• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.383-392
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• 2010

Modelling financial phenomena with diffusion processes is frequently used technique. This study reviews the earlier researches on the approximation problem of transition densities of diffusion processes, which takes important roles in estimating diffusion processes, and consider the method to obtain the coefficients of series efficiently, in series approximation method of transition densities. We developed a new efficient algorithm to compute the coefficients which are represented by repeated Dynkin operator on Hermite polynomial.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.343-347
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• 2012

Non-linear equalization techniques using decision feedback structure are highly demanded for cancellation of intersymbol interferences occurred in severe channel environments. In this paper decision feedback structure is applied to the linear blind equalizer algorithm that is based on information theoretic learning and a randomly generated symbol set. At the decision feedback equalizer (DFE) the random symbols are generated to have the same probability density function (PDF) as that of the transmitted symbols. By minimizing difference between the PDF of blind DFE output and that of randomly generated symbols, the proposed DFE algorithm produces equalized output signal. From the simulation results, the proposed method has shown enhanced convergence and error performance compared to its linear counterpart.

• Journal of Korea Water Resources Association
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• v.39 no.5 s.166
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• pp.417-430
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• 2006

In this study, the probability density functions (PDFs) of the rainfall generated by PRPM(Poisson Rectangular Pulse Model) and the runoff simulated by SLRM(Single Linear Reservoir Model) and Nash model, were compared to find out the changes of runoff characteristics due to the change of rainfall characteristics. Effect of rainfall frequency, Intensity, and duration on runoff were evaluated using the PDFs derived. Two basin, small and midium-sized ones, were also selected to find out the effect of basin size. As the results, we found that the arrival time, the intensity, and the duration of rainfall differently influence the runoff characteristics, which could be applied to evaluate the effect of climate change.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.1
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• pp.83-91
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• 2004

Nonlinear system responses of an impact system under randomly perturbed harmonic excitations are predicted in the probability domain by adopting the semi-analytical procedure previously developed. The semi-analytical procedure is obtained by solving the Fokker-Planck equation corresponding to the stochastic differential equation of the given impact system by utilizing the path-integral solution. The evolutionary joint probability density functions are generated by using the method, and the characteristics of nonlinear dynamic response behaviors of the system are examined. Noise effects on the responses are also examined. It Is found that the semi-analytical method can provides the accurate information of the responses via the joint probability functions for the impact system. It is found that the noises weaken and eventually terminate the chaos in the responses, but it is also found that the chaotic signatures reside in the presence of the external noise with relatively high intensity. The joint probability density function shows that the ensemble of the system responses are weakly stationary.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.259-271
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• 2020

In Korea, snow damage has happened in the region with little snowfalls in history. Also, accidental damage was caused by heavy snow leads and the public interest on heavy snow has been increased. Therefore, policy about the Natural Disaster Reduction Comprehensive Plan has been changed to include the mitigation measures of snow damage. However, since heavy snow damage was not frequent, studies on snowfall have not been conducted on different points. The characteristics of snow data commonly are not the same as the rainfall data. Some southern coastal areas in Korea are snowless during the year. Therefore, a joint probability distribution was suggested to analyze the snow data with many 0s in a previous research and fitness from the joint probability distribution was higher than the conventional methods. In this study, snow frequency analysis was implemented using the joint probability distribution and compared to the design codes. The results were compared to the design codes. The results of this study can be used as the basic data to develop a procedure for the snow frequency analysis in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.225-236
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• 2008

The techniques to calculate the probability precipitation for the design of hydrological projects can be determined by the point frequency analysis and the regional frequency analysis. Probability precipitation usually calculated by point frequency analysis using rainfall data that is observed in rainfall observatory which is situated in the basin. Therefore, Probability precipitation through point frequency analysis need observed rainfall data for enough periods. But, lacking precipitation data can be calculated to wrong parameters. Consequently, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to point frequency analysis because of suppositions about probability distributions. In this paper, rainfall observatory in Korea did grouping by cluster analysis using position of timely precipitation observatory and characteristic time rainfall. Discordancy and heterogeneity measures verified the grouping precipitation observatory by the cluster analysis. So, there divided rainfall observatory in Korea to 6 areas, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function. At the results, the regional frequency analysis of the variable kernel function can utilize for decision difficulty of suitable probability distribution in other methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.285-287
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• 2010

본 논문에서는 기존 오류정정부호의 복호 과정에 사용되는 Belief propagation (BP) 알고리즘을 이용한 저밀도 양자 오류정정 부호의 복호 기법에 대해 기술한다. Depolarizing 채널 가정하에 기존 오류정정부호와 다르게 양자 오류정정 부호가 갖는 초기 채널 오류 확률에 의한 성능 열화를 개선하기 위해 초기 채널 오류 확률 정보를 개선하는 기법을 적용하였다. 테너 그래프를 바탕으로 각 체크 노드의 신드롬과 노드의 연결 상태를 고려하여 오류가 발생한 위치를 추적하고 BP 알고리즘에 입력되는 초기 채널 오류 확률 정보를 수정하여 반복 복호 시 발생할 수 있는 성능 열화를 개선하였다.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.56.1-56.1
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• 2012

구경 3.6m CFHT 관측으로 얻은 산개성단 NGC 1245와 NGC 2506의 측광결과를 이용하여 두 성단의 역학적 진화와 헤일로 구조를 연구하였다. 이를 위하여 우리는 별들의 색-등급 도상의 위치와 천구 상에서의 위치를 동시에 고려하여, 각 별들에 대한 측광학적 구성원 확률을 정의하는 방법을 개발하였다. 이 방법을 사용하여 얻어진 구성원 확률을 이용하여 성단의 반경에 따른 구성원 별들의 광도함수와 반경에 따른 별들의 유효개수밀도 분포를 구하였다. 이들로부터 우리는 두 성단에서 질량분리현상을 확인하였으며, 성단에서 증발한 별들에 의해 형성된 헤일로의 존재를 알 수 있었다. 또한, 각 성단들의 공간상에 투영된 유효개수밀도 분포를 분석하여, 두 성단 모두 형태가 찌그러져 있으며 그 방향은 은하면과 관련이 적음을 보였다. 본 발표에서는 새로 개발한 구성원 확률을 정의하는 방법 및 두 성단의 역학적 진화와 헤일로 구조에 대한 내용을 소개하고자 한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.825-834
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• 1999

In this paper we derive the probability density function of the phase error and that of the envelope of the received signal over Nakagami fading channel. Using the new probability density functions we analyze the performance of coherent BPSK, noncoherent FSK, DPSK and noncoherent ASK on Nakagami fading by means of new methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.847-851
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• 2004

In the original mixture density network(MDN), which was introduced by Bishop and Nabney, the parameters of the conditional probability density function are represented by the output vector of a single multi-layer perceptron. Among the recent modification of the MDNs, there is the so-called modified mixture density network, in which each of the priors, conditional means, and covariances is represented via an independent multi-layer perceptron. In this paper, we consider a further simplification of the modified MDN, in which the conditional means are linear with respect to the input variable together with the development of the MATLAB program for the simplification. In this paper, we first briefly review the original mixture density network, then we also review the modified mixture density network in which independent multi-layer perceptrons play an important role in the learning for the parameters of the conditional probability, and finally present a further modification so that the conditional means are linear in the input. The applicability of the presented method is shown via an illustrative simulation example.