• Proceedings of the KWS Conference
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• 2004.05a
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• pp.109-111
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• 2004

아크 플라즈마의 온도 분포를 측정하는 방법으로 개발된 CCD 카메라를 이용한 온도 측정 방법을 비대칭 플라즈마의 온도 분포 측정에 적용하기 위해 비대칭 플라즈마의 데이터 변환에 이용 가능한 아벨 역산 방법을 개발하였다. 비대칭 플라즈마 중 V-groove 상의 아크는 타원형의 플라즈마로 볼 수가 있으므로 타원형의 단면을 가지는 플라즈마에 대해 온도 측정을 수행하고 그 결과로서 모재로 전달되는 아크의 열속(heat flux)의 분포를 예측 할 수 있었다.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.1
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• pp.28-33
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• 2013

Multidirectional random waves that obliquely approach the shore were found to become directionally asymmetric due to refraction. The directional asymmetry was expressed in terms of the asymmetry parameter which is related to the maximum spreading parameter ($s_{max}$). In this study, we calculate variation of both the asymmetry and maximum spreading parameters at different water depths for various cases of incident wave angles and maximum spreading parameters in deep water. These values are different from Goda and Suzuki (1975) who neglected directional asymmetry of waves. In calculating directional asymmetry and maximum spreading parameters, we use the JONSWAP spectrum (Hasselmann et al., 1973) and Lee et al.'s (2010) directional distribution function. The processes and results are nondimensionalized with significant wave height, peak frequency and peak wave length in deep water.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1039-1047
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• 2014

An autoregressive model with normal errors is a natural model that attempts to fit time series data. More flexible models that include normal distribution as a special case are necessary because they can cover normality to non-normality models. The skewed exponential power distribution is a possible candidate for autoregressive models errors that may have tails lighter(platykurtic) or heavier(leptokurtic) than normal and skewness; in addition, the use of skewed exponential power distribution can reduce the influence of outliers and consequently increases the robustness of the analysis. We use SIR algorithm and grid method for an efficient Bayesian estimation.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.9-24
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• 2009

We compare the relation among the annual distribution of sunspots: coronal mass ejections (CMEs) and geomagnetic storms and North-South asymmetry during solar cycle 23. For this purpose, we calculate correlation coefficients between (i) annual distribution and N-S asymmetry of CMEs - sunspots (ii) distribution of CMEs - occurrence number of geomagnetic storms (iii) distribution of sunspots - occurrence number of geomagnetic storms. We find that (i) the annual distribution of total CMEs has good correlation with distribution of annual average of sunspots but poor correlation with N-S asymmetry of sunspots, N-S asymmetry of CMEs has good correlation with N-S asymmetry of sunspots: (ii) total and N-S asymmetry of CMEs have poor correlation with occurrence number of geomagnetic storms, it's, however, well correlated with the classified groups of CMEs (Ap, Dst and an indices vs. fast CMEs($\upsilon$ > $1000kms^{-1}$), Dst index vs. Halo CMEs), and (iii) sunspot numbers and area are correlated with occurrence number of geomagnetic storms. We conclude that annual distribution of CMEs and sunspots have well correlated with geomagnetic storms, N-S asymmetry of CMEs and sunspots have poor correlated with the geomagnetic storms.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.249-250
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• 2023

본 논문은 단선터널 라이닝에 비대칭 분포하중 작용 시 최적 계측 측점수를 산정하기 위해 단선터널 라이닝에 작용하는 하중조건을 비대칭 분포하중이 작용하는 경우로 가정하여 터널해석 시 널리 사용되는 상용 프로그램에 하중조건을 입력시켜 터널 라이닝의 단면 위치별 변위와 응력을 산출하였다. 산출된 변위를 계측 측점 3점, 5점, 7점으로 단선터널 라이닝 역해석 프로그램에 입력시켜서 구한 응력과 변위를 비교하여 단선터널 라이닝의 최적 계측 측점수를 산정한 결과 정확도는 측점 3점이 낮고, 측점 5점과 7점이 높으며, 현장 적용성은 측점 5점이 높은 것으로 해석되어 터널 계측 실무와 일치하는 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.81-96
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• 2006

This paper proposes a simple control chart method which can be practically used for asymmetric process data where the distribution is unknown. If we use the Shewhart type control charts which are based on normality assumption for the asymmetric process data, the type I error could increase as the asymmetry increases and the effectiveness of control chart to control variation decreases. To solve such problems, this paper suggests to calculate the control limits based on the quartiles. If we obtain the control limits by such quartile method, the type I error could decrease and it looks much more practical for asymmetric distributed process data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2621-2626
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• 2013

This paper has presented the potential distribution for asymmetric double gate(DG) MOSFET, and sloved Poisson equation to obtain the analytical solution of potential distribution. The symmetric DGMOSFET where both the front and the back gates are tied together is three terminal device and has the same current controllability for front and back gates. Meanwhile the asymmetric DGMOSFET is four terminal device and can separately determine current controllability for front and back gates. To approximate with experimental values, we have used the Gaussian function as doping distribution in Poisson equation. The potential distribution has been observed for gate bias voltage and gate oxide thickness and channel doping concentration of the asymmetric DGMOSFET. As a results, we know potential distribution is greatly changed for gate bias voltage and gate oxide thickness, especially for gate to increase gate oxide thickness. Also the potential distribution for source is changed greater than one of drain with increasing of channel doping concentration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.691-694
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• 2013

This paper has presented the potential distribution for asymmetric double gate(DG) MOSFET, and sloved Poisson equation to obtain the analytical solution of potential distribution. The symmetric DGMOSFET where both the front and the back gates are tied together is three terminal device and has the same current controllability for front and back gates. Meanwhile the asymmetric DGMOSFET is four terminal device and can separately determine current controllability for front and back gates. To approximate with experimental values, we have used the Gaussian function as charge distribution in Poisson equation. The potential distribution has been observed for gate bias voltage and gate oxide thickness and channel doping concentration of the asymmetric DGMOSFET. As a results, we know potential distribution is greatly changed for gate bias voltage and gate oxide thickness, especially for gate to increase gate oxide thickness. Also the potential distribution for source is changed greater than one of drain with increasing of channel doping concentration.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.241-250
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• 2010

Over the past few decades, several studies have been made on the modeling of circular data. But these studies focused mainly on the symmetrical cases including von Mises distribution. Recently, many studies with skew-normal distribution have been conducted in the linear case. In this paper, we dealt the problem of fitting of non-symmetrical circular data with wrapped skew-normal distribution which can be derived by using the principle of wrapping. Wrapped skew-normal distribution is very flexible to asymmetical data as well as to symmetrical data. Multi-modal data are also fitted by using the mixture of wrapped skew-normal distributions. To estimate the parameters of mixture, we suggested the EM algorithm. Finally we verified the accuracy of the suggested algorithm through simulation studies. Application with real data is also considered.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1263-1274
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• 2013

VaR (value at risk), which represents the expectation of the worst loss that may occur over a period of time within a given level of confidence, is currently used by various financial institutions for the purpose of risk management. In the majority of previous studies, the probability of return has been modeled with normal distribution. Recently Chen et al. (2010) measured VaR with asymmetric Laplacian distribution. However, it is difficult to estimate the mode, the skewness, and the degree of variance that determine the shape of an asymmetric Laplacian distribution with limited data in the real-world market. In this paper, we show that the VaR estimated with (symmetric) Laplacian distribution model provides more accuracy than those with normal distribution model or asymmetric Laplacian distribution model with real world stock market data and with various statistical measures.