Machine Learning Implementation for Unambiguous Refractive Index Measurement Using a Self-Referenced Fiber Refractometer

Martínez-Manuel, Rodolfo; Valentín-Coronado, Luis M.; Esquivel-Hernández, Jonathan; Monga, Kaboko Jean-Jacques; LaRochelle, Sophie

doi:10.1109/jsen.2022.3183475

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…Because of the EFPI sensor systems’ great sensitivity, the recorded time-transient signal for a droplet evaporation event provides extensive information, such as the initial droplet size and the evaporation rate, implicitly. Alternatively, these informative time-domain signals can also be used with conventional machine learning models like random forests, 42 support vector machines, 43 fully connected NNs, 44 and so forth. For most such approaches to be viable, feature engineering and extraction must be performed first.…”

Section: Methodsmentioning

confidence: 99%

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

et al. 2023

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In this paper, we report an array of fiber-optic sensors based on the Fabry–Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events.

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Section: Methodsmentioning

confidence: 99%

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

et al. 2023

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“…In this sense, in order to expand the measurement range or to counteract the cross-sensitivity, other techniques different to the sensitive matrix equation have been reported. For example, the empirical mode decomposition algorithm [8], the method of intensity-modulated [9], and the use of neural network algorithms [10,11] have been reported. In these last works, the mathematical models are based on different features of the output signal of the optical sensor utilized to predict the correct value of the measured parameter.…”

Section: Introductionmentioning

confidence: 99%

Application of a Multiple Regression Model for the Simultaneous Measurement of Refractive Index and Temperature Based on an Interferometric Optical System

Guzmán-Chávez

Rodríguez

2023

Journal of Sensors

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Interferometric optical systems have been proposed for implementing dual-parameter optical sensors. For this type of sensors, the sensitivity matrix equation is generally used to determine the parameters to be measured based on the sensitivity of each parameter to one particular feature of the output reflective spectrum of the interferometric system. One of the disadvantages of this method is that the measurement ranges will be very short if the sensitivities are not linear or if these present cross-sensitivity. In this work, a multiple regression model for the simultaneous detection of refractive index and temperature based on an interferometric optical sensor is proposed. Here, the mathematical model is a weighted sum of features used to estimate the values of two response variables. These features are functions of an initial set of 27 explanatory variables whose values were extracted of the output reflective spectrum of the interferometric system. Besides, in order to sustenance the model application, the sensor was modeled and experimentally carried out. Three cases were studied: the estimation of temperature at different refractive indices, the estimation of temperature when refractive index is equal to one, and the estimation of refractive index at different temperatures. For each one of these cases, an optimal basis of functions was founded with the algorithm proposed and used to estimate the values of the response variables. Besides, a technique to reduce the initial set of variables was implemented. Finally, for the experimental data, For each one of these cases, an optimal basis of functions was founded with the algorithm 1 proposed and used to estimate the values of the response variables.

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Application of machine learning in optical fiber sensors

Zhou,

Zhang,

et al. 2024

Measurement

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Machine Learning Implementation for Unambiguous Refractive Index Measurement Using a Self-Referenced Fiber Refractometer

Cited by 13 publications

References 36 publications

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

Application of a Multiple Regression Model for the Simultaneous Measurement of Refractive Index and Temperature Based on an Interferometric Optical System

Application of machine learning in optical fiber sensors

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