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基于深度学习的同时温度和曲率敏感的散射图识别.

Jianli Liu1, Yuxin Ke2, Dong Yang3

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

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PubMed
概括
此摘要是机器生成的。

本研究对多模光纤 (MMF) 光散射的温度和曲率效应进行了建模. 一个优化的神经网络准确地识别了这些对货币基金的环境影响.

关键词:
深度学习是一种深度学习.光纤传感器是光纤传感器.有限元素方法的有限元素方法.散射图 (scatterplot) 是一个散射图.温度识别功能 温度识别功能

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 多模纤维 (MMF) 中的光传播显示了由外部因素影响的复杂的散射模式.
  • 了解这些模式对于可靠的光纤传感和通信至关重要.

研究的目的:

  • 以数值建模温度和曲率对MMF光散射的影响.
  • 开发和验证一个优化的神经网络来识别这些环境影响.

主要方法:

  • 有限元法 (FEM) 用于模拟温度和曲率效应.
  • 对折射率和曲损失进行分析,以确定关键曲半径 (15毫米).
  • 开发一个端到端的残余神经网络用于散射特征提取.

主要成果:

  • 确定了MMF的15毫米的临界曲半径.
  • 为各种曲半径生成温度斑点地图.
  • 优化的神经网络在识别温度和曲率散射图方面表现出高精度.

结论:

  • 拟议的神经网络模型对于识别货币市场基金中的温度和曲率效应是有效和强大的.
  • 数字模拟和实验验证了该方案的实际可行性.