基于深度学习的多模式纤维分布式温度传感器
Luxuan Yang1, Xiaoyan Wang1, Tong Wu1
1Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究使用多模光纤 (MMF) 进行深度学习,用于精确的非接触式温度传感. 该方法可以准确地预测温度和加热点的位置,即使在危险的环境中.
科学领域:
- 光电学是指光电子产品.
- 人工智能的人工智能
- 传感器技术 传感器技术
背景情况:
- 多模纤维 (MMF) 产生温度敏感的激光斑纹图案.
- 传统的温度传感方法可能缺乏精度或不适合危险环境.
研究的目的:
- 开发一种高精度的非接触式温度传感方法,使用MMF和深度学习.
- 预测温度和加热点的确切位置.
主要方法:
- 设计了一个基于MMF的温度传感配置.
- 开发了一种双输出卷积神经网络 (CNN) 用于数据分析.
- 构建了一个用于培训和验证的数据集.
主要成果:
- 在预测加热点位置方面实现了100%的准确性.
- 在±1°C的边际内,证明了高温预测准确度 (在两个实验中为100%和95.12%).
- 热点位置的精度小于1厘米.
- 在不同类型的货币市场基金中保持一致的高准确性.
结论:
- 深度学习显著提高了基于MMF的温度传感精度.
- 开发的方法提供了准确的,非接触温度和位置监测.
- 这种技术非常适用于危险环境中的应用,需要精确的热量测量.
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