Machine Learning Assisted Fiber Bragg Grating-Based Temperature Sensing | Zendy

Martin S. E. Djurhuus | Zendy; Stefan Werzinger | Zendy; Bernhard Schmauß | Zendy; A.T. Clausen | Zendy; Darko Zibar | Zendy

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Machine Learning Assisted Fiber Bragg Grating-Based Temperature Sensing

Author(s) -

Martin S. E. Djurhuus,

Stefan Werzinger,

Bernhard Schmauß,

A.T. Clausen,

Darko Zibar

Publication year - 2019

Publication title -

ieee photonics technology letters

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.81

H-Index - 157

eISSN - 1941-0174

pISSN - 1041-1135

DOI - 10.1109/lpt.2019.2913992

Subject(s) - fiber bragg grating , ground penetrating radar , kriging , temperature measurement , computer science , gaussian process , gaussian , optical fiber , process (computing) , signal (programming language) , signal processing , acoustics , electronic engineering , machine learning , engineering , physics , radar , telecommunications , quantum mechanics , programming language , operating system

This letter proposes an alternative approach to the signal processing of temperature measurements based on fiber Bragg gratings (FBGs) using the machine learning tool Gaussian process regression (GPR). The experimental results show that for a majority of the cases under consideration, the reported technique provides a more accurate calculation of the temperature than the conventional methods. Furt...

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