• Bulletin of the Korean Chemical Society
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• v.27 no.6
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• pp.863-868
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• 2006

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of sulfite and sulfide is described. This method is based on the difference between the rate of the reaction of sulfide and sulfite with Malachite Green in pH 7.0 buffer solution and at 25 ${^{\circ}C}$. The absorption kinetic profiles of the solutions were monitored by measuring the decrease in the absorbance of Malachite Green at 617 nm in the time range 10-180 s after initiation of the reactions with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 24 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.030-1.5 and 0.030-1.2 $\mu$g m$L ^{-1}$ for sulfite and sulfide, respectively. The proposed method was successfully applied to simultaneous determination of sulfite and sulfide in water samples and whole human blood.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.879-886
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• 2009

The purpose of this article is to present the application of Least Squares Support Vector Machine in analyzing the existing structure of brand. We estimate the parameters of the Market Share Attraction Model using a non-parametric technique for function estimation called Least Squares Support Vector Machine, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. Estimation by Least Squares Support Vector Machine technique makes it a good candidate for solving the Market Share Attraction Model. To illustrate the performance of the proposed method, we use the car sales data in South Korea's car market.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.4
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• pp.675-688
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• 2014

In this paper, we present an efficient way to determine a suitable value of the regularization parameter using the global generalized cross validation and analyze the experimental results from preconditioned global conjugate gradient linear least squares(Gl-CGLS) method in solving image deblurring problems. Preconditioned Gl-CGLS solves general linear systems with multiple right-hand sides. It has been shown in [10] that this method can be effectively applied to image deblurring problems. The regularization parameter, chosen from the global generalized cross validation, with preconditioned Gl-CGLS method can give better reconstructions of the true image than other parameters considered in this study.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.831-839
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• 2010

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

• Journal of the Chungcheong Mathematical Society
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• v.29 no.1
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• pp.59-71
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• 2016

To obtain more accurate approximation of the true images in the deblurring problems, the weighted global generalized cross validation(GCV) function to the inverse problem with multiple right-hand sides is suggested as an efficient way to determine the regularization parameter. We analyze the experimental results for many test problems and was able to obtain the globally useful range of the weight when the preconditioned global conjugate gradient linear least squares(Gl-CGLS) method with the weighted global GCV function is applied.

• Communications of the Korean Mathematical Society
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• v.11 no.4
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• pp.1147-1157
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• 1996

Nonlinear ill-posed problems arise in many application including parameter estimation and inverse scattering. We introduce a least squares regularization method to solve nonlinear ill-posed problems with constraints robustly and efficiently. The regularization method uses Trust-Region approach to handle the constraints on variables. The Generalized Cross Validation is used to choose the regularization parameter in computational tests. Numerical results are given to exhibit faster convergence of the method over other methods.

• Journal of the Korean Wood Science and Technology
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• v.45 no.4
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• pp.399-408
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• 2017

Research on the rapid and accurate prediction of physical properties of wood using near-infrared (NIR) spectroscopy has attracted recent attention. In this study, partial least squares analysis was performed between NIR spectra and air-dried specific gravity of five domestic conifer species including larch (Larix kaempferi), Korean pine (Pinus koraiensis), red pine (Pinus densiflora), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa). Fifty different lumbers per species were purchased from the five National Forestry Cooperative Federations of Korea. The air-dried specific gravity of 100 knot- and defect-free specimens of each species was determined by NIR spectroscopy in the range of 680-2500 nm. Spectral data preprocessing including standard normal variate, detrend and forward first derivative (gap size = 8, smoothing = 8) were applied to all the NIR spectra of the specimens. Partial least squares analysis including cross-validation (five groups) was performed with the air-dried specific gravity and NIR spectra. When the performance of the regression model was expressed as $R^2$ (coefficient of determination) and root mean square error of calibration (RMSEC), $R^2$ and RMSEC were 0.63 and 0.027 for larch, 0.68 and 0.033 for Korean pine, 0.62 and 0.033 for red pine, 0.76 and 0.022 for cedar, and 0.79 and 0.027 for cypress, respectively. For the calibration model, which contained all species in this study, the $R^2$ was 0.75 and the RMSEC was 0.37.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1511-1511
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• 2001

Energy content, expressed as calories per gram, is an important part of the evaluation and marketing of foods in developed countries. Currently accepted methods of measurement of energy by U.S. food labeling legislation include measurement of gross calories by bomb calorimetry with an adjustment for undigested protein and by calculation using specific factors for the energy values of protein, carbohydrate less the amount of insoluble dietary fiber, and total fat. The ability of NIRS to predict the energy value of diverse, processed and unprocessed cereal food products was investigated. NIR spectra of cereal products were obtained with an NIR Systems monochromator and the wavelength range used for analysis was 1104-2494 nm. Gross energy of the foods was measured by oxygen bomb calorimetry (Parr Manual No. 120) and expressed as calories per gram (CPGI, range 4.05-5.49 cal/g). Energy value was adjusted for undigested protein (CPG2, range 3.99-5.38 cal/g) and undigested protein and insoluble dietary fiber (CPG3, range 2.42-5.35 cal/g). Using a multivariate analysis software package (ISI International, Inc.) partial least squares models were developed for the prediction of energy content. The standard error of cross validation and multiple coefficient of determination for CPGI using modified partial least squares regression (n=127) was 0.060 cal/g and 0.95, respectively, and the standard error of performance, coefficient of determination, bias and slope using an independent validation set (n=59) were 0.057 cal/g, 0.98, -0.027 cal/g and 1.05 respectively. The PLS loading for factor 1 (Pearson correlation coefficient 0.92) had significant absorption peaks correlated to C-H stretch groups in lipid at 1722/1764 nm and 2304/2346 nm and O-H groups in carbohydrate at 1434 and 2076 nm. Thus the model appeared to be predominantly influenced by lipid and carbohydrate. Models for CPG2 and CPG3 showed similar trends with standard errors of performance, using the independent validation set, of 0.058 and 0.088 cal/g, respectively, and coefficients of determination of 0.96. Thus NIRS provides a rapid and efficient method of predicting energy content of diverse cereal foods.

• Journal of the Korean Institute of Gas
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• v.16 no.1
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• pp.26-32
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• 2012

The autoignition temperature is one of the most important physical properties used to determine the flammability characteristics of chemical substances. Despite the needs of the experimental autoignition temperature data for the design of chemical plants, it is not easy to get the data. This study have built and compared partial least squares (PLS) and support vector machine (SVM) models to predict the autoignition temperatures of 503 organic compounds out of DIPPR 801. As the independent variables of the models, 59 functional groups were chosen based on the group contribution method. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, particle swarm optimization was used to get three parameters of SVM model. The PLS and SVM results of the average absolute errors for the whole data range from 58.59K and 29.11K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.