Application of Distributed Estimation Algorithm in Wavelength Demodulation of Overlapping Spectra of Fiber Bragg Grating Sensor Networks

Zhou, Qing-Xu; Jiang, Hao; Lin, Yating; Chen, Jing

doi:10.23919/chicc.2019.8866493

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…This section evaluates and compares the performance of the proposed CNN-AE model with existing methods from the literature through simulations. The comparison has been carried with many evolutionary algorithms [8], [19]- [22], and classical machine learning approaches [9], [23] reported in the literature; however, for the sake of clarity and conciseness, here we only present a comparison with the approaches having the best performance, namely SDE [8] and LS-SVR [9], hereafter referred to as baselines.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/JSEN.2024.3400819 (SDE) [8], a binary genetic algorithm [19], simulated annealing [20], dynamic multi-swarm particle swarm optimization [21], and distributed estimation algorithm [22].…”

Section: B Spectrally-overlapped Wavelength Division Multiplexingmentioning

confidence: 99%

“…On the contrary, simulating a serial topology is more complex due to the occurrence of multiple reflections between pairs of FBGs. A simple approach involves considering only the primary reflections while neglecting higher-order reflections [22]. A more precise and rigorous simulation uses a transfer-matrix formalism [8], [29], utilizing a transfer matrix to model the amplitude of reflected and transmitted light in each FBG sensor.…”

Section: B Joint Spectrummentioning

confidence: 99%

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Multipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder

Rudloff,

Soto

2024

IEEE Sensors J.

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This paper presents a machine learning solution to identify the peak wavelengths of fiber Bragg grating (FBG) sensors multiplexed in a network with high spectral overlapping. Machine learning solutions generally require high-quality, extensive datasets, which can be difficult to obtain. In contrast, the proposed model corresponds to a convolutional neural network autoencoder that can be trained in an unsupervised manner using only FBG spectrum data, without the need for information about the spectral positions of the sensors. The model is specifically designed to encode the spectral positions of overlapping FBG sensors as Dirac deltas, facilitating the straightforward extraction of spectral positions by computing their center of mass. The effectiveness and precision of the model are validated using both simulation and experimental data of a two-sensor serial array. In simulations, the model demonstrates promising adaptation capability, outperforming methods reported in the literature by over an order of magnitude in terms of mean absolute error. Meanwhile, when evaluated with experimental sensor data, the proposed autoencoder matches the performance of one of the most effective existing methods, but employing a much more efficient computing approach, thereby offering the potential for real-time inference of the spectral position of highly-overlapping FBG sensors.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: B Spectrally-overlapped Wavelength Division Multiplexingmentioning

confidence: 99%

Section: B Joint Spectrummentioning

confidence: 99%

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Multipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder

Rudloff,

Soto

2024

IEEE Sensors J.

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“…Several Bragg wavelength detection methods have been proposed in the introduction part, such as the differential evolution DE method [ 21 , 22 ], a tree-search dynamic multi-swarm particle swarm algorithm (TS-DMS-PSO) [ 23 ], particle swarm optimization-based simulated annealing (PSO-SA) [ 24 ], a search tree-based Least Square Support Regression [ 26 ], Genetic Algorithm, GA [ 20 ], distributed estimation algorithm (EDA) [ 25 ], and ELM [ 11 ]. Thus, in this section, we compare and contrast the performance of our proposed GRU model with previously proposed algorithms and machine learning techniques, as shown in Table 5 .…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, accurate determination of the FBG Bragg wavelength from the overlapping spectra is the primary task of the FBG sensor network. Recently, to boost the accuracy of Bragg wavelength measurements, several evolutionary algorithms such as genetic algorithms (GAs) [ 20 ], differential evolution (DE) algorithms [ 21 , 22 ], particle swarm (PSO) algorithms [ 23 , 24 ], and distributed estimation algorithms (EDA) [ 25 ] have been proposed. However, as the number of sensors increases, evolutionary algorithms typically need longer processing time to achieve better accuracy and have higher detection errors, which affect real-time monitoring of the Bragg wavelength measurement.…”

Section: Introductionmentioning

confidence: 99%

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

Dehnaw

Manie

Chen

et al. 2020

Sensors

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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.

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Highly sensitive and stable probe refractometer based on configurable plasmonic resonance with nano-modified fiber core

Jing¹,

Liu²,

Jiang³

et al. 2023

OEA

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A dispersion model is developed to provide a generic tool for configuring plasmonic resonance spectral characteristics. The customized design of the resonance curve aiming at specific detection requirements can be achieved. According to the model, a probe-type nano-modified fiber optic configurable plasmonic resonance (NMF-CPR) sensor with tip hot spot enhancement is demonstrated for the measurement of the refractive index in the range of 1.3332-1.3432 corresponding to the low-concentration biomarker solution. The new-type sensing structure avoids excessive broadening and redshift of the resonance dip, which provides more possibilities for the surface modification of other functional nanomaterials. The tip hot spots in nanogaps between the Au layer and Au nanostars (AuNSs), the tip electric field enhancement of AuNSs, and the high carrier mobility of the WSe 2 layer synergistically and significantly enhance the sensitivity of the sensor. Experimental results show that the sensitivity and the figure of merit of the tip hot spot enhanced fiber NMF-CPR sensor can achieve up to 2995.70 nm/RIU and 25.04 RIU −1 , respectively, which are 1.68 times and 1.29 times higher than those of the conventional fiber plasmonic resonance sensor. The results achieve good agreements with numerical simulations, demonstrate a better level compared to similar reported studies, and verify the correctness of the dispersion model. The detection resolution of the sensor reaches up to 2.00×10 −5 RIU, which is obviously higher than that of the conventional side-polished fiber plasmonic resonance sensor. This indicates a high detection accuracy of the sensor. The dense Au layer effectively prevents the intermediate nanomaterials from shedding and chemical degradation, which enables the sensor with high stability. Furthermore, the terminal reflective sensing structure can be used as a practical probe and can allow a more convenient operation.

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Application of Distributed Estimation Algorithm in Wavelength Demodulation of Overlapping Spectra of Fiber Bragg Grating Sensor Networks

Cited by 4 publications

References 9 publications

Multipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder

Multipeak Wavelength Detection of Spectrally Overlapped Fiber Bragg Grating Sensors Through a CNN-Based Autoencoder

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

Highly sensitive and stable probe refractometer based on configurable plasmonic resonance with nano-modified fiber core

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