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结构化光增强机器学习用于纤维曲传感.

Sara Angelucci, Zhaozhong Chen, Ľubomír Škvarenina

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    概括
    此摘要是机器生成的。

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    轨道角动量 (OAM) 特性提取有效地减轻光纤传感中的光学扭曲. 与传统方法相比,这种结构光方法显著提高了准确性,并减少了数据需求.

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 机器学习 机器学习
    • 光纤传感传感器是指光纤传感器.

    背景情况:

    • 像光纤这样的复杂介质中的光学扭曲会导致通信和传感中的错误.
    • 传统方法很难准确地解释这些扭曲,限制传感能力.

    研究的目的:

    • 提出和演示轨道角动量 (OAM) 特性提取,以减轻光纤传感中的相位噪声.
    • 以利用多式联网作为利用OAM功能进行光纤传感的有效工具.

    主要方法:

    • 对于特征提取的被动全光轨道角动量 (OAM) 解复.
    • 使用OAM特征提取进行光纤曲跟踪的演示.
    • 与在强度测量上训练的卷积神经网络 (CNN) 的比较.

    主要成果:

    • 使用OAM特征提取实现了光纤曲跟踪94.1%的准确性.
    • 传统的基于强度的CNN方法在相同的任务中只能达到14%的准确性.
    • 对OAM特征的提取需要的训练信息比基于强度的测量少120倍.

    结论:

    • 轨道角动量 (OAM) 特性提取为减轻光纤传感中的光学扭曲提供了强大的解决方案.

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  • 结构光增强机器学习为先进的传感技术提供了一个有希望的途径.
  • 这种方法显著提高了光纤传感应用中的精度和数据效率.