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Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors via Artificial Neural Network Integration.
- Source :
- Optics (2673-3269); Jun2024, Vol. 5 Issue 2, p223-237, 15p
- Publication Year :
- 2024
-
Abstract
- In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- Volume :
- 5
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Optics (2673-3269)
- Publication Type :
- Academic Journal
- Accession number :
- 178184012
- Full Text :
- https://doi.org/10.3390/opt5020016