• 한국지진공학회논문집
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• 제9권6호
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• pp.53-58
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• 2005

본 연구에서는 지진하중을 받는 교량의 거동을 확률밀도함수를 통하여 분석할 수 있는 기법을 개발하였다. 확률밀도함수의 전개는 추계론적 이론을 이용한 반해석적 방법을 통하여 구하였으며, 반해석적 방법은 교량운동방정식으로부터 상응하는 Fokker-Planck equation을 구한 후, path-integral solution을 유도하여 이를 수치적으로 해석함으로써 구할 수 있다. 교량거동의 확률밀도 함수전개로부터 교량거동의 확률적 특성을 파악하고 확률밀도함수의 범위로부터 교량응답거동의 포락선을 얻을 수 있으며 이를 이용하여 최대응답의 범위를 결정할 수 있다는 것을 밝혔다.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.349-359
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• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

• 한국광학회지
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• 제11권6호
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• pp.441-446
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• 2000

Balanced Homodyne Detection 방법으로 국소 진동자의 위상을 조절해 주면서, 각 위상에 대하여 광전류를 측정하여 전류세기의 분포함수를 구하여 이 값을 라돈 역변환을 포함한 Filtered Back Projection하여 빛의 양자역학적 상태를 규정하는 위그너 함수를 구하고 이로부터 간접적으로 밀도행렬을 구한다. 또 위상에 관계없이 구해진 분포함수에서 Pattern함수를 이용하여 밀도행렬을 구할 수 있다. 본 연구에서는 위의 모든 과정을 전산시늉을 통하여 여러 양자역학적 상태 입력 광에 대하여 예측되는 위그너 함수와 밀도 행렬을 구하였다.

• 한국유체기계학회 논문집
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• 제19권3호
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• pp.33-36
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• 2016

The investigation on the verification of availability simulation for small-scale plant has been carried out. This study focuses on the availability variation induced by number of equipment and iteration with failure density function. The equipment classification of small-scale plant and failure type and the methodologies on Monte-Carlo simulation are established. The availability deviation with programs showed under Max. 1.7% for the case of normal function. This method could be used to availability evaluation of small-scale plant, but calibration of the failure density function is necessary for general application.

• 대한기계학회논문집B
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• 제22권5호
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• pp.607-617
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• 1998

Comparisons of joint probability density distribution obtained from the raw data of measured droplet sizes and velocities in a transient diesel fuel spray with computed joint probability density function were made. Simultaneous droplet sizes and velocities were obtained using PDPA. Mathematical probability density functions which can fit the experimental distributions were extracted using the principle of maximum likelihood. Through the statistical process of functions, mean droplet diameters, non-dimensional mass, momentum and kinetic energy were estimated and compared with the experimental ones. A joint log-hyperbolic density function presents quite well the experimental joint density distribution which were extracted from experimental data.

• 대한수학회논문집
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• 제33권4호
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• pp.1367-1376
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• 2018

In practice, the density function of a random variable X is always unknown. Even its smoothness parameter is unknown to us. In this paper, we will consider a density smoothness parameter estimation problem via wavelet theory. The smoothness parameter is defined in the sense of equivalent Besov norms. It is well-known that it is almost impossible to estimate this kind of parameter in general case. But it becomes possible when we add some conditions (to our proof, we can not remove them) to the density function. Besides, the density function contains impurities. It is covered by some additive noises, which is the key point we want to show in this paper.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.507-514
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• 1998

The minimum distance estimation based on the L$_2$ distance between a model density and a density estimator is studied from M-estimation point of view. We will show that how a model density and a density estimator are incorporated in order to create an M-estimation function. This method enables us to create an M-estimating function reflecting the natures of both an assumed model density and a given set of data. Some new types of M-estimation functions for estimating a location and scale parameters are introduced.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 추계학술대회 논문집
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• pp.397-398
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• 2007

• 한국전산응용수학회:학술대회논문집
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• 한국전산응용수학회 2003년도 KSCAM 학술발표회 프로그램 및 초록집
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• pp.12.1-12
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• 2003

Sufficient conditions are given under which a generalized class of kernel-type estimators allows asymptotic approximation On the modulus of continuity This generalized class includes sample distribution function, kernel-type estimator of density function, and an estimator that may apply to the censored case. In addition, an application is given to asymptotic normality of recursive density estimators of density function at an unknown point.

• Journal of applied mathematics & informatics
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• 제15권1_2호
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• pp.147-158
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• 2004

Sufficient conditions are given under which a generalized class of kernel-type estimators allows asymptotic approximation on the modulus of continuity. This generalized class includes sample distribution function, kernel-type estimator of density function, and an estimator that may apply to the censored case. In addition, an application is given to asymptotic normality of recursive density estimators of density function at an unknown point.