• Journal of the Society of Cosmetic Scientists of Korea
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• v.30 no.2
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• pp.201-205
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• 2004

In these several years, as many people have been attracted by the functional cosmetics, there are a lot of study to enhance the stability of active ingredients for light, heat, oxygen, etc. in the academic and industrial field. Especially, catechin is well known as strong anti-oxidant, anti-inflammatory and reducing agent for oxidative stress but it is very unstable for light, heat, oxygen. etc. In this study, the stability and skin penetration of catechin are improved by 3-dimensional method. As I-dimension, porous silica is prepared using sol-gel method, and then catechin is adsorbed in pores of silica. As 2-dimension, solid lipid nanoparticles (SLN) are obtained using non-phospholipid vesicles. Finally 3-dimension is completion through lamellar phase self-organization that combines SLN catechin with skin lipid matrix. We used laser light scattering system, cyro-SEM, chromameter, HPLC and image analyzer to analyze our 3-dimentional systems. According to chromameter date, the color stability of 3-dimensional catechin is enhanced by 5-10 times compared with general liposome systems. We also confirmed through HPLC analysis that 3-dimensional catechin is more long lasting. The effect of skin penetration and wrinkle reduction are improved, too.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1130-1134
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• 1990

In this paper, we propose a mthod to decouple the multi-input multi-output two-dimensional system. Then, we analyze the realization dimension of the feedback, feedforward given to decouple. Moreover, we consider the possibility of the reduction of the dynamical dimension needed to decouple. Besides, in order to stabilize the decoupled two-dimensional system, we suggest a method to assign the poles of each entry of the transfer function matrix to the desired positions.

• Explosives and Blasting
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• v.28 no.2
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• pp.50-58
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• 2010

This study explores the slot-drilling method that has not yet enough been studied in Korea and intends to provide a theoretical framework for putting the method into practice in a construction site. The possible reduction of ground vibration by implementing slot-drilling methods is addressed. Two main subjects dealt with include the variation of vibration velocity that is based on the distance between the slot-drilling and the epicenter of blasting and the analysis of appropriate effective dimension of slot-drilling width and height to control blasting vibration. This study shows that effect of vibration reduction decreases when distance of the slot-drilling and the epicenter of blasting is getting larger and also reveals that there is a correlation between the slot size and the vibration velocity at any point.

• Journal of Korean Dental Science
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• v.6 no.1
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• pp.4-12
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• 2013

Purpose: This study sought to verify the usefulness of mini-implant and surgical steel wire in the treatment of mandibular fracture through the objective identifi cation of the change of bone structure and bone density before and after reduction by evaluating radiological change through fractal analysis when mandibular fracture is treated using mini-implant and surgical wire. Materials and Methods: This study looked at 45 patients (males: 38, female: 7) diagnosed with mandibular fracture in the oral and maxillofacial surgery division of Chung-Ang University Dental Hospital and who received open reduction and intra-osseous fi xation. Result: The average fracture dimension values were higher for the group of the patients who had mini-implants and surgical wire treatment. Conclusion: Based on the results of the study on the usefulness of the reduction technique using mini-implant and surgical steel wire in the treatment of mandibular fracture through the fractal analysis method, the reduction technique using mini-implant and surgical steel wire is regarded as an effective method of minimizing the gap between mandibular fracture fragments.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.393-400
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• 2006

There are many feature extraction methods for face recognition. We need a new method to overcome the small sample problem that the number of feature variables is larger than the sample size for face image data. The paper considers partial least squares(PLS) as a new dimension reduction technique for feature vector. Principal Component Analysis(PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So, PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases shows that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.905-922
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• 2021

Cluster analysis is one of unsupervised learning techniques used for discovering clusters when there is no prior knowledge of group membership. K-means, one of the commonly used cluster analysis techniques, may fail when the number of variables becomes large. In such high-dimensional cases, it is common to perform tandem analysis, K-means cluster analysis after reducing the number of variables using dimension reduction methods. However, there is no guarantee that the reduced dimension reveals the cluster structure properly. Principal component analysis may mask the structure of clusters, especially when there are large variances for variables that are not related to cluster structure. To overcome this, techniques that perform dimension reduction and cluster analysis simultaneously have been suggested. This study proposes probabilistic reduced K-means, the transition of reduced K-means (De Soete and Caroll, 1994) into a probabilistic framework. Simulation shows that the proposed method performs better than tandem clustering or clustering without any dimension reduction. When the number of the variables is larger than the number of samples in each cluster, probabilistic reduced K-means show better formation of clusters than non-probabilistic reduced K-means. In the application to a real data set, it revealed similar or better cluster structure compared to other methods.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.49-66
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• 1996

The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.173-180
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• 2015

We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal components among the candidate set using sparse regression techniques. The performance of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression and partial least square regression) using synthetic and real datasets.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.251-258
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• 2016

It is important to verify the identity of plant-based multivitamins prepared with a natural-concept and popular for daily consumption because they are easily purchased in markets with imperfect information. For this study, a generalized partially double-index model (GPDIM) was employed as a main statistical method to identify the contribution of vitamins and phytochemicals to antioxidant potentials using data on antioxidant capacities and chemical fingerprinting. A bootstrapping approach via sufficient dimension reduction is adopted to estimate the two unknown coefficient vectors in the GPDIM. Fifth order polynomial regressions are fitted to measure the contributions of vitamins and phytochemicals after estimating the coefficient vectors with the two double indices.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.233-250
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• 2021

This paper studies if the accuracies of mortality models (LC model vs. 4-parametric model) are aggravated if a mortality structure changes due to the impact of COVID-19. LC model (LCM) uses dimension reduction for fitting to the log mortality matrix so that the performance of the dimension reduction method may not be good when the matrix structure changes. On the other hand, 4-parametric factor model (4-PFM) is designed to use factors for fitting to log mortality data by age groups so that it would be less affected by the change of the mortality structure. In fact, the forecast accuracies of LCM are better than those of 4-PFM when life-tables are used whereas those of 4-PFM are better when the mortality structure changes. Thus this result shows that 4-PFM is more reliable in performance to the structural changes of the mortality. To support the accuracy changes of LCM the functional aspect is explained by computing eigenvalues produced by singular vector decomposition