折射率结构参数建模的监督机器学习

IF 1.3 Q3 INSTRUMENTS & INSTRUMENTATION
Antonios Lionis, Konstantinos Peppas, Hector E. Nistazakis, Andreas Tsigopoulos, Keith Cohn, Kyle R. Drexler
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引用次数: 0

摘要

希腊海军学院(HNA)报告了位于Psitalia岛灯塔和学院实验室大楼之间的中程、近海、自由空间激光通信测试设施的最新结果。FSO链路建立在比雷埃夫斯港的场地内,路径长度为2958米,平均高度为35米,主要在水面以上。最近,该设施通过增加BLS450闪烁仪进行了升级,该闪烁仪与MRV TS5000/155 FSO系统和WS-2000气象站位于同一位置。本文介绍了2022年5月24日至31日收集的初步光学湍流测量结果,以及宏观气象参数。四种机器学习算法(随机森林(RF)、梯度增强回归(GBR)、单层(ANN)和深度神经网络(DNN))用于折射率-结构-参数回归建模。此外,另一个DNN用于将光学湍流的强度等级分类为强或弱。结果表明,所有模型的预测精度都很高。其中,人工神经网络算法的r平方为0.896,均方误差(MSE)为0.0834;RF算法也给出了高度可接受的r平方0.865和均方根误差(RMSE) 0.241。梯度增强回归器(GBR)的r平方为0.851,RMSE为0.252,最后,DNN算法的r平方为0.79,RMSE为0.088。考虑到目标值(Cn2)的高度可变性,dnn湍流强度分类模型表现出非常可接受的分类性能,因为我们观察到该模型的预测精度为87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supervised Machine Learning for Refractive Index Structure Parameter Modeling
The Hellenic Naval Academy (HNA) reports the latest results from a medium-range, near-maritime, free-space laser-communications-testing facility, between the lighthouse of Psitalia Island and the academy’s laboratory building. The FSO link is established within the premises of Piraeus port, with a path length of 2958 m and an average altitude of 35 m, mainly above water. Recently, the facility was upgraded through the addition of a BLS450 scintillometer, which is co-located with the MRV TS5000/155 FSO system and a WS-2000 weather station. This paper presents the preliminary optical turbulence measurements, collected from 24 to 31 of May 2022, alongside the macroscopic meteorological parameters. Four machine-learning algorithms (random forest (RF), gradient boosting regressor (GBR), single layer (ANN), and deep neural network (DNN)) were utilized for refractive-index-structural-parameter regression modeling. Additionally, another DNN was used to classify the strength level of the optical turbulence, as either strong or weak. The results showed very good prediction accuracy for all the models. Specifically, the ANN algorithm resulted in an R-squared of 0.896 and a mean square error (MSE) of 0.0834; the RF algorithm also gave a highly acceptable R-squared of 0.865 and a root mean square error (RMSE) of 0.241. The Gradient Boosting Regressor (GBR) resulted in an R-squared of 0.851 and a RMSE of 0.252 and, finally, the DNN algorithm resulted in an R-squared of 0.79 and a RMSE of 0.088. The DNN-turbulence-strength-classification model exhibited a very acceptable classification performance, given the highly variability of our target value (Cn2), since we observed a predictive accuracy of 87% with the model.
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来源期刊
CiteScore
2.80
自引率
28.60%
发文量
27
审稿时长
11 weeks
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