Improved spectral tag method for FBG sensor multiplexing with equally spaced spectral codes and simulated annealing algorithm

Choi, Kwang‐Seong; Youn, Jaesoon; You, Eunmi; Yoon, Jiwon; Kim, Gyoung-A; Baik, Se-Jong; Im, Kiegon; Kim, K.; Jeong, Sung-Hyeon

doi:10.1109/icsens.2009.5398380

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…In FBG sensor system, a self-healing structure is a good solution to increase the reliability and survivability issue of the sensor network [13]. Furthermore, one of the most important benefits of FBG sensors is their multiplexing and wavelength encoding capability [14][15]. In a quasi-distributed sensor system, several FBG sensors can be multiplexed in a single cable using different multiplexing techniques.…”

Section: Introductionmentioning

confidence: 99%

Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

et al. 2021

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This paper proposes a new fiber Bragg grating central wavelength interrogation system by combining evolutionary algorithm and machine learning techniques integrated with an unsupervised autoencoder (AE) pre-training strategy. The proposed unsupervised AE pre-training convolution neural network (CNN) allows training of the convolutional layers independently from a regression task in order to learn a new data representation and give better generalization. It is also used to improve the system accuracy by four times without extra-labeled data. Moreover, AE is combined with a differential evolutionary (DE) algorithm to automate the human labeling task. The proposed autoencoder pre-training convolution neural network and differential evolutionary (AECNNDE) interrogation system achieve good accuracy and can speed-up the computational time by a maximum of 30 times than DE algorithm.

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Section: Introductionmentioning

confidence: 99%

Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

et al. 2021

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“…Multiplexing of FBG sensors is an effective technology to deliver multipoint measurement along a single-fiber cable. Multiplexing reduces the operation and installation costs for quasidistributive strain measurements [7]. Among the various multiplexing techniques, wavelength division multiplexing (WDM) is an essential mechanism to recognize the exact central wavelength of each FBG sensor within the sensor network [7,8].…”

Section: Introductionmentioning

confidence: 99%

Intensity and Wavelength Division Multiplexing FBG Sensor System Using a Raman Amplifier and Extreme Learning Machine

et al. 2018

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A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped.

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Improved spectral tag method for FBG sensor multiplexing with equally spaced spectral codes and simulated annealing algorithm

Cited by 2 publications

References 8 publications

Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

Intensity and Wavelength-Division Multiplexing Fiber Sensor Interrogation Using a Combination of Autoencoder Pre-Trained Convolution Neural Network and Differential Evolution Algorithm

Intensity and Wavelength Division Multiplexing FBG Sensor System Using a Raman Amplifier and Extreme Learning Machine

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