TY  - JOUR
PY  - 2022//
TI  - Semi-supervised learning for optical fiber sensor road intrusion signal detection
JO  - Applied optics (2004)
A1  - He, Jun
A1  - Hu, Xing
A1  - Zhang, Dawei
A1  - Kong, Yong
A1  - Cheng, Jing
A1  - Xiao, Wenzhe
SP  - C65
EP  - C72
VL  - 61
IS  - 6
N2  - 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>
LA  - en
SN  - 1559-128X
UR  - http://dx.doi.org/10.1364/AO.437852
ID  - ref1
ER  -