• Journal of Information Technology Services
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• v.10 no.2
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• pp.203-221
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• 2011

The purpose of this study is to investigate the applications of Structural Equation Modeling(SEM) into MIS area in recent years. Two methodologies, Lisrel and PLS, are adopted for the method comparison. A research model, based upon TAM(Technology Acceptance Model) is used for the analysis of the data set of a previous study. The research model includes six research variables that are composed of twenty-eight question items. 272 data are used for data analyses through Lisrel v.8.72 and Visual PLS v.1.04. This study shows the statistical results of Lisrel are the same to those of PLS. The contribution of this study can be suggested as the followings; (1) A theoretical comparison of two methodologies is shown, (2) A statistical analysis is done at a real-situated data set, and (3) Several implications are suggested.

• Journal of the Ergonomics Society of Korea
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• v.20 no.1
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• pp.45-62
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• 2001

This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

• Information Systems Review
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• v.16 no.3
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• pp.89-112
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• 2014

This tutorial presents an approach to perform the PLS structural equation modeling using the R. For this purpose, the practical guide defines the criteria for the PLS structural equation modeling by reviewing previous studies, and shows how to analyze the research model with an example using the "plspm" which is the R package for the performing PLS path analysis against the criteria. This practical guide will be useful for the study of the PLS model analysis for new researchers and will provide the knowledge base for in-depth analysis through the new PLS structural equation modeling technique using R which is the integrated statistical software operating environment for the researchers familiar with the PLS structural equation modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.706-712
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• 2006

Recursive Partial Least Squares(RPLS) method has been used for processing the on-line available multivariate chemical process data and modeling adaptive prediction model for process changes. However, RPLS method is unstable in PLS model updating because RPLS method updates PLS model by merging past PLS model and new data. In this study, Adaptive Predictive Partial Least Squres(APPLS) method is suggested for more sensitive adaptation to process changes. By expanding APPLS method, block-wise Adaptive Predictive Partial Least Squares(block-wise APPLS) method is suggested for a lager scale data of chemical processes. APPLS method has been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PTT), and block-wise APPLS method has been applied to predict the cetane number using NIR Diesel Spectra data. APPLS and block-wise APPLS methods show better prediction and updating performance than RPLS method.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.107-115
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• 2015

The social commerce is an innovative and emerging transaction. It is the result of combination with transaction and social media technology. This study analyzes Korean college students' social commerce behavior through extending technology acceptance model(TAM) with social influence(SI). We confirmed all proposed hypotheses are significant and supported by the given data through PLS path modeling method with SmartPLS. It indicates that SI is an important factor influencing intention to use, so SI should be consider for theorists to enhance explanation and prediction of TAM and for practitioners to earn higher performances as well. Finally, based on the findings, suggestions for future studies are discussed.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.117-124
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• 2015

As the acceptance of statistical analysis has been increased because of Big Data, the needs for an advanced second generation of statistical analysis method like Structural Equation Model are also increasing. This study suggests how R-Program, as open software, can be utilized when Partial Least Square Model, one of the SEMs, is applied to statistical analysis. R is a free software as a part of GNU projects as well as a powerful and useful tool for statistical analysis including Big Data. The study utilized R and SmartPLS, a representative statistical package of PLS-SEM, and analyzed internal consistency reliability, convergent validity, and discriminant validity of the measurement model. The study also analyzed path coefficients and moderator effects of the structural model and compared the results, respectively. The results indicated that R showed the same results with SmartPLS on the measurement model and the structural model. Therefore, the study confirmed that R could be a powerful tool that is alternative to a commercial statistical package in the future.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.560-568
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• 2000

The aims of this research were to investigate the preprocessing effect of spectrum data on prediction performance and to develop a robust model to predict SSC in intact apple. Spectrum data of 320 Fuji apples were measured with the on-line transmittance measurement system at the wavelength range of 550∼1100nm. Preprocess methods adopted for the tests were Savitzky Golay, MSC, SNV, first derivative and OSC. Several combinations of those methods were applied to the raw spectrum data set to investigate the relative effect of each method on the performance of the calibration model. PLS method was used to regress the preprocessed data set and the SSCs of samples, and the cross-validation was to select the optimal number of PLS factors. Smoothing and scattering corection were essential in increasing the prediction performance of PLS regression model and the OSC contributed to reduction of the number of PLS factors. The first derivative resulted in unfavorable effect on the prediction performance. MSC and SNV showed similar effect. A robust calibration model could be developed by the preprocessing combination of Savitzky Golay smoothing, MSC and OSC, which resulted in SEP= 0.507, bias=0.032 and R$^2$=0.8823.

• Journal of the Korean Society of Industry Convergence
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• v.12 no.1
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• pp.27-33
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• 2009

In this paper, a computational analysis using a lumped system model is performed to investigate the hemodynamics of coronary circulation under the operation of T-PLS relevant to the cardiac arrest cases. The coronary circulation system is assumed to be comprised of three compartments: coronary arteries, coronary capillaries, and coronary veins. The effect of myocardial muscle contraction or relaxation is represented by temporal variations in the bias pressure. To verify the present method, we analyzed the coronary circulation for normal case and then compared the results with the existing data. Numerical results on the cardiac arrest model showed that T-PLS can increase LAD flow significantly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.531-540
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• 2019

Existing dozer productivity computation models use different input variables, formulas, productivity correction factors, and experimental data source. This paper presents a method that characterizes the productivity outputs obtained by the PLS model and the Caterpillar model that are accepted as industry standards. The method identifies the input variables to be collected from the site, the performance charts to be referenced, and the formulas and implements them in a single computational tool. This study verifies that the PLS model may replace the manual computational process of Caterpillar model by eliminating reliance on graphics manipulation. Replacing the Caterpillar model with the PLS model and implementing the process as a function contributes to assess the productivity of a dozer timely by encouraging to utilize real-time information collected directly from the site. This study allows researchers and practitioners to effectively deal with the values of productivity correction factors collected from the job site and to control the productivity. The practicality and effectiveness of the method have been validated by applying to a project case.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.335-340
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• 2004

This study was conducted to evaluate the internal quality of Korean red ginseng using VIS/NIR transmittance spectra. To classify the internal qualities, partial least squares(PLS) regression was conducted. The main results are as follows: To develop the PLS model, several wave bands were divided and incorporated into the model. Among the bands, the wavelength range of 550-1,020nm, excluded noise signal, showed the best evaluation results. Effect of step size on the performance of quality evaluation showed optimal at 15 steps. In order to enhance the accuracy of quality evaluation, the abnormal spectrum shape was considered first and then the PLS model was applied. Among the 150 samples, 12 samples were evaluated by the spectrum shape. In this study, to develop the optimal PLS regression model, among the 150 samples, 138 samples was used with exception of 12 samples which could evaluate the spectrum shape. The result of quality evaluation was promising as SEC and correlation coefficient were 1.09 and 0.967, respectively, and SEP and correlation coefficient were 1.04 and 0.958, respectively.