• Imaging Science in Dentistry
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• v.53 no.4
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• pp.313-323
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• 2023

Purpose: This study aimed to investigate the potential factors that could affect the reduction rate of odontogenic cysts following decompression using cone-beam computed tomography (CBCT) for 3-dimensional volumetric analysis. Materials and Methods: The study sample consisted of CBCT images of 41 individuals who underwent decompression of odontogenic cysts at the Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Chulalongkorn University, between 2010 and 2022. Preoperative and postoperative CBCT results were collected, and a volumetric analysis was conducted to evaluate the differences in the reduction rate and the percentage of volume reduction of cystic lesions based on different parameters. Correlations between these parameters were analyzed to determine associations. Results: In this study, the average time of decompression for odontogenic cysts was 316 days. Males demonstrated a higher reduction rate than females (P<0.05). The reduction rate was directly proportional to initial cyst volume, with higher reduction rates for cysts with large initial volume than those with small initial volume (P<0.05). Spearman's rank correlation coefficient indicated a weak positive correlation between the initial cyst volume and the duration of decompression. Additionally, a strong positive correlation was observed between the initial volume and the reduction rate. Conclusion: Knowledge of the reduction rate of odontogenic cysts is vital for surgeons to evaluate the duration of decompression before enucleation and to determine a definitive treatment plan. Sex and initial lesion volume had significant effects on the reduction rate.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.2
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• pp.72-79
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• 2021

The present paper aims to set forth a two-dimensional theory for the dynamics of plates that is valid over a large range of excitation. To construct a dynamic plate theory within the long-wavelength approximation, two dimensional-reduction procedures must be used for analyzing the low- and high-frequency behaviors under the dynamic variational-asymptotic method. Moreover, a separate and logically independent step for the short-wavelength regime is introduced into the present approach to avoid violation of the positive definiteness of the derived energy functional and to facilitate qualitative description of the three-dimensional dispersion curve in the short-wavelength regime. Two examples are presented to demonstrate the capabilities and accuracy of all of the formulas derived herein by using various dispersion curves through comparison with the three-dimensional finite element method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• Communications for Statistical Applications and Methods
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• v.28 no.3
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• pp.267-279
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• 2021

A regression with multi-dimensional responses is quite common nowadays in the so-called big data era. In such regression, to relieve the curse of dimension due to high-dimension of responses, the dimension reduction of predictors is essential in analysis. Sufficient dimension reduction provides effective tools for the reduction, but there are few sufficient dimension reduction methodologies for multivariate regression. To fill this gap, we newly propose two fused slice-based inverse regression methods. The proposed approaches are robust to the numbers of clusters or slices and improve the estimation results over existing methods by fusing many kernel matrices. Numerical studies are presented and are compared with existing methods. Real data analysis confirms practical usefulness of the proposed methods.

• Journal of Electrochemical Science and Technology
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• v.11 no.4
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• pp.392-398
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• 2020

Producing solar grade silicon using an inexpensive method is a key factor in lowering silicon solar cell costs; the direct electrochemical reduction of SiO₂ in molten salt is one of the more promising candidates for manufacturing this silicon. In this study, SiO₂ granules were electrochemically reduced in molten CaCl₂ (850℃) using Ag-Si eutectic droplets that catalyze electrochemical reduction and purify the Si product. When Ag is used as the working electrode, the Ag-Si eutectic mixture is formed naturally during SiO₂ reduction. However, since the Ag-Si eutectic droplets are liquid at 850℃, they are easily lost during the reduction process. To minimize the loss of liquid Ag-Si eutectic droplets, a cylindrical graphite container working electrode was introduced and Ag was added separately to the working electrode along with the SiO₂ granules. The graphite container working electrode successfully prevented the loss of the Ag-Si eutectic droplets during reduction. As a result, the Ag-Si eutectic droplets acted as stable catalysts for the electrochemical reduction of SiO₂, thereby producing one-dimensional Si rods through a mechanism similar to that of vapor-liquid-solid growth.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.7
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• pp.1127-1140
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• 1997

With the study for identifying the transmission characteristics of vibration and noise generated by operating engine system of a vehicle, recently many engineers have studied actively the reduction of vibration and noise inducing uncomfortableness to the passenger. In this study, output noise was analyzed by multi-dimensional spectral analysis and vector synthesis method. The multi-dimensional analysis method is very effective in case of identification of primary source, but this method has little effect on suggestion for interior noised reduction. For compensation of this, vector synthesis method was used to obtain effective method for interior noise reduction, after identifying primary source for output noise. In this paper, partial coherence function of each input was calculated to know which input was most coherent to output noise, then with simulation of changes for input magnitude and phase by vector synthesis diagram, the trends of synthesized output vector was obtained. As a result, the change of synthesized output vector could be estimated.

• Journal of the Korea Furniture Society
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• v.19 no.3
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• pp.172-179
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• 2008

The objective of this study was to investigate the influence of the water repellents' treatment on the water absorptivity and the dimensional stability of Korean red pine wood(Pinus densiflora). The alkylketene dimer(AKD), fluororesin emulsion(Wood-seal, WS), and paraffin wax emulsions(PW25, PW40, and PW1200) were used as water repellents. PW40 and WS were proved as excellent water repellents for pine wood, because the samples treated with these agents showed high contact angles and large reduction in water absorptivities. Also, the dimensional stability(antiswelling efficiency) of pine wood was considerably improved by water repellents treatments, such as PW40 and WS at the pressure of 2MPa. The water repellent treated woods with WS, PW40, and PW1200 at the pressure of 2MPa were relatively stable to the wet-dry cyclic leaching test.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.817-824
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• 2010

Linear Discriminant Analysis (LDA) has been successfully applied for dimension reduction in face recognition. However, LDA requires the transformation of a face image to a one-dimensional vector and this process can cause the correlation information among neighboring pixels to be disregarded. On the other hand, 2D-LDA uses 2D images directly without a transformation process and it has been shown to be superior to the traditional LDA. Nevertheless, there are some problems in 2D-LDA. First, it is difficult to determine the optimal number of feature vectors in a reduced dimensional space. Second, the size of rectangular windows used in 2D-LDA makes strong impacts on classification accuracies but there is no reliable way to determine an optimal window size. In this paper, we propose a new algorithm to overcome those problems in 2D-LDA. We adopt an ensemble approach which combines several classifiers obtained by utilizing various window sizes. And a practical method to determine the number of feature vectors is also presented. Experimental results demonstrate that the proposed method can overcome the difficulties with choosing an optimal window size and the number of feature vectors.

• Journal of the Korea Furniture Society
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• v.27 no.4
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• pp.302-308
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• 2016

It has been known that acetylation improves the dimensional stability of wood. Liquid phase acetylation is more popular than gas-phase acetylation for the effectiveness of weight gain of wood. In this study the specimens of domestic red and Korean pines are acetylated in liquid phase and their physical properties, such as density, bending strength, anti-hygroscopicity etc., are analyzed. Acetylation increases the average weights and volume of larch specimens by 11.4% and 3.4%, respectively, and their average oven-dry densities are increased by $0.03g/cm^3$. Acetylation does not influence on Modulus of Rupture (MOR) and Modulus of Elasticity (MOE). The average Percentage Reduction in Hygroscopicity (PRH) and average Percentage Reduction in Water soaking (PRW) of larch specimens are respectively 20.2% and 20.8%. Thus it can be concluded that acetylation improves the dimensional stability of larch specimens.