Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor

Nordin, Nur Dalilla; Zan, Mohd Saiful Dzulkefly; Abdullah, Fairuz

doi:10.1016/j.yofte.2020.102298

Cited by 16 publications

(7 citation statements)

References 26 publications

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“…For an application requiring fast analysis, GLM may be the best choice, since it is the quickest model to generate predicted outcome. Additionally, we had previously demonstrated that GLM has better prediction accuracy than LCF method even at larger frequency scanning step and lower SNR [34]. At the same time, RF and SVM may be suitable for a system requiring higher measurement precision and prediction accuracy, but it can tolerate slightly longer processing times.…”

Section: Resultsmentioning

confidence: 99%

“…A similar method can also be employed for temperature and strain prediction from BFS in BOTDA technique, such as combining artificial neural network (ANN) and principal component analysis (PCA) algorithms for temperature extraction [22,23], ANN and PCA for strain and temperature discrimination [24][25][26][27], deep learning (DL) [28][29][30][31], convolutional neural network [32], and support vector machine (SVM) [33] for accuracy improvement. We have also previously demonstrated the use of generalized linear model (GLM) in data processing for BOTDA [34].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

2020

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This paper demonstrates a comparative analysis of five machine learning (ML) algorithms for improving the signal processing time and temperature prediction accuracy in Brillouin optical time domain analysis (BOTDA) fiber sensor. The algorithms analyzed were generalized linear model (GLM), deep learning (DL), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM). In this proof-of-concept experiment, the performance of each algorithm was investigated by pairing Brillouin gain spectrum (BGS) with its corresponding temperature reading in the training dataset. It was found that all of the ML algorithms have significantly reduced the signal processing time to be between 3.5 and 655 times faster than the conventional Lorentzian curve fitting (LCF) method. Furthermore, the temperature prediction accuracy and temperature measurement precision made by some algorithms were comparable, and some were even better than the conventional LCF method. The results obtained from the experiments would provide some general idea in deploying ML algorithm for characterizing the Brillouin-based fiber sensor signals.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

2020

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“…GLM is an extension of the general linear model in machine learning that could compute response based on the maximum likelihood of the training set of data [ 44 , 45 ]. It allows Lorentzian or Gaussian, Poisson, normal and a few other distribution-like data to be processed through GLM.…”

Section: Generalize Liner Model (Glm) Theorymentioning

confidence: 99%

Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring

Nordin

Abdullah

Zan

et al. 2022

Sensors

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In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 με); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 με). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.

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“…Generalized linear model (GLM), an extension of the predictable linear regression model, was formulated by Nelder and Wedderburn (1972) to produce answers based on the Maximum Likelihood (ML) of the training variables. The GLM allows the dataset to be overfitted by exponential distribution (normal, binomial, or gamma distribution) (Nordin et al 2020). Regression methods, including linear, logistic, and log-linear regression, have been widely used to obtain the best model to illustrate the communication between a dependent parameter and multiple independent parameters (Ozdemir and Altural 2013;Karimidastenaei et al 2020).…”

Section: Generalized Linear Model (Glm)mentioning

confidence: 99%

“…The mean of the distribution (μ) depends on the independent variables (X). In the GLM, the linear predictor is given as (Nordin et al 2020):…”

Section: Generalized Linear Model (Glm)mentioning

confidence: 99%

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

et al. 2020

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Land degradation (LD) is a complex process affected by both anthropogenic and natural driving variables, and its prevention has become an essential task globally. The aim of the present study was to develop a new quantitative LD mapping approach using machine learning techniques, benchmark models, and human-induced and socio-environmental variables. We employed four machine learning algorithms [Support Vector Machine (SVM), Multivariate Adaptive Regression Splines (MARS), Generalized Linear Model (GLM), and Dragonfly Algorithm (DA)] for LD risk mapping, based on topographic (n = 7), human-induced (n = 5), and geo-environmental (n = 6) variables, and field measurements of degradation in the Pole-Doab watershed, Iran. We assessed the performance of different algorithms using receiver operating characteristic, Kappa index, and Taylor diagram. The results revealed that the main topographic, geoenvironmental, and human-induced variable was slope, geology, and land use change, respectively. Assessments of model performance indicated that DA had the highest accuracy and efficiency, with the greatest learning and prediction power in LD risk mapping. In LD risk maps produced using SVM, GLM, MARS, and DA, 19.16%, 19.29%, 21.76%, and 22.40%, respectively, of total area in the Pole-Doab watershed had a very high degradation risk. The results of this study demonstrate that in LD risk mapping for a region, topographic, and geological factors (static conditions) and human activities (dynamic conditions, e.g., residential and industrial area expansion) should be considered together, for best protection at watershed scale. These findings can help policymakers prioritize land and water conservation efforts.

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Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor

Cited by 16 publications

References 26 publications

Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor

Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring

Land degradation risk mapping using topographic, human-induced, and geo-environmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran

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