光纤传感器频谱噪声降低基于一个生成对抗网络
Yujie Lu1, Qingbin Du1, Ruijia Zhang1
1School of Information Engineering, Huzhou University, Huzhou 313000, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
使用Cycle-GAN的新型深度学习方法有效地消除光纤传感器光谱. 这种方法显著提高了信号噪声比 (SNR) 和精度,增强了研究和工业中的实际应用.
科学领域:
- 光电学和光子学的光电子学和光子学.
- 信号处理 信号处理
- 人工智能在感知中的作用
背景情况:
- 减少光纤传感器光谱中的噪声对于准确的测量至关重要.
- 像波形变换 (WT) 和经验模式分解 (EMD) 这样的现有消极化方法有局限性.
研究的目的:
- 开发和评估基于深度学习的光纤传感器光谱的除方法.
- 为了提高信号噪声比 (SNR) 和光纤传感数据的准确性.
主要方法:
- 预处理传感器光谱成二维图像.
- 训练一个循环一致的生成对抗网络 (Cycle-GAN) 模型.
- 评估来自FPI,FBG,的FBG和FBG对传感器的模拟光谱的性能.
主要成果:
- 与传统方法相比,实现了高达13.71dB的SNR改进.
- 减少RMSE高达三倍,并保持R2≥99.70%与原始信号.
- 在温度响应中显示出优异的线性 (R2为99.95%) 降低多模式噪声.
结论:
- 拟议的Cycle-GAN无声化方法有效地减少光纤传感中的各种噪音类型.
- 这种方法提高了专业应用的光纤传感器的实用性和可靠性.
- 这种方法比传统的除尘技术具有显著的优势.
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