Transverse Load Discrimination in Long-Period Fiber Grating via Artificial Neural Network

AUTOR(ES)

Barino, F. O.; Delgado, F. S.; Santos, A. Bessa dos

FONTE

J. Microw. Optoelectron. Electromagn. Appl.

DATA DE PUBLICAÇÃO

2020-03

RESUMO

Abstract We present a general investigation of a Long-Period Grating (LPG) for transverse strain measurement. The transverse strain sensing characteristics, for instance, the load intensity and azimuthal angle, are analyzed with the data set generated by the LPG sensor and probed by artificial neural network (ANN). Furthermore, we evaluate and compare the predictive performance of the interrogation model considering the square correlation coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results indicate that the ANN model could be successfully employed to estimate the load intensity and azimuthal angle using a single LPG sensor.

Documentos Relacionados

• Alternative technique for biodiesel quality control using an optical fiber long-period grating sensor
• Quantal release, incremental detection, and long-period Ca2+ oscillations in a model based on regulatory Ca2+-binding sites along the permeation pathway.
• Long Period Bragg Grating in Coaxial Transmission Lines
• Geoprocessing in the discrimination of degraded pastures using artificial neural network in image Ikonos II
• Prediction of energy absorption capability in fiber reinforced self-compacting concrete containing nano-silica particles using artificial neural network