A machine learning approach for improving monitoring in passive optical networks with almost equidistant branches is proposed and experimentally validated. It achieves a high diagnostic accuracy of 98.7% and an event localization error of 0.5m

翻译：本文提出了一种基于机器学习方法来改进几乎等距分支的被动光网络监测的方法，并得到了实验验证。该方法实现了高达98.7%的诊断准确性和0.5m的事件定位误差。