• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.733-745
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• 2017

Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, $L_2$-norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.6 no.3
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• pp.43-49
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• 1992

Forecasting of electric power demand has been a basic element for electric power system operation and system development, and it's accuracy has very strong influence on reliability and economical efficience of power supply. So, in this paper, long―term maximum electric power demand has been forecasted by using the triple exponential smoothing method initiated R.G.Brown. It has been regarded this method as high accuracy and operational convenience. The smoothing function is a liner combination of all past observations and the weight given to previous observations decreases geometrically with age.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.173-184
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• 1994

원자력 압력용기강의 탄소성 파괴인성값 $J_IC$를 CT형 시험편을 이용하여 검토하였으며, 평활 시험편 및 측면홈 시험편의 두께는 각각 $B_O$=25.4mm, $B_N$=20.4mm 이다. 측면홈의 깊이는 19.7% 이며, 홈의 각도는 90 .deg.로 가공하였다. 탄소성 파괴인성시험은 ASTM E813-81과 JSME S001-81의 추천방법에 따라 실시하였다. 두 추천방법으로 실험한 결과 ASTM 방법에 의한 $J_IC$값이 과대평가됨으로써 부대조건에 만족되지 못하였지만 JSME방법은 만족되었다. 측면홈 시험편은 R고선법에 의한 ductile tearing의 결정이 평활 시험편보다 용이하였으며, 이에 따른 $J_IC$값의 정확성을 배가 할 수 있었다. 또한 임계 스트레치존 폭($SZW_C$)은 측면홈에 의한 높은 3축응력이 발생되어 평활시험편보다 적게 나타났으며, 이러한 복합적인 원인에 기인하여 스트레치존법에 의한 $J_IC$평가는 R곡선법에 의한 평가보다 약간 과대평가됨을 알 수 있었다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.3
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• pp.289-297
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• 2011

The explicit finite element method is an essential tool for solving large problems with severe nonlinear characteristics, but its results can be difficult to interpret. In particular, it can be impossible to evaluate its acceleration responses because of severe discontinuity, extreme noise or aliasing. We suggest a new post-processing method for transient responses and their response spectra. We propose smoothing methods using a Gaussian kernel without in depth knowledge of the complex frequency characteristics; such methods are successfully used in the filtering of digital signals. This smoothing can be done by measuring the velocity results and monitoring the response spectra. Gaussian kernel smoothing gives a better smoothness and representation of the peak values than other approaches do. The floor response spectra can be derived using smoothed accelerations for the design.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.748-760
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• 2012

In case brightness distribution is concentrated in a region, it is difficult to classify the image features. To solve this problem, we apply global histogram equalization and local histogram equalization to images. In case of global histogram equalization, it can be too bright or dark because it doesn't consider the density of brightness distribution. Thus, it is difficult to enhance the local contrast in the images. In case of local histogram equalization, it can produce unexpected blocks in the images. In order to enhance the contrast in the images, this paper proposes a local histogram equalization based on the Gaussian Mixture Models(GMMs) in regions of histogram. Mean and variance parameters in each regions is updated EM-algorithm repeatedly and then ranges of equalization on each regions. The experimental results performed with image of various contrasts show that the proposed algorithm is better than the global histogram equalization.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.504-511
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• 2014

This paper presents a hybrid recognition method, which is based on the nonlinear histogram equalization and the multidimensional intensity variation of an images. The nonlinear histogram equalization based on a adaptively modified function is applied to improve the quality by adjusting the brightness of the image. The multidimensional intensity variation by considering the a extent of 4-step changes in brightness between the adjacent pixels is also applied to reflect accurately the attributes of image. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to comprehensively measure the similarity between the images. The NCC is considered by the intensity variation of each 2-direction(x-axis and y-axis) image. The proposed method has been applied to the problem for recognizing the 50-face images of 40*40 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the histogram equalization, or the linear histogram equalization, respectively.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.993-1000
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• 2016

Semi-supervised learning makes it easy to use an unlabeled data in the supervised learning such as classification. Applying the semi-supervised learning on the regression analysis, we propose two methods for a better regression function estimation. The proposed methods have been assumed different marginal densities of independent variables and different smoothing parameters in unlabeled and labeled data. We shows that the overfitted pilot estimator should be used to achieve the fastest convergence rate and unlabeled data may help to improve the convergence rate with well estimated smoothing parameters. We also find the conditions of smoothing parameters to achieve optimal convergence rate.

• Journal of Life Science
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• v.22 no.12
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• pp.1595-1599
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• 2012

Cell cycle activation and progression in vascular proliferative disease represent potent therapeutic targets. Luteolin, which occurs as glycosylated forms in celery, green pepper, perilla leaf, and camomile tea, has demonstrated antimutagenic, antitumorigenic, antioxidant, and antiinflammatory properties. In this study, we investigated the effect of luteolin on the proliferation of primary cultured rat aortic vascular smooth muscle cells induced by 5% fetal bovine serum. Luteolin at concentrations of 5, 20, and $50{\mu}M$ significantly inhibited this proliferation by 29.6, 50.8, and 83.1%, respectively. The incorporation of $[^3H]$-thymidine into DNA was also inhibited by 25.8, 57.6, and 81.0%, respectively. Flow cytometry analysis of DNA content revealed that FBS-inducible cell cycle progression was blocked by luteolin. Luteolin showed no cytotoxicity in VSMCs in this experimental condition according to WST-1 assays. Luteolin may represent a potential anti-proliferative agent for treatment of angioplasty restenosis and atherosclerosis.

• Journal of Life Science
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• v.28 no.12
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• pp.1516-1522
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• 2018

Atherosclerosis is an obstructive vessel disease mainly caused by chronic arterial inflammation to which the proliferation and migration of vascular smooth muscle cells (VSMCs) is the main pathological response. In the present study, the primary responsible inflammatory cytokine and its signaling pathway was investigated. The proliferation and migration of VSMCs was significantly enhanced by the prostaglandin $F_{2{\alpha}}$ ($PGF_{2{\alpha}}$), while neither was affected by tumor necrosis factor ${\alpha}$. Prostacyclin $I_2$ was seen to enhance the proliferation of VSMCs while simultaneously suppressing their migration. Both prostaglandin $D_2$ and prostaglandin $E_2$ significantly enhanced the migration of VSMCs, however, proliferation was not affected by either of them. The proliferation and migration of VSMCs stimulated by $PGF_{2{\alpha}}$ progressed in a dose-dependent manner; the $EC_{50}$ value of both proliferation and migration was $0.1{\mu}M$. VSMCs highly expressed the phospholipase isoform $C-{\beta}3$ ($PLC-{\beta}3$) while others such as $PLC-{\beta}1$, $PLC-{\beta}2$, and $PLC-{\beta}4$ were not expressed. Inhibition of the PLCs by U73122 completely blocked the $PGF_{2{\alpha}}$-induced migration of VSMCs, and, in addition, silencing $PLC-{\beta}3$ significantly diminished the $PGF_{2{\alpha}}$-induced proliferation and migration of VSMCs. Given these results, we suggest that $PGF_{2{\alpha}}$ plays a crucial role in the proliferation and migration of VSMCs, and activation of $PLC-{\beta}3$ could be involved in their $PGF_{2{\alpha}}$-dependent migration.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.275-287
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• 1998

The goal of this paper is to suggest the goodness-of-fit test statistic for the no effect model. The comparisons of powers on test statistics are conducted with three types alternative modles.