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Deep Learning-Based Simultaneous Temperature- and Curvature-Sensitive Scatterplot Recognition.

Jianli Liu1, Yuxin Ke2, Dong Yang3

  • 1School of Mechanical Engineering, Yangtze University, Jingzhou 434023, China.

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|July 13, 2024
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Summary
This summary is machine-generated.

This study models temperature and curvature effects on multimode fiber (MMF) light scattering. An optimized neural network accurately identifies these environmental influences on MMFs.

Keywords:
deep learningfiber optic sensorfinite element methodscatterplottemperature recognition

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Area of Science:

  • Optics and Photonics
  • Materials Science
  • Artificial Intelligence

Background:

  • Light propagation in multimode fibers (MMF) displays complex scattering patterns influenced by external factors.
  • Understanding these patterns is crucial for reliable optical fiber sensing and communication.

Purpose of the Study:

  • To numerically model the impact of temperature and curvature on MMF light scattering.
  • To develop and validate an optimized neural network for identifying these environmental influences.

Main Methods:

  • Finite element method (FEM) for modeling temperature and curvature effects.
  • Analysis of refractive index and bending loss to determine the critical bending radius (15 mm).
  • Development of an end-to-end residual neural network for scattering feature extraction.

Main Results:

  • A critical bending radius of 15 mm was identified for the MMF.
  • Temperature speckle atlases were generated for various bending radii.
  • The optimized neural network demonstrated high accuracy in identifying temperature and curvature scattering maps.

Conclusions:

  • The proposed neural network model is effective and robust for identifying temperature and curvature effects in MMFs.
  • Numerical simulations and experiments validate the scheme's practical viability.