Open AccessRandom forest assisted vector displacement sensor based on a multicore fiber
Author(s) -
Jingxian Cui,
Huaijian Luo,
Jianing Lu,
Xin Cheng,
Hwa Yaw Tam
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.425842
Subject(s) - displacement (psychology) , amplitude , optics , random forest , fiber optic sensor , optical fiber , fiber bragg grating , range (aeronautics) , acoustics , computer science , particle displacement , core (optical fiber) , algorithm , physics , artificial intelligence , materials science , psychology , composite material , psychotherapist
We proposed a two-dimensional vector displacement sensor with the capability of distinguishing the direction and amplitude of the displacement simultaneously, with improved performance assisted by random forest, a powerful machine learning algorithm. The sensor was designed based on a seven-core multi-core fiber inscribed with Bragg gratings, with a displacement direction range of 0-360° and the amplitude range related to the length of the sensor body. The displacement information was obtained under a random circumstance, where the performances with theoretical model and random forest model were studied. With the theoretical model, the sensor performed well over a shorter linear range (from 0 to 9 mm). Whereas the sensor assisted with random forest algorithm exhibits better performance in two aspects, a wider measurement range (from 0 to 45 mm) and a reduced measurement error of displacement. Mean absolute errors of direction and amplitude reconstruction were decreased by 60% and 98%, respectively. The proposed displacement sensor shows the possibility of machine learning methods to be applied in point-based optical systems for multi-parameter sensing.