• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.317-323
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• 2017

Due to the unique characteristic of auxetic material, negative poisson's ratio, it has a variety of distinctive properties compared to conventional materials. Numerous researches have been conducted on the auxetic material in order to find out how to make auxetics. In this study, we analyzed triangular and rectangular patterned rotating rigid units using finite element method. Our purpose is to investigate the mechanical properties of the rotating rigid units and to show their auxetic behaviors. We studied the Poisson's ratio and the bulk modulus of the rotating rigid units depending on their unit cell sizes. The Poisson's ratio and the bulk modulus decreased as the number of unit cells increased. Also, when the geometry of the unit cell was changed, the tendency of the Poisson's ratio and the bulk modulus was also different from the previous case. The results of the Poisson's ratio and the bulk modulus referred that they were critically affected by the number of unit cells and the shape of unit cell.

• Journal of Biosystems Engineering
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• v.20 no.2
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• pp.133-140
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• 1995

The model of Poisson's ratio of the fruits was developed on the basis that the cylindrical fruits specimen became the barrel shape when it was being compressed. The model of the corrected creep compliance of the fruits was developed under considering the developed model of Poisson's ratio. Both of the Poisson's ratio and the corrected creep compliance of the samples showed the nonlinear viscoelastic behavior. Those models were a similar form, but their coefficients of the model were different, and these behaviors of the samples were well described by the nonlinear model as a function of the initial stress and time. Effects of storage condition and period on the Poisson's ratio of the samples were investigated, and comparisons between the corrected and the uncorrected creep compliance of the samples were made.

• Structural Engineering and Mechanics
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• v.19 no.6
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• pp.707-720
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• 2005

The bending stress formula that taking into account the transverse deformation is developed for plane-curved, untwisted isotropic beams subjected to loadings that result in deformations in the plane of curvature. In order to account the transverse Poisson contraction effect, a new constitutive relation between force resultants, moment resultants, mid-plane strains and deformed curvatures for a curved plate is derived in a $6{\times}6$ matrix form. This constitutive relation will provide the fundamental basis to the analyses of curved structures composing of isotropic or anisotropic materials. Then, the bending stress formula of a curved isotropic beam can be deduced from this newly developed curved plate theory. The stress predictions by the present analysis are compared to those by the analysis that neglected the Poisson contraction effect. The results show that the Poisson effect becomes more significant as the Poisson ratio and the curvature are getting larger.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.953-961
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• 2006

Let's say that we are given a k number of random variables following Poisson distribution that are individually dependent and which forms multivariate Poisson distribution. We particularly dealt with a method of creating random numbers that satisfies the covariance matrix, where the elements of covariance matrix are parameters forming a multivariate Poisson distribution. To create such random numbers, we propose a new algorithm based on the method reducing the number of parameter set and deal with its relationship to the Park et al.(1996) algorithm used in creating multivariate Bernoulli random numbers.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.153-158
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• 2000

Poisson 신뢰구간을 구하는 방법을 살펴보고 평균포함확률 측면에서 붓스트랩 신뢰 구간이 지니는 특징을 모의실험을 통하여 기존의 신뢰구간과 비교하였다.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.505-519
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• 2006

We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.13-19
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• 2004

This study shows the possible use of the Poisson process for the characterization of dry year return period and duration. For the analysis we used an annual precipitation data, which has been collected since 1911 in Seoul. The highest threshold for the application of the Poisson process was determined to be the mean-0.5standard deviation, and then the results from the Poisson process are compared with the observed. Especially, the Poisson process was found to reproduce the mean duration and return interval quite well and show the possibility of using the Poisson process for the drought analysis.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.47-56
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• 1998

Zero-Inflated Poisson models are mixed models of the Poisson and Bernoulli models. Recently Zero-Inflated Poisson distributions have been used frequently rather than previous Poisson distributions because the developement of industrial technology make few defects in manufacturing process. It is important that univariate Zero-Inflated Poisson distributions are extended to bivariate distributions to generalize the multivariate distributions. In this paper we proposed three types of the bivariate Zero-Inflated Poisson distributions and obtained these moments. We compared the three types of distributions by using the moments.

• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.481-493
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• 2016

The statistical analysis to the torrential rainfall data that is defined as a rainfall amount more than 80 mm/day is performed with Daegu and Busan rainfall data which is collected during 384 months. The number of occurrence of the torrential rainfall events can be simulated usually using Poisson distribution. However, the Poisson distribution can be frequently failed to simulate the statistical characteristics of the observed value when the observed data is zero-inflated. Therefore, in this study, Generalized Poisson distribution (GPD), Zero-Inflated Poisson distribution (ZIP), Zero-Inflated Generalized Poisson distribution (ZIGP), and Bayesian ZIGP model were used to resolve the zero-inflated problem in the torrential rainfall data. Especially, in Bayesian ZIGP model, a informative prior distribution was used to increase the accuracy of that model. Finally, it was suggested that POI and GPD model should be discouraged to fit the frequency of the torrential rainfall data. Also, Bayesian ZIGP model using informative prior provided the most accurate results. Additionally, it was recommended that ZIP model could be alternative choice on the practical aspect since the Bayesian approach of this study was considerably complex.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.2
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• pp.357-365
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• 2013

Fix $n-2$ elements $h_1,{\cdots},h_{n-2}$ of the quotient field B of the polynomial algebra $\mathbb{C}[x_1,x_2,{\cdots},x_n]$. It is proved that B is a Poisson algebra with Poisson bracket defined by $\{f,g\}=det(Jac(f,g,h_1,{\cdots},h_{n-2})$ for any $f,g{\in}B$, where det(Jac) is the determinant of a Jacobian matrix.