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Title: Random forest assisted vector displacement sensor based on a multicore fiber
Authors: Cui, J 
Luo, H 
Lu, J 
Cheng, X 
Tam, HY 
Issue Date: May-2021
Source: Optics express, 10 May 2021, v. 29, no. 10, p. 15852-15864
Abstract: 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.
Publisher: Optical Society of America
Journal: Optics express 
EISSN: 1094-4087
DOI: 10.1364/OE.425842
Rights: © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement (
Journal © 2021
© 2021 Optical Society of America. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.
The following publication Cui, J., Luo, H., Lu, J., Cheng, X., & Tam, H. Y. (2021). Random forest assisted vector displacement sensor based on a multicore fiber. Optics Express, 29(10), 15852-15864 is available at
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