当前位置: X-MOL 学术Opt. Express › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Random forest assisted vector displacement sensor based on a multicore fiber
Optics Express ( IF 3.8 ) Pub Date : 2021-05-07 , DOI: 10.1364/oe.425842
Jingxian Cui , Huaijian Luo , Jianing Lu , Xin Cheng , Hwa-Yaw Tam

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.

中文翻译:

基于多芯光纤的随机森林辅助矢量位移传感器

我们提出了一种二维矢量位移传感器,该传感器能够同时区分位移的方向和幅度,并借助随机森林(一种功能强大的机器学习算法)来改善性能。该传感器是基于刻有布拉格光栅的七芯多芯光纤设计的,位移方向范围为0-360°,幅度范围与传感器主体的长度有关。在随机情况下获得位移信息,并用理论模型和随机森林模型研究其性能。根据理论模型,该传感器在较短的线性范围(0至9 mm)内表现良好。结合随机森林算法的传感器在两个方面表现出更好的性能,测量范围更广(从0到45 mm),并减少了位移的测量误差。方向和幅度重构的平均绝对误差分别减少了60%和98%。提出的位移传感器显示了将机器学习方法应用于基于点的光学系统中进行多参数传感的可能性。
更新日期:2021-05-10
down
wechat
bug