The Experts below are selected from a list of 340038 Experts worldwide ranked by ideXlab platform
Shouxin Ren - One of the best experts on this subject based on the ideXlab platform.
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ICNC - Simultaneous multicomponent polycyclic aromatic hydrocarbon analysis using an independent component analysis-based latent Variable Regression
2012 8th International Conference on Natural Computation, 2012Co-Authors: Shouxin Ren, Ling GaoAbstract:This article developed an independent component analysis-based latent Variable Regression (ICA-LVR) method, which is based on latent Variable Regression combined with independent component analysis. This strategy has been applied to the resolution of mixtures of four polycyclic aromatic hydrocarbons. Independent component analysis is a novel statistical signal processing technique based on the fourth-order moment of the signals aiming at solving related blind source separation (BSS) problem. Independent source Variables and their corresponding concentration profiles can be extracted from the observed spectra of chemical mixtures. The independent source matrix instead of the original observed spectra combined with concentration matrix was used to build the Regression model by latent Variable Regression (LVR). The method can obtain very selective information from unselective full-spectrum data. Experimental results showed the ICA-LVR method to be successful even where there was severe overlap of spectra and had the clear superiority over the LSV method.
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Simultaneous multicomponent analysis of overlapping spectrophotometric signals using a wavelet-based latent Variable Regression.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2008Co-Authors: Ling Gao, Shouxin RenAbstract:A wavelet-based latent Variable Regression (WLVR) method was developed to perform simultaneous quantitative analysis of overlapping spectrophotometric signals. The quality of the noise removal was improved by combining wavelet thresholding with principal component analysis (PCA). A method for selecting the optimum threshold was also developed. Eight error functions were calculated for deducing the number of factor. The latent Variables were made by projecting the wavelet-processed signals onto orthogonal basis eigenvectors. Two-programs WMRA and WLVR, were designed to perform wavelet thresholding and simultaneous multicomponent determination. Experimental results showed the WLVR method to be successful even where there was severe overlap of spectra.
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Simultaneous determination of Sm( III) and Y(III) by spectrophotometry with a wavelet packet transform latent Variable Regression
Guang pu xue yu guang pu fen xi = Guang pu, 2007Co-Authors: Ling Gao, Shouxin RenAbstract:A wavelet packet transform latent Variable Regression (WPLVR) method was developed to perform simultaneous quantitative analysis of Sm(III) and Y(III). The quality of the noise removal was improved by combining wavelet packet transform with latent Variable Regression (VLR). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet domain, the quality of the noise removal can be improved. The latent Variables were made by projecting the wavelet packet processed signals onto orthogonal basis eigenvectors. The latent Variable is expressible in term of linear combination of the original signals. By this method one can obtain highly selective information from unselective full-spectrum data. Through optimization, the wavelet function and wavelet packet decomposition levels (L) were selected. Two programs, PWPLVR and PFTLVR, were designed to perform WPLVR and Fourier transform latent Variable Regression (FTLVR) calculations. Experimental results showed that both methods were successful, but the WPLVR methed was better than FTLVR.
Ling Gao - One of the best experts on this subject based on the ideXlab platform.
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ICNC - Simultaneous multicomponent polycyclic aromatic hydrocarbon analysis using an independent component analysis-based latent Variable Regression
2012 8th International Conference on Natural Computation, 2012Co-Authors: Shouxin Ren, Ling GaoAbstract:This article developed an independent component analysis-based latent Variable Regression (ICA-LVR) method, which is based on latent Variable Regression combined with independent component analysis. This strategy has been applied to the resolution of mixtures of four polycyclic aromatic hydrocarbons. Independent component analysis is a novel statistical signal processing technique based on the fourth-order moment of the signals aiming at solving related blind source separation (BSS) problem. Independent source Variables and their corresponding concentration profiles can be extracted from the observed spectra of chemical mixtures. The independent source matrix instead of the original observed spectra combined with concentration matrix was used to build the Regression model by latent Variable Regression (LVR). The method can obtain very selective information from unselective full-spectrum data. Experimental results showed the ICA-LVR method to be successful even where there was severe overlap of spectra and had the clear superiority over the LSV method.
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Simultaneous multicomponent analysis of overlapping spectrophotometric signals using a wavelet-based latent Variable Regression.
Spectrochimica acta. Part A Molecular and biomolecular spectroscopy, 2008Co-Authors: Ling Gao, Shouxin RenAbstract:A wavelet-based latent Variable Regression (WLVR) method was developed to perform simultaneous quantitative analysis of overlapping spectrophotometric signals. The quality of the noise removal was improved by combining wavelet thresholding with principal component analysis (PCA). A method for selecting the optimum threshold was also developed. Eight error functions were calculated for deducing the number of factor. The latent Variables were made by projecting the wavelet-processed signals onto orthogonal basis eigenvectors. Two-programs WMRA and WLVR, were designed to perform wavelet thresholding and simultaneous multicomponent determination. Experimental results showed the WLVR method to be successful even where there was severe overlap of spectra.
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Simultaneous determination of Sm( III) and Y(III) by spectrophotometry with a wavelet packet transform latent Variable Regression
Guang pu xue yu guang pu fen xi = Guang pu, 2007Co-Authors: Ling Gao, Shouxin RenAbstract:A wavelet packet transform latent Variable Regression (WPLVR) method was developed to perform simultaneous quantitative analysis of Sm(III) and Y(III). The quality of the noise removal was improved by combining wavelet packet transform with latent Variable Regression (VLR). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet domain, the quality of the noise removal can be improved. The latent Variables were made by projecting the wavelet packet processed signals onto orthogonal basis eigenvectors. The latent Variable is expressible in term of linear combination of the original signals. By this method one can obtain highly selective information from unselective full-spectrum data. Through optimization, the wavelet function and wavelet packet decomposition levels (L) were selected. Two programs, PWPLVR and PFTLVR, were designed to perform WPLVR and Fourier transform latent Variable Regression (FTLVR) calculations. Experimental results showed that both methods were successful, but the WPLVR methed was better than FTLVR.
Qinqin Zhu - One of the best experts on this subject based on the ideXlab platform.
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Latent Variable Regression for supervised modeling and monitoring
IEEE CAA Journal of Automatica Sinica, 2020Co-Authors: Qinqin ZhuAbstract:A latent Variable Regression algorithm with a regularization term ( rLVR ) is proposed in this paper to extract latent relations between process data X and quality data Y. In rLVR, the prediction error between X and Y is minimized, which is proved to be equivalent to maximizing the projection of quality Variables in the latent space. The geometric properties and model relations of rLVR are analyzed, and the geometric and theoretical relations among rLVR, partial least squares, and canonical correlation analysis are also presented. The rLVR-based monitoring framework is developed to monitor process-relevant and quality-relevant variations simultaneously. The prediction and monitoring effectiveness of rLVR algorithm is demonstrated through both numerical simulations and the Tennessee Eastman ( TE ) process.
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Latent Variable Regression for Process and Quality Modeling
2019 1st International Conference on Industrial Artificial Intelligence (IAI), 2019Co-Authors: Qinqin Zhu, S. Joe QinAbstract:The supervised learning methods, partial least squares (PLS) and canonical correlation analysis (CCA), have been widely used in industrial processes to perform multivariate statistical modeling and monitoring based on process Variables and quality Variables. However, the latent Variables extracted by PLS may contain irrelevant components, while CCA focuses only on the correlation but ignores the variance information. To overcome their drawbacks, a latent Variable Regression (LVR) modeling method with regularization is proposed to retain the prediction efficiency of CCA while exploiting the quality variance structure. LVR minimizes the prediction error between input and output scores, and retains consistent objectives in inner and outer modeling. Synthetic case studies and the Tennessee Eastman process are used to demonstrate the effectiveness of the proposed algorithm.
Ren Shou-xin - One of the best experts on this subject based on the ideXlab platform.
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The Resolution of Overlapped UV Spectra by a Wavelet Packet Transform Latent Variable Regression
Journal of Analytical Science, 2004Co-Authors: Ren Shou-xinAbstract:A wavelet packet transform latent Variable Regression (WPLVR) method was developed to perform simultaneous quantitative analysis of biphynel, phenol, o-dihydroxybenzene. The quality of the noise removal was improved by combining wavelet packet transform with latent Variable Regression (LVR). Wavelet function and, wavelet packet decomposition levels (L) were optimized. Two-programs, PWPLVR and PFTLVR were designed to perform WPLVR and Fourier transform latent Variable Regression (FTLVR) calculations. Experimental results showed the WPLVR method was successful and better than FTLVR.
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Simultaneous Determination of Three-Component Mixtures Using Wavelet-Based Latent Variable Regression and Generalized Regression Neural Network
Journal of Analytical Science, 2002Co-Authors: Ren Shou-xinAbstract:In this paper, a wavelet-based latent Variable Regression (WLVR) method was applied to simultaneous quantitative analysis of overlapping spectrophotometric signals. The quality of the noise removal was improved by combining wavelet thresholding with principal component analysis (PCA). Eight error functions were calculated for deducing the number of factor. The latent Variables were made by projecting the wavelet-processed signals onto orthogonal basis eigenvectors. The generalized Regression neural network (GRNN) was applied to the simultaneous multicomponent determination. Three programs called PWMRA, PWLVR and PGRNN, which are based on arithmetic algorithms, were designed to perform relative calculations. The three methods (WLVR, latent Variable Regression (LVR) and GRNN) were applied to simultaneous three-components determination with satisfactory results. Experimental studies showed that the predictory capability for WLVR, LVR and GRNN was in the following sequence: WLVR, LVR, GRNN.
Le Yao - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear probabilistic latent Variable Regression models for soft sensor application: From shallow to deep structure
Control Engineering Practice, 2020Co-Authors: Bingbing Shen, Le YaoAbstract:Abstract Probabilistic latent Variable Regression models have recently caught much attention in the process industry, particularly for soft sensor applications. One of the main challenges for those models is how to effectively extract nonlinear features for latent Variable Regression. This paper proposes a nonlinear probabilistic latent Variable Regression (NPLVR) model based on the features extracted by variational auto-encoder. To extend the NPLVR model from shallow to deep structure, a hierarchical form of NPLVR model is proposed to extract deeper nonlinear information by stacking VAE. Under the same modeling framework, a semi-supervised version of hierarchical NPLVR model is further developed to handle the problem of scarce amount of labeled data samples, which is quite common in practical applications. Two industrial case studies are provided to demonstrate the effectiveness and superiority of the developed models.
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Similarity based robust probability latent Variable Regression model and its kernel extension for process monitoring
Chemometrics and Intelligent Laboratory Systems, 2017Co-Authors: Le Zhou, Junghui Chen, Le Yao, Zhihuan Song, Beiping HouAbstract:Abstract In most industries, process and quality measurements with outliers are often collected. The outliers would have negative influences on data-based modelling and process monitoring. In our previous work on probability latent Variable Regression (PLVR), the model is constructed under the assumption that the data quality of the process characteristics is good and the operation processes are linear. In this article, a robust PLVR (RPLVR) model is developed. Then it is extended to its nonlinear form, called robust probability kernel latent Variable Regression (RPKLVR). Both models can reduce the effects of outliers. RPLVR and RPKLVR are the weighted probability models. The similarity of each sample among all the collected data would be chosen as the weighting factor for each sample. Thus, the outliers for modelling are weakened. With the weighted training data, an expectation-maximization algorithm of training RPLVR and RPKLVR are derived. The corresponding statistics are also systematically constructed for the fault detection. Two case studies are presented to illustrate the effectiveness of the proposed methods.
