Stationary wavelet singular entropy based electronic tongue for classification of milk

Kaushal, Pauroosh; Mudhalwadkar, Rohini

doi:10.1177/0142331219893895

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…The main mentality is to transform the wavelet coefficient matrix of the denoize data into a series of probability distributions. With decomposing the sequence of distributions into a set of singular values, the basic feature can be then reflected through the singular value decomposition [ 29 ]. The uncertainty of the singular values is computed by the information entropy algorithm [ 7 ] for the discrete sequence of the system.…”

Section: Identification Strategy On the Aerodynamic System Instabilitymentioning

confidence: 99%

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

et al. 2023

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The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

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Section: Identification Strategy On the Aerodynamic System Instabilitymentioning

confidence: 99%

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

et al. 2023

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“…According to the singular value decomposition theorem [25], for any matrix W with n × m order where the elements belong to the real or complex field, there must exist a kind of decomposition so that the matrix can be decomposed into the formula [26] as:…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability

et al. 2022

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In order to effectively identify the signs of instability in the aerodynamic system of an axial compressor, a wavelet singular spectral entropy algorithm incorporated within the wavelet transform, singular value decomposition and information entropy is proposed to describe the distribution complexity of the spatial modalities in the flow field. This kind of identification design can accurately distinguish the boundary between the stable and unstable states of the internal flow field from the view of a dynamic system. On the basis of the information entropy algorithm, the wavelet singular spectral entropy algorithm is designed to integrate with the advantages of wavelet transform analysis on the time-frequency localization and singular value decomposition for signal processing and data mining together. So that the quantitative analysis of the definition of rebuilding a system image can be achieved by the solution of wavelet singular spectral entropy. This method can automatically extract the transient information of the space mode in the time-frequency domain. It effectively avoids the shortcoming that the feature extraction on spatial information cannot be accomplished from multiple angles with the single information entropy algorithm. In the data processing of instability signals under different speeds, the wavelet singular spectral entropy algorithm shows a greater advantage in the early warning for compressor stall. The result shows that the value of the wavelet singular spectral shows an obvious mutation when the aerodynamic system approaches the instability boundary. According to the threshold set, the identification hybrid algorithm can detect the stall precursor about 23~96 r in advance. Compared to the single information entropy algorithm, the hybrid wavelet singular spectral entropy algorithm is able to shift to an earlier precursor identification by about 11~82 r. This established hybrid identification algorithm accounts for the nonlinearity of the aerodynamic system, providing a new perspective for the nonlinear system instability identification.

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“…Additionally, electronic tongue (e-tongue) is a concept created for the electronic detection of tastes. Today, this concept is used more generally and means the identification and detection in the presence of liquid (Kaushal & Mudhalwadkar, 2020). In e-tongue, an array of sensors converts the chemical compounds of an unknown solution into an electrical signal.…”

mentioning

confidence: 99%

Application of a voltammetric electronic tongue combined with chemometric approaches for the early classification of heavy metals in sunflower oil

Kiani

Beheshti

Borghei

et al. 2021

J. Food Process. Preserv.

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Electronic simulation of the five senses has received attention for many years. Additionally, electronic tongue (e-tongue) is a concept created for the electronic detection of tastes. Today, this concept is used more generally and means the identification and detection in the presence of liquid (Kaushal & Mudhalwadkar, 2020). In e-tongue, an array of sensors converts the chemical compounds of an unknown solution into an electrical signal. The responses are transferred to the processing unit. In the processing unit, the information hidden in the responses is drawn out using pattern recognition methods and used to analyze the analyte. The array responses consisting of

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Stationary wavelet singular entropy based electronic tongue for classification of milk

Cited by 11 publications

References 37 publications

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

Aerodynamic System Machine Learning Modeling with Gray Wolf Optimization Support Vector Regression and Instability Identification Strategy of Wavelet Singular Spectrum

Identification Strategy Design with the Solution of Wavelet Singular Spectral Entropy Algorithm for the Aerodynamic System Instability

Application of a voltammetric electronic tongue combined with chemometric approaches for the early classification of heavy metals in sunflower oil

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