Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157513
Title: Machine learning models on demodulation of FBG sensors
Authors: Cheok, Jake Ke Jun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Project: A2256-211
Abstract: 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
URI: https://hdl.handle.net/10356/157513
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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