Inteligência computacional no sensoriamento a fibra ótica

Abstract

This thesis presents new optical fiber sensing methodologies employing computational intelligence approaches seeking for the improvement of the sensing performance. Particularly, artificial neural networks, support vector regression, differential evolution and compressive sensing methods were employed with fiber Bragg grating transducers. Artificial neural networks (multilayer perceptron) and fiber Bragg gratings were used to determine the location of a load applied to a polymethyl methacrylate sheet. A new method based on the application of differential evolution is proposed to solve the inverse scattering problem in fiber Bragg gratings, where constraints are imposed to solve the problem without the need of phase information. A method for detecting multiple loads on a metal sheet is also proposed. In this method, the metal sheet is supported by iron rings containing fiber Bragg gratings, and compressive sensing methods are employed to solve the resulting underdetermined inverse problem. Further developments of the method replaced the iron rings by silicon blocks and employed a new reconstruction method based on compressive sensing and differential evolution. Experimental results show that the proposed computational methods improve the optical fiber sensing and lead to an enhancement of the spatial resolution without increasing the number of transducers.