Text

Rapid Response DAS Denoising Method Based on Deep Learning

In most optical fiber distributed acoustic sensing (DAS) systems, to obtain the desired outcome, the sensing signal acquired by DAS systems normally needs to be denoised. In some applications, such as the identifiation of the position of fast-moving targets, we need DAS systems to respond with sufficient speed. However, most classical denoising algorithms do not work if the signals are insufficiently collected within a short period (called a short-time signal). To obtain ideal results within a short time window, we propose an attention-based convolutional neural network (CNN) structure with extremely short signal windows to learn and approximate the results of classical denoising methods. To evaluate the effectiveness of the proposed method, the experiment is conducted under a real field highway scenario where the desired signals are overwhelmed with noise. The results show that by using signals collected within extremely short time windows of 100 ms, an insufficient time for the processing of existing denoising algorithms, our structure yields a satisfactory denoising performance.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.8 April 15 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2583 - 2593

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENTUK GALAKSI
DISTRIBUTED OPTICAL FIBER ACOUSTIC SENSING (DAS)
SIGNAL DENOSING

Info Detail Spesifik

DOI: 10.1109/JLT.2021.3052651

Pernyataan Tanggungjawab

Maoning Wang, Lin Deng, Yuzhong Zhong, Jianwei Zhang, Fei Peng

Versi lain/terkait

Lampiran Berkas

Komentar