机器学习辅助的确定C6H14分子分数从激光诱导的气等离子体的分子排放
Ashwin P Rao1, Noshin Nawar2, Christopher J Annesley1
1Space Vehicles Directorate, Air Force Research Laboratory, Kirtland Air Force Base, New Mexico, USA.
Applied spectroscopy
|February 26, 2024
概括
机器学习模型使用光谱特征准确量化空气等离子体中的六. 这些模型为在复杂环境中检测碳物种提供了高灵敏度.
科学领域:
- 血物理学的等离子体物理学
- 频谱学是一种光谱学.
- 机器学习是机器学习.
背景情况:
- 碳化合物的激光诱导等离子体表现出复杂的光谱特征.
- 通过光谱分析量化碳化合物含量是具有挑战性的,因为动态复杂性和温度不平衡.
研究的目的:
- 开发机器学习模型,用于量化-空气等离子体中的分子分数.
- 利用CN紫色和C2天光谱特征进行准确的预测.
主要方法:
- 为了准确的预测,采用了集成回归方法.
- 使用人工神经网络回归来实现高灵敏度.
- 模型被训练在从气等离子体的光谱数据.
主要成果:
- 集成回归模型实现了高精度,根平均平方误差大约为10^-2.
- 人工神经网络模型表现出异常灵敏度,检测极限低至0.008.
- 该研究成功地将光谱特征与六分子分数相关联.
结论:
- 机器学习模型提供了一种可靠的方法来量化等离子体中的碳化合物含量.
- 这些模型可以改进,用于分析复杂环境中的碳物种,例如高速反应流.
- 开发的方法促进了动态系统中的等离子体诊断和化学分析.
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