@article{ref1,
title="Semi-supervised learning for optical fiber sensor road intrusion signal detection",
journal="Applied optics (2004)",
year="2022",
author="He, Jun and Hu, Xing and Zhang, Dawei and Kong, Yong and Cheng, Jing and Xiao, Wenzhe",
volume="61",
number="6",
pages="C65-C72",
abstract="This paper proposes a road intrusion detection model based on distributed optical fiber vibration sensors signals. Considering that the existing unsupervised classification method often has a high false alarm rate when meeting the new non-intrusion samples, we propose a one-dimensional semi-supervised generative adversarial network (1D-SSGAN) model for intrusion signal recognition. The 1D-SSGAN is composed of a generator and a discriminator. The output layer of the discriminator is mapped to N+1 classes, and the generator and discriminator are trained on the N class dataset. During the learning process of the generator against the discriminator, many new samples are generated based on a small number of samples, which effectively expands the datasets and assists the training of the discriminator. Experimental result analysis demonstrates the effectiveness of the proposed model.<p /> <p>Language: en</p>",
language="en",
issn="1559-128X",
doi="10.1364/AO.437852",
url="http://dx.doi.org/10.1364/AO.437852"
}