基于离轴集成腔吸收光谱的二氧化碳传感,结合告知器和多层感知器模型
Kehao Zhang1, Tao Wu1, Linlin Shen2
1Key Laboratory of Nondestructive Test (Ministry of Education), Nanchang Hangkong University, Nanchang 330063, China.
Analytical chemistry
|January 30, 2025
概括
深度学习增强了二氧化碳 (CO2) 传感,使用离轴集成腔输出光谱 (OA-ICOS). 这种方法显著改善了信号与噪声比率和检测极限,以便更准确地测量二氧化碳度.
科学领域:
- 频谱学是一种光谱学.
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 离轴集成腔输出光谱 (OA-ICOS) 通过增加有效的吸收路径长度,为气体检测提供高灵敏度.
- 在OA-ICOS系统中,传统的噪声过方法表现出低效率和有限的特征提取能力,用于复杂的光谱数据.
- 深度学习模型擅长从大型光谱数据中提取特征,从而实现高效和准确的分析.
研究的目的:
- 开发一台利用OA-ICOS结合深度学习的二氧化碳传感器,以改进光谱数据处理和度预测.
- 研究"告知器"神经网络对过CO2光谱时间序列和增强信号噪声比 (SNR) 的有效性.
- 评估多层感知子 (MLP) 模型用于直接光谱特征提取和二氧化碳度预测的性能.
主要方法:
- 在近红外 (1.602微米) 光谱区域运行的OA-ICOS系统的实施,用于CO2检测.
- 利用射频 (RF) 噪声源来减轻空腔模式噪声,并在OA-ICOS系统中增强SNR.
- 应用"告知器"神经网络进行时间序列光谱数据过,然后使用MLP进行特征提取和度预测.
主要成果:
- "告知者"过方法与传统方法 (如萨维茨基-戈莱,卡尔曼和波形值过) 相比,SNR大约翻了一番.
- 对于二氧化碳度测量,MLP模型实现了线性相关系数 (R^2) 的显著改善,从79.74%提高到98.52%.
- 使用MLP模型,二氧化碳传感器的检测极限提高了3.79倍,在224.4秒内达到1.38ppm,相比于吸收-峰值适配方法.
结论:
- 深度学习方法的整合,特别是"informer"和MLP模型,为基于光谱的传感中的光谱数据处理提供了一种强大的方法.
- 拟议的传感器系统在SNR,准确性和CO2监测检测极限方面表现出卓越的性能.
- 这项研究强调了深度学习在光学气体传感和光谱分析领域的发展潜力.
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