一个结合频域物理先验和深度学习的度检测模型:对于SO2和NO混合气体在NH3干扰下
Bo Peng1, Haiwang Liu1, Shuai Li1
1Liangjiang School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 28, 2026
概括
本研究引入了一种混合模型,将频率分析和深度学习结合起来,在氨 (NH3) 存在时准确检测二氧化硫 (SO2) 和氧化 (NO). 该方法实现了低检测极限,克服了光谱干扰的挑战.
科学领域:
- 频谱学是一种光谱学.
- 环境科学 环境科学
- 机器学习 机器学习
背景情况:
- 二氧化硫 (SO2),氧化 (NO) 和氨 (NH3) 的紫外线吸收光谱在200-230 nm之间明显重叠.
- 这种光谱重叠使SO2和NO度的准确量化变得复杂.
研究的目的:
- 开发一种混合模型,用于准确检测SO2和NO,尽管存在NH3.3的光谱干扰.
- 改进气体度检测极限并减少复杂混合物的不确定性.
主要方法:
- 提出了一个混合模型,将频域物理先验与深度学习相结合.
- 一个带通过层利用频域特征来分离气体光谱.
- 一个具有注意力机制的平行双输出网络被设计用于特征提取.
主要成果:
- 混合模型在NH3干扰下实现了SO2的0.25 ppm和NO的0.26 ppm的检测极限.
- 检测SO2和NO的不确定性分别为1.25%和1.30%.
- 拉丁式超立方体采样和微调策略改善了对真实数据的模型性能.
结论:
- 拟议的混合模型有效地克服了多元组件气体分析中的光谱干扰.
- 这种方法为环境气体监测提供了一种新且高效的技术解决方案.
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