Comparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy.

ABSTRACT: A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in non-homogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and mac

SUBMITTER: Kang R 

PROVIDER: S-EPMC11760626 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature