In this paper, we present a dynamic calibration method for FBG sensor temperature measurement, utilizing the online sequential extreme learning machine (OS-ELM). During the measurement process, the calibration model is continuously updated instead of retrained, which can reduce tedious calculations. In particular, fiber Bragg grating (FBG) sensors are excellent candidates for sensing various physical quantities, including temperature and strain, owing to their remarkable properties like small size, high accuracy, and low energy consumption. An FBG which is used for a wide temperature range needs an expensive calibration curve measured for this particular FBG to enable the.
Fiber Bragg grating (FBG) sensors are prone to spectral distortions in practical applications, which may cause large demodulation errors. There are many algorithms demodulating FBG spectra, but no approach is able to evaluate demodulation results under spectral. Fibre Bragg grating (FBG) sensors are used to measure various quantities such as temperature, stress, vibrations, pressure, or refractive index. Their most important advantage is signal modulation consisting in shifting the spectrum in the wavelength domain. By changing the step size of each calculation.
A fiber Bragg grating (FBG) is a type of constructed in a short segment of that reflects particular of light and transmits all others. This is achieved by creating a periodic variation in the of the fiber core, which generates a wavelength-specific. Hence a fiber Bragg grating can be used as an inline to block certain wavelengths, can be use.
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A fiber Bragg grating (FBG) is a type of constructed in a short segment of that reflects particular of light and transmits all others. This is achieved by creating a periodic variation in the of the fiber core, which generates a wavelength-specific. Hence a fiber Bragg grating can be used as an inline to block certain wavelengths, can be use.
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