

    Article  Google Scholar 

  15. Song W, Chi M, Gao M et al (2017) Self-assembly directed synthesis of Au nanorices induced by polyaniline and their enhanced peroxidase-like catalytic properties. J Mater Chem C 5(30):7465–7471

    Article  Google Scholar 

  16. van Dommelen R, Fanzio P, Sasso L (2018) Surface self-assembly of colloidal crystals for micro-and nano-patterning. Adv Coll Interface Sci 251:97–114

    Article  Google Scholar 

  17. Zediker M S, Land M S, Rinzler C C, et al. 2017 Apparatus for performing oil field laser operations. U.S. Patent 9,534,447, p 1–3.

  18. Miller TN, Besuner RW, Levi ME et al (2018) Fabrication of the DESI corrector lenses[C]//advances in optical and mechanical technologies for telescopes and instrumentation III. Int Soc Opt Photonics 10706:107060X

    Google Scholar 

  19. Geyl R, Ruch E, Vayssade H et al (2011) NIRSpec optics development: the final report[C]//UV/optical/IR space telescopes and instruments: innovative technologies and concepts V. Int Soc Opt Photonics 8146:81460B

    Google Scholar 

  20. Roberts W T, Farr W H, Rider B, et al. 2017 Transceiver OPTICS FOR INTERPLANETARY communications[C]//international conference on space optics—ICSO 2010. international society for optics and photonics 10565: 105651G.

  21. Ramsey BD (2017) Optics for the imaging x-ray polarimetry explorer[C]//optics for EUV, X-ray, and gamma-ray astronomy VIII. Int Soc Opt Photonics 10399:1039907

    Google Scholar 

  22. Spiga D, Ferreira DDM, Shortt B et al (2017) Optical simulations for design, alignment, and performance prediction of silicon pore optics for the ATHENA x-ray telescope[C]//optics for EUV, X-ray, and gamma-ray astronomy VIII. Int Soc Opt Photonics 10399:103990H

    Google Scholar 

  23. Xu T, Yin C, Kan XF et al (2017) Drying-mediated optical assembly of silica spheres in a symmetrical metallic waveguide structure. Opt Lett 42(15):2960–2963

    Article  Google Scholar 

Download references

Acknowledgements

The study was supported by the Natural Science Foundation of Heilongjiang (Grant No. F2018030).

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  1. College of Science, Heilongjiang University of Science and Technology, Harbin, 150022, China

    Xiangbao Yin

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  1. Xiangbao Yin
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Correspondence to Xiangbao Yin.

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Yin, X. Driven by machine learning to intelligent damage recognition of terminal optical components. Neural Comput & Applic 33, 789–804 (2021). https://doi.org/10.1007/s00521-020-05051-x

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  • DOI: https://doi.org/10.1007/s00521-020-05051-x

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