• East Asian mathematical journal
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• 제39권5호
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• pp.537-547
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• 2023

In this paper, we introduce the optimal approximation by a Gaussian function for a probability density function. We show that the approximation can be obtained by solving a non-linear system of parameters of Gaussian function. Then, to understand the non-normality of the empirical distributions observed in financial markets, we consider the nearly Gaussian function that consists of an optimally approximated Gaussian function and a small periodically oscillating density function. We show that, depending on the parameters of the oscillation, the nearly Gaussian functions can have fairly thick heavy tails.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제5B권3호
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• pp.258-261
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• 2005

The Preisach model needs a distribution function or Everett function to simulate the hysteresis phenomena. To obtain these functions, many experimental data obtained from the first order transition curves are usually required. In this paper, a simple procedure to determine the Preisach density function using the Gaussian distribution function and genetic algorithm is proposed. The Preisach density function for the interaction field axis is known to have Gaussian distribution. To determine the density and distribution, genetic algorithm is adopted to decide the Gaussian parameters. With this method, just basic data like the initial magnetization curve or saturation curves are enough to get the agreeable density function. The results are compared with experimental data and we got good agreements comparing the simulation results with the experiment ones.

• Composites Research
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• 제29권5호
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• pp.299-305
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• 2016

본 연구는 이변량 Gaussian 분포함수를 적용하여 CFRP 적층판의 섬유물성 변화를 추정하는 방법을 제안하였다. 섬유의 손상 분포를 규명하기 위하여 수정된 이변량 Gaussian 분포함수를 적용하여 5개의 미지 변수가 고려되었다. 조합된 컴퓨터 기법을 적용하여 역문제를 해결하기 위하여 본 연구에서는 몇 개의 고유진동수와 모드 정보를 입력데이터로 활용하였다. 수치해석 예제는 제안된 기법이 적층배열 변화에 따른 CFRP 판의 섬유 손상 분포 및 위치를 규명할 수 있는 적합하고 실용적은 방법임을 보여준다.

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.200-204
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• 2012

This study has presented the analysis of breakdown voltage for a double-gate metal-oxide semiconductor field-effect transistor (MOSFET) based on the doping distribution of the Gaussian function. The double-gate MOSFET is a next generation transistor that shrinks the short channel effects of the nano-scaled CMOSFET. The degradation of breakdown voltage is a highly important short channel effect with threshold voltage roll-off and an increase in subthreshold swings. The analytical potential distribution derived from Poisson's equation and the Fulop's avalanche breakdown condition have been used to calculate the breakdown voltage of a double-gate MOSFET for the shape of the Gaussian doping distribution. This analytical potential model is in good agreement with the numerical model. Using this model, the breakdown voltage has been analyzed for channel length and doping concentration with parameters such as projected range and standard projected deviation of Gaussian function. As a result, since the breakdown voltage is greatly changed for the shape of the Gaussian function, the channel doping distribution of a double-gate MOSFET has to be carefully designed.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.26-29
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• 2002

Radial Basis Function (RBF) networks is known as efficient method in classification problems and function approximation. The basis function of RBF networks is usual adopted normal distribution like the Gaussian function. The output of the Gaussian function has the maximum at the center and decrease as increase the distance from the center. For learning of neural network, the method treating the limited area of input space is sometimes more useful than the method treating the whole of input space. The q-normal distribution is the set of probability density function include the Gaussian function. In this paper, we introduce the RBF networks with the basis function of q-normal distribution and actually approximate a function using the RBF networks.

• ETRI Journal
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• 제32권1호
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• pp.145-147
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• 2010

In this letter, we present a new approximation for the twodimensional (2-D) Gaussian Q-function. The result is represented by only the one-dimensional (1-D) Gaussian Q-function. Unlike the previous 1-D Gaussian-type approximation, the presented approximation can be applied to compute the 2-D Gaussian Q-function with large correlations.

• Wind and Structures
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• 제31권6호
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• 해양환경안전학회지
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• 제21권3호
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• pp.253-258
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• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• 한국정보통신학회논문지
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• 제18권5호
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• pp.1143-1148
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• 2014

본 연구에서는 비대칭 이중게이트 MOSFET의 채널 내 도핑분포함수의 변화에 따른 문턱전압이하 스윙의 변화를 분석하였다. 이중게이트 MOSFET의 특성을 결정하는 가장 기본적인 요소는 채널의 크기 즉, 채널길이, 채널두께 등과 채널의 도핑분포함수이다. 도핑분포는 채널도핑 시 사용하는 이온주입법에 의하여 결정되며 일반적으로 가우스분포함수에 준한다고 알려져 있다. 포아송방정식을 이용하여 전하분포를 구하기 위하여 가우스분포함수을 이용하였다. 가우스분포함수는 반드시 상하 대칭이 아니므로 채널길이 및 채널두께, 그리고 비대칭 이중게이트 MOSFET의 상하단 게이트 전압 변화 등에 따라 문턱전압이하 스윙 값은 크게 변화할 것이다. 이에 본 연구에서는 가우스분포함수의 파라미터인 이온주입범위 및 분포편차에 따른 문턱전압이하 스윙의 변화를 관찰하고자 한다. 분석결과, 문턱전압이하 스윙은 도핑분포함수 및 게이트 전압 등에 따라 크게 영향을 받는 것을 관찰할 수 있었다.

• ETRI Journal
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• 제33권6호
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• pp.949-952
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• 2011

In this letter, a simplified suboptimum receiver based on soft-limiting for the detection of binary antipodal signals in non-Gaussian noise modeled as a generalized normal-Laplace (GNL) distribution combined with Gaussian noise is presented. The suboptimum receiver has low computational complexity. Furthermore, when the number of diversity branches is small, its performance is very close to that of the Neyman-Pearson optimum receiver based on the probability density function obtained by the Fourier inversion of the characteristic function of the GNL-plus-Gaussian distribution.