Denoising and robust temperature extraction for botda systems based on denoising autoencoder and DNN

Biwei Wang; Nan Guo; Liang Wang; Changyuan Yu; Chao Lu

Denoising and robust temperature extraction for botda systems based on denoising autoencoder and DNN

Biwei Wang, Nan Guo, Liang Wang, Changyuan Yu, Chao Lu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

Denoising autoencoder is used for denoising of the data obtained by the Brillouin optical time-domain analyzer (BOTDA) sensing system and is also used to form the deep neural networks (DNN) for robust temperature information extraction.

Original language	English
Title of host publication	Optical Fiber Sensors, OFS 2018
Publisher	OSA - The Optical Society
ISBN (Print)	9781943580507
Publication status	Published - 24 Sep 2018
Event	Optical Fiber Sensors, OFS 2018 - Lausanne, Switzerland Duration: 24 Sep 2018 → 28 Sep 2018

Publication series

Name	Optics InfoBase Conference Papers
Volume	Part F124-OFS 2018

Conference

Conference	Optical Fiber Sensors, OFS 2018
Country	Switzerland
City	Lausanne
Period	24/09/18 → 28/09/18

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

Cite this

@inproceedings{7adeb35e6fc84ad68f2047716a5bb038,

title = "Denoising and robust temperature extraction for botda systems based on denoising autoencoder and DNN",

abstract = "Denoising autoencoder is used for denoising of the data obtained by the Brillouin optical time-domain analyzer (BOTDA) sensing system and is also used to form the deep neural networks (DNN) for robust temperature information extraction.",

author = "Biwei Wang and Nan Guo and Liang Wang and Changyuan Yu and Chao Lu",

year = "2018",

month = sep,

day = "24",

language = "English",

isbn = "9781943580507",

series = "Optics InfoBase Conference Papers",

publisher = "OSA - The Optical Society",

booktitle = "Optical Fiber Sensors, OFS 2018",

note = "Optical Fiber Sensors, OFS 2018 ; Conference date: 24-09-2018 Through 28-09-2018",

}

Denoising and robust temperature extraction for botda systems based on denoising autoencoder and DNN. / Wang, Biwei; Guo, Nan; Wang, Liang; Yu, Changyuan ; Lu, Chao.

Optical Fiber Sensors, OFS 2018. OSA - The Optical Society, 2018. (Optics InfoBase Conference Papers; Vol. Part F124-OFS 2018).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Denoising and robust temperature extraction for botda systems based on denoising autoencoder and DNN

AU - Wang, Biwei

AU - Guo, Nan

AU - Wang, Liang

AU - Yu, Changyuan

AU - Lu, Chao

PY - 2018/9/24

Y1 - 2018/9/24

N2 - Denoising autoencoder is used for denoising of the data obtained by the Brillouin optical time-domain analyzer (BOTDA) sensing system and is also used to form the deep neural networks (DNN) for robust temperature information extraction.

AB - Denoising autoencoder is used for denoising of the data obtained by the Brillouin optical time-domain analyzer (BOTDA) sensing system and is also used to form the deep neural networks (DNN) for robust temperature information extraction.

UR - http://www.scopus.com/inward/record.url?scp=85059467556&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

AN - SCOPUS:85059467556

SN - 9781943580507

T3 - Optics InfoBase Conference Papers

BT - Optical Fiber Sensors, OFS 2018

PB - OSA - The Optical Society

T2 - Optical Fiber Sensors, OFS 2018

Y2 - 24 September 2018 through 28 September 2018

ER -

Denoising and robust temperature extraction for botda systems based on denoising autoencoder and DNN

Abstract

Publication series

Conference

ASJC Scopus subject areas

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Cite this