TY  - JOUR
AU  - Rabasović, Maja S
AU  - Savić-Šević, Svetlana N.
AU  - Križan, Janez
AU  - Matović, Branko
AU  - Nikolić, Marko
AU  - Šević, Dragutin
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11984
AB  - This paper examines the potential applications of machine learning algorithms in the analysis of optical spectra from Gd2O3:Er,Yb thermophosphor. The material was synthesized using the solution combustion method. For data acquisition, we employed pulsed laser diode excitation at 980 nm and utilized a streak camera with a spectrograph to obtain time-resolved spectral data of the optical emission from Gd2O3:Er,Yb. To ensure data consistency and facilitate visualization, we employed principal component analysis and Uniform Manifold Approximation and Projection clustering. Our findings demonstrate that, instead of the conventional approach of identifying spectral peaks and calculating intensity ratios, it is feasible to train computer software to recognize time-resolved spectra associated with different temperatures of the thermophosphor. Through our analysis, we have successfully devised a technique for remote temperature estimation by leveraging deep learning artificial neural networks.
T2  - Physica Scripta
T1  - Time resolved study of temperature sensing using Gd                    2                    O                    3                    :Er,Yb: deep learning approach
VL  - 98
IS  - 11
SP  - 116003
DO  - 10.1088/1402-4896/ad01ed
ER  - 

@article{
author = "Rabasović, Maja S and Savić-Šević, Svetlana N. and Križan, Janez and Matović, Branko and Nikolić, Marko and Šević, Dragutin",
year = "2023",
abstract = "This paper examines the potential applications of machine learning algorithms in the analysis of optical spectra from Gd2O3:Er,Yb thermophosphor. The material was synthesized using the solution combustion method. For data acquisition, we employed pulsed laser diode excitation at 980 nm and utilized a streak camera with a spectrograph to obtain time-resolved spectral data of the optical emission from Gd2O3:Er,Yb. To ensure data consistency and facilitate visualization, we employed principal component analysis and Uniform Manifold Approximation and Projection clustering. Our findings demonstrate that, instead of the conventional approach of identifying spectral peaks and calculating intensity ratios, it is feasible to train computer software to recognize time-resolved spectra associated with different temperatures of the thermophosphor. Through our analysis, we have successfully devised a technique for remote temperature estimation by leveraging deep learning artificial neural networks.",
journal = "Physica Scripta",
title = "Time resolved study of temperature sensing using Gd                    2                    O                    3                    :Er,Yb: deep learning approach",
volume = "98",
number = "11",
pages = "116003",
doi = "10.1088/1402-4896/ad01ed"
}

Rabasović, M. S., Savić-Šević, S. N., Križan, J., Matović, B., Nikolić, M.,& Šević, D.. (2023). Time resolved study of temperature sensing using Gd                    2                    O                    3                    :Er,Yb: deep learning approach. in Physica Scripta, 98(11), 116003.
https://doi.org/10.1088/1402-4896/ad01ed

Rabasović MS, Savić-Šević SN, Križan J, Matović B, Nikolić M, Šević D. Time resolved study of temperature sensing using Gd                    2                    O                    3                    :Er,Yb: deep learning approach. in Physica Scripta. 2023;98(11):116003.
doi:10.1088/1402-4896/ad01ed .

Rabasović, Maja S, Savić-Šević, Svetlana N., Križan, Janez, Matović, Branko, Nikolić, Marko, Šević, Dragutin, "Time resolved study of temperature sensing using Gd                    2                    O                    3                    :Er,Yb: deep learning approach" in Physica Scripta, 98, no. 11 (2023):116003,
https://doi.org/10.1088/1402-4896/ad01ed . .