敌对的未配对解网络 (AUDNet):在UV-DOAS中对严重重叠的CS2/SO2信号进行精确的分析
1School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|December 6, 2025
概括
本研究引入了一种新的系统,将UV-DOAS与生成对抗网络 (GAN) 结合起来,以准确检测气体绝缘开关设备中的CS2和SO2. 该方法有效地分离重叠的光谱,改善绝缘状况评估.
科学领域:
- 频谱学是一种光谱学.
- 气体分析 气体分析
- 机器学习 机器学习
背景情况:
- 二硫化碳 (CS2) 和二氧化硫 (SO2) 是评估气体绝缘开关装置绝缘状况的关键指标.
- 紫外差光学吸收光谱法 (UV-DOAS) 适合检测这些气体,因为它们的紫外线吸收峰值.
- 在UV-DOAS测量中的光谱重叠对准确的气体度确定构成重大挑战.
研究的目的:
- 在混合气体环境中开发先进的CS2和SO2检测系统.
- 为了克服传统UV-DOAS方法固有的光谱重叠干扰.
- 提高气体分析的精度和灵敏度,用于气体隔热开关设备的监控.
主要方法:
- 将UV-DOAS与用于光谱分析的生成对抗网络 (GAN) 集成.
- 基于WGAN-GP的对抗性未配对解网络 (AUDNet) 的应用,用于光谱解.
- 在应用GAN模型之前,使用DOAS预处理混合光谱.
主要成果:
- 实现了低平均绝对百分比误差 (MAPE),CS2为1.67%,SO2为1.12%.
- 在50厘米光学路径下,CS2的检测极限为0.33ppb,SO2的检测极限为7.04ppb.
- 拟议的系统不需要严格的频谱配对培训,提供比现有的深度学习方法更大的灵活性.
结论:
- 联合的UV-DOAS和GAN系统有效地解决了CS2/SO2检测中的光谱重叠问题.
- 这种方法显著提高了用于关键基础设施监控的气体分析的准确性和灵敏性.
- 该方法提供了一个强大而灵活的解决方案,用于实时评估气体绝缘开关装置的绝缘状态.
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