Evidence map›Paper›PMID 33371509›Full record

ArticleSensors (Basel, Switzerland)2020

Design Reliable Bus Structure Distributed Fiber Bragg Grating Sensor Network Using Gated Recurrent Unit Network.

Amare Mulatie Dehnaw, Yibeltal Chanie Manie, Ya Yu Chen, Po Han Chiu, Hung Wei Huang, Guan Wei Chen, Peng Chun Peng

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In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Amare Mulatie DehnawDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Yibeltal Chanie ManieDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Ya Yu ChenDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Po Han ChiuDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Hung Wei HuangDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Guan Wei ChenDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Peng Chun PengDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The focus of this paper was designing and demonstrating bus structure FBG sensor networks using intensity wavelength division multiplexing (IWDM) techniques and a gated recurrent unit (GRU) algorithm to increase the capability of multiplexing and the ability to detect Bragg wavelengths with greater accuracy. Several Fiber Bragg grating (FBG) sensors are coupled with power ratios of 90:10 and 80:10, respectively in the suggested experimental setup. We used the latest IWDM multiplexing technique for the proposed scheme, as the IWDM system increases the number of sensors and allows us to alleviate the limited operational region drawback of conventional wavelength division multiplexing (WDM). However, IWDM has a crosstalk problem that causes high-sensor signal measurement errors. Thus, we proposed the GRU model to overcome this crosstalk or overlapping problem by converting the spectral detection problem into a regression problem and considered the sequence of spectral features as input. By feeding this sequential spectrum dataset into the GRU model, we trained the GRU system until we achieved optimal efficiency. Consequently, the well-trained GRU model quickly and accurately identifies the Bragg wavelength of each FBG from the overlapping spectra. The Bragg wavelength detection performance of our proposed GRU model is tested or validated using different numbers of FBG sensors, such as 3-FBG, 5-FBG, 7-FBG, and 10-FBG, separately. As a result, the experiment result proves that the well-trained GRU model accurately identifies each FBG Bragg wavelength, and even the number of FBG sensors increase, as well as the spectra of FBGs, which are partially or fully overlapped. Therefore, to boost the detection efficiency, reliability, and to increase the multiplexing capabilities of FBG sensor networks, the proposed sensor system is better than the other previously proposed methods.

Indexed as

Fiber Bragg Gratinggated recurrent unit networkIntensity Wavelength Division Multiplexing

Identifiers

PMID33371509
PMCPMC7767492

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.