• Proceedings of the Korean Vacuum Society Conference
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• 2006.02a
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• pp.240-240
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• 2006

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.29-33
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• 2004

• Proceedings of the Korean Vacuum Society Conference
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• 1994.06a
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• pp.39-39
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• 1994

• Proceedings of the Korean Vacuum Society Conference
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• 1994.06a
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• pp.40-41
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• 1994

• Proceedings of the Korean Magnestics Society Conference
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• 1995.10a
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• pp.13-14
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• 1995

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.185-191
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• 2004

Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.295-298
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• 1996

In distillation column control, secondary measurements such as temperatures and flows are widely used in order to infer product composition. This paper addresses the design of static estimators using the secondary measurements for estimating the product compositions of the multicomponent distillation columns. Based on the unified framework for the estimator problems, the relationships among several typical static estimators are discussed including the effect of the measured inputs. Design guidelines for the composition estimator using PLS regression are also presented. The estimator based on the guidelines is robust to sensor noise and has a good predictive power.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.355-366
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• 2003

A unified procedure for principal component regression (PCR), partial least squares (PLS) and ordinary least squares (OLS) is proposed. The process gives solutions for PCR, PLS and OLS in a unified and non-iterative way. This enables us to see the interrelationships among the three regression coefficient vectors, and it is seen that the so-called E-matrix in the solution expression plays the key role in differentiating the methods. In addition to setting out the procedure, the paper also supplies a robust numerical algorithm for its implementation, which is used to show how the procedure performs on a real world data set.

• 한국경영정보학회:학술대회논문집
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• 2007.11a
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• pp.533-538
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• 2007

Since the Internet has been widespread all over the world, it has been getting more popular. Almost every bank that runs offline business has its own Web site to provide online banking service. In this study, we developed a research model based on the Technology Acceptance Model (TAM). We added the concepts of trust and cost to evaluate our model because trust is a major concept in adopting online service, and cost is one of the main strategies to attract customers to use online banking service. To see the validation of the model, we used partial least squares (PLS). A survey was done to gather data. The result was drawn from the model test, and we discuss it and conclude the study.

• Journal of Biosystems Engineering
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• v.23 no.1
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• pp.49-56
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• 1998

The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.