神经网络增强的FMCW气体光谱
Xunzhou Xiao1, Zihuai Liu2, Haojia Sun1
1Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, 999077, China.
ACS sensors
|September 4, 2025
概括
这项研究引入了一个神经网络增强的激光光谱系统,用于准确的多元组件气体检测. 该方法简化了信号处理并提高了广泛度范围的测量精度.
科学领域:
- 激光光谱学
- 化学传感器
- 科学中的人工智能
背景情况:
- 激光光谱在多元组件气体检测方面面临挑战,包括复杂的设置和分析.
- 神经网络为光谱实验的自动化和优化提供了潜力.
- 精确的气体传感对于环境监测,医疗诊断和工业过程控制至关重要.
研究的目的:
- 开发一个通过前传神经网络 (FNN) 增强的频率调制连续波 (FMCW) 光谱系统.
- 展示FNN分析叠加光谱的能力,以准确确定气体度.
- 展示该系统在简化信号处理和提高测量精度方面的潜力.
主要方法:
- 使用FMCW光谱将气体吸收光谱编码为光学信号.
- 训练FNN算法来分析特定气体组件的宽带叠加光谱.
- 使用乙 (C2H2) 和二氧化碳 (CO2) 混合物进行概念验证演示.
主要成果:
- 在调节混合气体时,FNN实现了高精度:C2H2的残留量<±2ppm (100-900ppm) 和CO2的残留量±0.3% (80%-96%).
- 与传统方法相比,FNN表现出优异的线性动态反应,R2> 0. 99999跨越5个数量级.
- 该系统通过简化信号处理实现了高精度量化.
结论:
- 用FNN增强的FMCW光谱系统为准确的多元组件气体检测提供了有前途的方法.
- 这种方法简化了光谱分析,提高了测量精度,优于传统技术.
- 这种技术非常适合于环境,医疗和工业应用中的准分布式传感.
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