通过EWT-SE-WTD增强的BPNN模型预测中红外TDLAS系统中的CO度
Tingting Zhang1, Guo Sun1, Qinduan Zhang1
1Shandong Key Laboratory of Optoelectronic Sensing Technologies / National-local Joint Engineering Laboratory for Energy and Environment Fiber Smart Sensing Technologies, Laser Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
这项研究引入了用于可调节二极管激光吸收光谱 (TDLAS) 气体检测的先进消噪方法. 该技术显著提高了信号质量,并使高精度,低度气体检测成为可能.
科学领域:
- 频谱学是一种光谱学.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 在TDLAS系统中,非线性噪声和背景干扰会降低检测准确度.
- 第二波信号的特征特征特别容易受到干扰.
- 准确的气体检测对于环境监测和工业安全至关重要.
研究的目的:
- 开发一种强大的方法来消除TDLAS信号的噪音.
- 为了提高气体度检测的精度和灵敏度.
- 为了提高反向传播神经网络 (BPNN) 模型在气体传感中的性能.
主要方法:
- 经验波形变换 (EWT) 用于自适应信号分解.
- 用于信号重建的光谱 (SE) 和波段值消解 (WTD).
- 将denoising算法与BPNN集成在一起,用于度预测.
主要成果:
- 信号与噪声比 (SNR) 从95.48提高到583.56.
- 对于预测度,获得了0.9999的合适相关系数 (R2).
- 建立了171ppb的系统检测极限 (LOD).
结论:
- 拟议的EWT-SE-WTD报销方法有效地提高了TDLAS信号质量.
- 综合方法证明了低度气体检测的高精度和稳定性.
- 这种方法为敏感和精确的气体传感应用提供了显著的进步.
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