• Structural Engineering and Mechanics
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• 제71권5호
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• pp.485-502
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• 2019

In this research, the nonlinear static, buckling and vibration analysis of viscoelastic micro-composite beam reinforced by various distributions of boron nitrid nanotube (BNNT) with initial geometrical imperfection by modified strain gradient theory (MSGT) using finite element method (FEM) are presented. The various distributions of BNNT are considered as UD, FG-V and FG-X and also, the extended rule of mixture is used to estimate the properties of micro-composite beam. The components of stress are dependent to mechanical, electrical and thermal terms and calculated using piezoelasticity theory. Then, the kinematic equations of micro-composite beam using the displacement fields are obtained. The governing equations of motion are derived using energy method and Hamilton's principle based on MSGT. Then, using FEM, these equations are solved. Finally the effects of different parameters such as initial geometrical imperfection, various distributions of nanotube, damping coefficient, piezoelectric constant, slenderness ratio, Winkler spring constant, Pasternak shear constant, various boundary conditions and three material length scale parameters on the behavior of nonlinear static, buckling and vibration of micro-composite beam are investigated. The results indicate that with an increase in the geometrical imperfection parameter, the stiffness of micro-composite beam increases and thus the non-dimensional nonlinear frequency of the micro structure reduces gradually.

• 기업가정신과 벤처연구
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• 제19권4호
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• pp.41-52
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• 2016

본 연구에서는 중소기업의 상장시장인 KOSDAQ에서 신규공모(initial public offering) 인수회사에게 부과하는 공모주 매각제한제도(underwriter lockup)와 공모주 상장초기 수익률의 관계를 분석하였다. 2008년부터 KOSDAQ에 상장된 공모주의 상장초기 수익률 평균은 감소하였으며, 일부 공모주 상장초기 수익률은 음이었다. 이런 환경에서 KOSDAQ는 매각제한제도를 실행하였는데, 매각제한제도란 신규공모 인수회사가 공모주를 발행가격으로 매입하고 상장부터 3개월간 보유하는 의무를 의미한다. 따라서 매각제한제도에서 인수회사는 공모주를 저가(underpricing)로 매입하고 고가(stabilization)로 매각하려는 의도를 가지게 될 것이므로, 매각제한제도에서 상장초기 수익률이 증가할 수가 있다. 본 연구에서는 2009년부터 KOSDAQ에 상장된 공모주에 관한 자료들을 수집하고, 매각제한제도에 의해서 상장초기 수익률을 증가시키는지를 혼합분포(mixture of distributions) 관점에서 검증하였다. 분석결과 매각제한제도가 적용되지 않는 신규공모보다는 매각제한제도가 적용되는 신규공모에서 공모주 상장초기수익률은 유의적으로 증가하였다. 그리고 이런 결과는 상장초기 수익률에 영향을 주는 요인들을 통제하더라도 유의적으로 존재하였다. 이것은 매각제한제도가 상장초기 수익률의 상승에 매우 유효한 제도라는 것을 의미한다.

• Structural Engineering and Mechanics
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• 제74권1호
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• pp.55-67
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• 2020

The common practice to predict the characteristic structural load effects (LEs) in long reference periods is to employ the extreme value theory (EVT) for building limit distributions. However, most applications ignore that LEs are driven by multiple loading events and thus do not have the identical distribution, a prerequisite for EVT. In this study, we propose the composite extreme value modeling approach using clustering to (a) cluster initial blended samples into finite identical distributed subsamples using the finite mixture model, expectation-maximization algorithm, and the Akaike information criterion; (b) combine limit distributions of subsamples into a composite prediction equation using the generalized Pareto distribution based on a joint threshold. The proposed approach was validated both through numerical examples with known solutions and engineering applications of bridge traffic LEs on a long-span bridge. The results indicate that a joint threshold largely benefits the composite extreme value modeling, many appropriate tail approaching models can be used, and the equation form is simply the sum of the weighted models. In numerical examples, the proposed approach using clustering generated accurate extrema prediction of any reference period compared with the known solutions, whereas the common practice of employing EVT without clustering on the mixture data showed large deviations. Real-world bridge traffic LEs are driven by multi-events and present multipeak distributions, and the proposed approach is more capable of capturing the tendency of tailed LEs than the conventional approach. The proposed approach is expected to have wide applications to general problems such as samples that are driven by multiple events and that do not have the identical distribution.

• 응용통계연구
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• 제19권1호
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• pp.1-12
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• 2006

본 논문에서는 2002년에 (사)볼런티어21에서 실시한 설문조사 자료를 이용하여 2001년에 우리나라 개인들이 기부한 횟수에 영향을 주는 유의한 변수들을 식별하였다. 기부횟수의 경험적 분포로 미루어 모집단은 기부를 적게 하는 집단과 많이 하는 집단으로 구성되며 따라서 모집단 분포를 두개 포아송 분포의 혼합분포로 모형화하였다. 이 모형에 기초하여 기부횟수에 영향을 미치는 변수들을 식별하였다. EM알고리즘을 이용하여 모수를 추정하고 2.5%와 97.5%에 기초한 백분위수 신뢰구간을 보완한 BCa(bias-corrected and accelerated) 신뢰구간을 계산하여 유의한 변수들을 찾았다. 연구결과 혼합 포아송 회귀모형에서는 기부횟수가 적은 집단("작은 군")과 기부횟수가 많은 집단("큰 군") 모두에서 소득과 자원봉사의 경험 유무(1:예, 0:아니오)가 기부횟수에 유의적으로 영향을 주는 변수로 밝혀졌다. 또한 두 변수 각각에서 회귀계수가 양수로 나타나 소득이 많을수록, 혹은 자원봉사의 경험이 있는 사람일수록 기부횟수가 증가하는 것을 알 수 있다. 그러나 소득과 자원봉사 변수의 회귀계수는 "작은 군"이 "큰 군"에 비해 더욱 크게 나타나고 있다. "작은 군"보다 "큰 군"의 사람들에게 기부가 생활화되어 있고, 따라서 소득과 자원봉사의 경험 유무가 기부횟수에 미치는 영향이 상대적으로 적은 것으로 파악된다.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.289-298
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• 1998

We investigate the distribution of likelihood ratio test(LRT) of null hypothesis a sample is from single gamma with unknown shape and scale against the alternative hypothesis a sample is from a mixture of two gammas, each with unknown scale and unknown (but equal) scale. To obtain stable maximum likelihood estimates(MLE) of a mixture of two gamma distributions, the EM(Dempster, Laird, and Robin(1977))and Modified Newton(Jensen and Johansen(1991)) algorithms were implemented. Based on EM, we made a simple structure likelihood equation for each parameter and could obtain stable solution by Modified Newton Algorithms. Simulation study was conducted to investigate the distribution of LRT for sample size n = 25, 50, 75, 100, 50, 200, 300, 400, 500 with 2500 replications. To determine the small sample distribution of LRT, I considered the model of a gamma distribution with shape parameter equal to 1 + f(n) and scale parameter equal to 2. The simulation results indicate that the null distribution is essentially invariant to the value of the shape parameter. Modeling of the null distribution indicates that it is well approximated by a gamma distribution with shape parameter equal to the quantity $0.927+1.18/\sqrt{n}$ and scale parameter equal to 2.16.

• 응용통계연구
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• 제22권4호
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• pp.759-770
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• 2009

본 논문은 신호와 잡음이 혼합된 관측치로부터 신호 관측치를 분류하는 문제를 다룬다. 잡음은 가우시안 분포를 따르고 신호는 감마 분포를 따른다고 가정할 때 관측치의 분포는 가우시안과 감마의 혼합 분포를 따르게 된다. EM 알고리즘을 통해 혼합 모델의 모수를 추정하고 신호 및 잡음을 분류하는 것을 다중 가설 검정으로 간주하여 베이즈 오류를 바탕으로 분류를 위한 경계치를 설정한다. 제안하는 방법을 분광 데이터에 근거하여 철강 제품에서 개재물 유무를 검출하는 문제에 적용하였고 별도의 시뮬레이션 데이터를 통해 성능의 우수성을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 하계학술대회 논문집
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• pp.154-157
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• 1990

This paper describes a new method of calculating expected energy generation and loss of load probability (L.O.L.P) for electric power system operation and expansion planning. The method represents an equivalent load duration curve (E.L.D.C) as a mixture of cumulants approximation (M.O.C.A), which is the general case of mixture of normals approximation (M.O.N.A). By regarding a load distribution as many normal distributions-rather than one normal distribution-and representing each of them in terms of Gram-Charller expansion, we could improve the accuracy of results. We developed an algorithm which automatically determines the number of distribution and demarcation points. In modelling of a supply system, we made subsets of generators according to the number of generator outage: since the calculation of each subset's moment needs to be processed rapidly, we futher developed specific recursive formulae. The method is applied to the test systems and the results are compared with those of cumulant, M.O.N.A and Booth-Baleriaux method. It is verified that the M.O.C.A method is faster and more accurate than any other methods.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.241-250
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• 2010

원형자료에 대한 모형화 분석은 주로 von Mises 분포를 비롯한 대칭형의 경우를 중심으로 많은 연구가 이루어져 왔다. 최근 선형자료의 분석에서 다양한 비대칭의 자료에 적합한 왜정규분포의 활용에 대한 연구가 활발히 수행되고 있다. 본 논문에서는 Pewsey (2000a)에 의해 처음 소개된 겹친왜정규분포를 이용한 비대칭의 원형자료에 대한 적합을 다루었다. 특히 비대칭 다봉형 원형자료의 적합을 위해 겹친왜정규혼합분포를 제안하고, EM 알고리즘을 통한 모수추정 과정을 제시하였다. 모의실험을 통해 EM 알고리즘을 통한 모수추정의 정확성을 확인하고, 실제 지방국도의 일일교통량 자료의 모형화 분석에 적용하였다.

• Journal of information and communication convergence engineering
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• 제11권2호
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• pp.95-102
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• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.297-309
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• 2021

In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.