发光
支持向量机
稳健性(进化)
计算机科学
材料科学
温度测量
大气温度范围
人工智能
机器学习
光电子学
物理
化学
热力学
生物化学
基因
作者
Wei Xu,Chenglong Xu,Junqi Cui,Chunhai Hu,Guilin Wen,Longjiang Zheng,Zhiguo Zhang,Zhen Sun,Yungang Zhang
出处
期刊:Optics Letters
[The Optical Society]
日期:2023-12-20
卷期号:49 (3): 606-606
被引量:4
摘要
Luminescence thermometry is a promising non-contact temperature measurement technique, but improving the precision and reliability of this method remains a challenge. Herein, we propose a thermal sensing strategy based on a machine learning. By using Gd 3 Ga 5 O 12 : Er 3+ -Yb 3+ as the sensing medium, a support vector machine (SVM) is preliminarily adopted to establish the relationship between temperature and upconversion emission spectra, and the sensing properties are discussed through the comparison with luminescence intensity ratio (LIR) and multiple linear regression (MLR) methods. Within a wide operating temperature range (303–853 K), the maximum and the mean measurement errors actualized by the SVM are just about 0.38 and 0.12 K, respectively, much better than the other two methods (3.75 and 1.37 K for LIR and 1.82 and 0.43 K for MLR). Besides, the luminescence thermometry driven by the SVM presents a high robustness, although the spectral profiles are distorted by the interferences within the testing environment, where, however, LIR and MLR approaches become ineffective. Results demonstrate that the SVM would be a powerful tool to be applied on the luminescence thermometry for achieving a high sensing performance.
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