• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4B
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• pp.345-352
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• 2002

In this paper, in order to evaluate the performance of IS-95 system reverse link in white gaussian noise and rayleigh fading environment, we suggest epochal proposal to improve computer run-time and its efficiency is verified in terms of the number of samples. MC(Monte Carlo) simulation is the most popular simulation technique lately, but MC simulation requires a number of samples at low bit error rate. Therefore, MC cannot avoid the limit of computer run-time. To alleviate these problems, we apply the suggested method called central moment technique to the reverse link of the IS-95 system and can obtain discrete probability mass functions from Nth order central moments of the less number of received signal samples than those required in MC. Continuous cumulative probability distribution function can be accurately estimated by using interpolation and the improvement effect for the number of samples is proven.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.61-67
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• 2017

For a project network analysis, a fundamental problem is to estimate the distribution function of the project completion time. In this paper, we propose a method for evaluating moments(mean, variance, skewness, kurtosis) of the project completion time under the assumption that the durations of activities are independently and normally distributed. The proposed method utilizes the technique of discretization to replace the continuous probability density function(pdf) of activity duration with its discrete pdf and a random number generation. The proposed method is easy to use for large-sized project networks, and the computational results of the proposed method indicate that the accuracy is comparable to that of direct Monte Carlo simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.857-864
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• 2004

Many image compression standards such as JPEG, MPEG or H.263 are based on the discrete cosine transform (DCT) and quantization method. Quantization error. is the major source of image quality degradation. The current dequantization method assumes the uniform distribution of the DCT coefficients. Therefore the dequantization value is the center of each quantization interval. However DCT coefficients are regarded to follow Laplacian probability density function (pdf). The center value of each interval is not optimal in reducing squared error. We use mean of the quantization interval assuming Laplacian pdf, and show the effect of correction on image quality. Also, we compare existing quantization error to corrected quantization error in closed form. The effect of PSNR improvements due to the compensation to the real image is in the range of 0.2 ∼0.4 ㏈. The maximum correction value is 1.66 ㏈.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1861-1870
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• 2013

A climate change-driven increased hydrological variability has been widely acknowledged over the past decades. In this regards, rainfall simulation techniques are being applied in many countries to consider the increased variability. This study proposed a Homogeneous Hidden Markov Chain(HMM) designed to recognize rather complex patterns of rainfall with discrete hidden states and underlying distribution characteristics via mixture probability density function. The proposed approach was applied to Seoul and Jeonju station to verify model's performance. Statistical moments(e.g. mean, variance, skewness and kurtosis) derived by daily and seasonal rainfall were compared with observation. It was found that the proposed HMM showed better performance in terms of reproducing underlying distribution characteristics. Especially, the HMM was much better than the existing Markov Chain model in reproducing extremes. In this regard, the proposed HMM could be used to evaluate a long-term runoff and design flood as inputs.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.25-36
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• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.