2019 Ieee Sensors 2019
DOI: 10.1109/sensors43011.2019.8956638
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Structural Health Monitoring using Apodized Pi-Phase Shifted FBG: Decoupling Strain and Temperature Effects

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Cited by 7 publications
(2 citation statements)
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“…It effectively verifies the feasibility of the model for calibrating and decoupling non-linear effects in the designed sensor. Non-linear calibration methods have as well been studied for FBG sensors [37][38][39], but the calibration processes were obtained via back-propagation method in the studies. The neural network, introduced as decoupling algorithm, in our study derives its theoretical induction model better in the working broadband that the linear calibration methods that are based on traditional least squares and back-propagation fitting methods.…”
Section: Discussionmentioning
confidence: 99%
“…It effectively verifies the feasibility of the model for calibrating and decoupling non-linear effects in the designed sensor. Non-linear calibration methods have as well been studied for FBG sensors [37][38][39], but the calibration processes were obtained via back-propagation method in the studies. The neural network, introduced as decoupling algorithm, in our study derives its theoretical induction model better in the working broadband that the linear calibration methods that are based on traditional least squares and back-propagation fitting methods.…”
Section: Discussionmentioning
confidence: 99%
“…The proposed techniques fall into three categories. In the rst category, more than one uniform FBG is used [19][20][21] and in the second category, only one special (nonuniform) FBG is adopted for simultaneous measurement [22][23][24][25][26]. Both categories have disadvantages such as the use of more than one uniform FBG, complicated setup and fabrication of special FBGs, excessive use of spectral sources, and di cult accessibility.…”
Section: Introductionmentioning
confidence: 99%