深度学习用于通过红外光谱检测的气体传感
M Arshad Zahangir Chowdhury1, Matthew A Oehlschlaeger1
1Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, 110 Eighth St., Troy, NY 12180, USA.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
深度学习使用红外光谱准确地识别混合物中的多种气体. 这种新的卷积神经网络方法在大气和工业气体物种化方面实现了82-97%的准确性.
科学领域:
- 频谱学是一种光谱学.
- 人工智能的人工智能
- 环境监测 环境监测
背景情况:
- 深度学习 (DL) 越来越多地用于光谱和气体传感.
- 使用DL从红外 (IR) 光谱中识别多组分混合物中的单个气体是一个尚未探索的领域.
- 精确的气体物种化对于大气和工业过程监测至关重要.
研究的目的:
- 开发和评估DL模型,用于识别和量化混合物中的多种气体,使用IR吸收光谱.
- 为了证明模型在各种大气和工业气体中的有效性.
- 调查模型的内部运作,以优先考虑光谱特征.
主要方法:
- 一个单维的深卷积神经网络 (CNN) 模型被设计用于气体分类.
- 使用HITRAN数据生成了空气中的十个关键分子 (例如,H2O,CO2,O3,NH3) 的红外吸收光谱的模拟数据集.
- 美国有线电视新闻网的模型是在模拟的光谱上训练的,并用噪音数据和合成实验光谱进行了测试.
主要成果:
- DL模型在预测混合物中的气体物种化方面实现了高准确度,从82%到97%.
- 类激活地图可视化了模型对特定光谱区域的重点进行分类.
- 该模型成功预测了合成实验混合物光谱的物种化,验证了其实际适用性.
结论:
- 拟议的CNN模型提供了一种通用且有效的方法,用于使用红外光谱进行多元组分气体物种化.
- 该方法可适应不同的气体,光谱范围和光谱学类型.
- 这项工作代表了在HITRAN模拟中训练有素的CNN用于气体混合物的光谱识别的首次应用.
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