计算机科学
光时域反射计
人工智能
模式识别(心理学)
人工神经网络
学习迁移
事件(粒子物理)
集合(抽象数据类型)
训练集
机器学习
支持向量机
数据挖掘
光纤
物理
程序设计语言
渐变折射率纤维
电信
量子力学
光纤传感器
作者
Yi Shi,Shangwei Dai,Xinyu Liu,Yingchao Zhang,Xinjie Wu,Tao Jiang
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2022-08-04
卷期号:30 (17): 31232-31232
被引量:21
摘要
Thanks to the development of machine learning and deep learning, data-driven pattern recognition based on neural network is a trend for Φ-OTDR system intrusion event recognition. The data-driven pattern recognition needs a large number of samples for training. However, in some scenarios, intrusion signals are difficult to collect, resulting in the lack of training samples. At the same time, labeling a large number of samples is also a very time-consuming work. This paper presents a few-shot learning classification method based on time series transfer and cycle generative adversarial network (CycleGAN) data augmentation for Φ-OTDR system. By expanding the rare samples based on time series transfer and CycleGAN, the number of samples in the dataset can finally meet the requirement of network training. The experimental result shows that even when the training set has two minor classes with only two samples, the average accuracy of the validation set with 5 classification tasks can still reach 90.84%, and the classification accuracy of minor classes can reach 79.28% with the proposed method.
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