基于数据的模型的比较分析,用于空间分辨温度测量,使用辐射光谱学
Ruiyuan Kang1, Dimitrios C Kyritsis2, Panos Liatsis3
1Directed Energy Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
PloS one
|January 24, 2025
概括
这项研究引入了一种新的数据驱动方法,用于使用辐射光谱测量空间分辨率的温度测量. 功能工程与机器学习模型相结合,有效地测量非均的温度分布,即使在未知气体度的情况下也是如此.
科学领域:
- 频谱学是一种光谱学.
- 数据科学数据科学数据科学
- 热力学是一种热力学.
背景情况:
- 视线辐射光谱在测量非均场中的温度方面存在局限性.
- 空间分辨率的温度测量对于理解复杂系统至关重要.
研究的目的:
- 开发和评估数据驱动的模型,用于使用辐射光谱测量空间分辨率的温度测量.
- 将特征工程与经典机器学习的性能与端到端卷积神经网络 (CNN) 的性能进行比较.
主要方法:
- 研究了两种类型的数据驱动方法:使用经典机器学习和CNN的功能工程.
- 评估了与15个经典机器学习模型相结合的15个特征组.
- 评估了11个CNN模型用于温度分布测量.
主要成果:
- 功能工程与机器学习相结合,超过了直接的CNN应用程序.
- 物理引导的转换,信号表示和主要组件分析被证明是最有效的特征提取.
- 使用提取特征的轻混合器组合模型实现了最佳性能 (RMSE: 64.3,RE: 0.017,RRMSE: 0.025,R: 0.994).
结论:
- 拟议的方法,利用特征工程和光混合器模型,准确地测量来自低分辨率光谱的不均温度分布.
- 这种方法即使在物种度分布未知的情况下也有效,克服了传统光谱学的局限性.
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