Machine learning models on demodulation of FBG sensors
Pressure, acceleration, vibration, strain, and temperature are all often measured with Fiber Bragg Grating (FBG) sensors. Spectral overlapping in the wavelength domain can occur in a multiplexed FBG network. The main difficulty that will be explored in this project is demodulating a single FBG...
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2022
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sg-ntu-dr.10356-1575132023-07-07T19:17:16Z Machine learning models on demodulation of FBG sensors Cheok, Jake Ke Jun Wong Liang Jie School of Electrical and Electronic Engineering liangjie.wong@ntu.edu.sg Engineering::Electrical and electronic engineering Pressure, acceleration, vibration, strain, and temperature are all often measured with Fiber Bragg Grating (FBG) sensors. Spectral overlapping in the wavelength domain can occur in a multiplexed FBG network. The main difficulty that will be explored in this project is demodulating a single FBG wave from a made by mixing spectrum. To determine the center Bragg wavelengths of each detector in the overlapping condition, the lowest technique, linear regression used in this project. With a root mean square error of 0.21 pm and an average testing duration of 0.8 milliseconds, a 30 layers residual Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-19T05:16:11Z 2022-05-19T05:16:11Z 2022 Final Year Project (FYP) Cheok, J. K. J. (2022). Machine learning models on demodulation of FBG sensors. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157513 https://hdl.handle.net/10356/157513 en A2256-211 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Cheok, Jake Ke Jun Machine learning models on demodulation of FBG sensors |
| description |
Pressure, acceleration, vibration, strain, and temperature are all often measured with
Fiber Bragg Grating (FBG) sensors. Spectral overlapping in the wavelength domain
can occur in a multiplexed FBG network. The main difficulty that will be explored in
this project is demodulating a single FBG wave from a made by mixing spectrum.
To determine the center Bragg wavelengths of each detector in the overlapping
condition, the lowest technique, linear regression used in this project.
With a root mean square error of 0.21 pm and an average testing duration of 0.8
milliseconds, a 30 layers residual |
| author2 |
Wong Liang Jie |
| author_facet |
Wong Liang Jie Cheok, Jake Ke Jun |
| format |
Final Year Project |
| author |
Cheok, Jake Ke Jun |
| author_sort |
Cheok, Jake Ke Jun |
| title |
Machine learning models on demodulation of FBG sensors |
| title_short |
Machine learning models on demodulation of FBG sensors |
| title_full |
Machine learning models on demodulation of FBG sensors |
| title_fullStr |
Machine learning models on demodulation of FBG sensors |
| title_full_unstemmed |
Machine learning models on demodulation of FBG sensors |
| title_sort |
machine learning models on demodulation of fbg sensors |
| publisher |
Nanyang Technological University |
| publishDate |
2022 |
| url |
https://hdl.handle.net/10356/157513 |
| _version_ |
1772829041931321344 |