• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.335-335
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• 2018

최근 이상기후현상으로 지구상의 여러 지역에서 극치 수문 사상의 발생 빈도와 강도가 날로 증가하고 있는 추세이다. 이에 대해 수공구조물의 설계를 위한 극치강우사상의 빈도해석에 있어서 적절한 확률분포모형의 적용은 매우 중요하다. 이에 수문통계분야에서는 generalized extreme value(GEV), generalized logistic(GLO), Gumbel(GUM) 모형과 같은 극치 분포를 이용한 수문통계적 특성에 대한 접근이 주로 이루어지고 있다. 하지만 우리나라 강우 사상의 경우 GEV 분포와 GUM 분포가 비교적 적합한 것으로 알려져 있지만 하나의 형상매개변수를 가지고 있어 분포 모형이 표현할 수 있는 통계적 특성에 한계를 가지고 있다. 기존의 GEV나 GUM분포로는 적절히 재현되지 않는 자료들을 분석하기 위해서 두 개의 형상매개변수를 가지는 분포형에 대한 연구가 진행되고 있다. 이에 본 연구에서는 두 개의 형상매개변수를 가지는 Burr XII 분포형의 우리나라 극한 강우자료에 대한 적용성을 평가하였다. Burr XII 분포형은 gamma나 exponential 분포 모형처럼 양의 확률변수만을 가지고, Cauchy나 Pareto 분포 모형처럼 두꺼운 꼬리(heavy-tailed distribution) 형상을 나타내기 때문에 비교적 큰 확률변수가 빈번히 나타나는 극치사상에도 적합한 것으로 알려져 있다. 이를 위해 Burr XII 분포 모형을 이용하여 우리나라 강우자료에 대해 지점빈도해석 및 지역빈도해석을 수행하고 우리나라 강우자료에 비교적 적합하다고 알려진 분포인 GEV, GLO, GUM 분포형을 통해 산정된 결과와 비교하였다.

• Structural Engineering and Mechanics
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• v.86 no.4
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• pp.535-546
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• 2023

In the present paper, multiple interface cracks between a functionally graded orthotropic coating and an orthotropic half-plane substrate under concentrated loading are considered by means of the distribution dislocation technique (DDT). With the use of integration of Fourier transform the problem is reduced to a system of Cauchy-type singular integral equations which are solved numerically to compute the dislocation density on the surfaces of the cracks. The distribution dislocation is a powerful method to calculate accurate solutions to plane crack problems, especially this method is very good to find SIFs for multiple unequal cracks located at the interface. Hence this technique allows considering any number of interface cracks. The primary objective of this paper is to investigate the effects of the interaction of multiple interface cracks, load location, material orthotropy, nonhomogeneity parameters and geometry parameters on the modes I and II SIFs. Numerical results show that modes I/II SIFs decrease with increasing the nonhomogeneity parameter and the highest magnitude of SIF occurs where distances between the load location and crack tips are minimal.

• The Korean Journal of Rheology
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• v.11 no.1
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• pp.18-27
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• 1999

Predicting the deformation behaviors of sheets in thermoforming processes has been a daunting challenge due to the strong nonlinearities arising from very large deformations, mold-polymer contact condition and hyperelasticity constitutive equations. Nonlinear numerical analysis is always required to face this challenge especially for realistic processing conditions. In this study a 3-D algorithm and the membrane approximation are developed for thermoforming processes. The constitutive equation is expressed in terms of the 2nd Piola-Kirchhoff stress tensor and the Cauchy-Green deformation tensor. The 2-term Mooney-Rivlin model is used for the material model equation. The algorithm is established by the finite element formulation employing the total Lagrangian coordinate. The deformation behavior and the stress distribution results of 3-D algorithm with various point boundary conditions are compared to those of the membrane approximation algorithm. Also, the slip boundary condition and the no-slip boundary condition are applied for the systems that have molds. Finally, the effect of sheet temperatures on the final thickness distribution is investigated for the ABS material.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1120-1130
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• 2018

An analysis of a pyroprocessing safeguards methodology employing the Pu-to-$^{244}Cm$ ratio is presented. The analysis includes characterization of representative used nuclear fuel assemblies with respect to computed nuclide composition. The nuclide composition data computationally generated is appropriately reformatted to correspond with the material conditions after each step in the head-end stage of pyroprocessing. Uncertainty in the Pu-to-$^{244}Cm$ ratio is evaluated using the Geary-Hinkley transformation method. This is because the Pu-to-$^{244}Cm$ ratio is a Cauchy distribution since it is the ratio of two normally distributed random variables. The calculated uncertainty of the Pu-to-$^{244}Cm$ ratio is propagated through the mass flow stream in the pyroprocessing steps. Finally, the probability of Type-I error for the plutonium Material Unaccounted For (MUF) is evaluated by the hypothesis testing method as a function of the sizes of powder particles and granules, which are dominant parameters to determine the sample size. The results show the probability of Type-I error is occasionally greater than 5%. However, increasing granule sample sizes could surmount the weakness of material accounting because of the non-uniformity of nuclide composition.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.693-702
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• 2019

Deep learning has gained popularity for the classification and prediction task. Neural network layers become deeper as more data becomes available. Saturation is the phenomenon that the gradient of an activation function gets closer to 0 and can happen when the value of weight is too big. Increased importance has been placed on the issue of saturation which limits the ability of weight to learn. To resolve this problem, Glorot and Bengio (Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249-256, 2010) claimed that efficient neural network training is possible when data flows variously between layers. They argued that variance over the output of each layer and variance over input of each layer are equal. They proposed a method of initialization that the variance of the output of each layer and the variance of the input should be the same. In this paper, we propose a new method of establishing initialization by adopting truncated normal distribution and truncated cauchy distribution. We decide where to truncate the distribution while adapting the initialization method by Glorot and Bengio (2010). Variances are made over output and input equal that are then accomplished by setting variances equal to the variance of truncated distribution. It manipulates the distribution so that the initial values of weights would not grow so large and with values that simultaneously get close to zero. To compare the performance of our proposed method with existing methods, we conducted experiments on MNIST and CIFAR-10 data using DNN and CNN. Our proposed method outperformed existing methods in terms of accuracy.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.3-9
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• 2001

In this paper, a random number generator for simulation of speech-waveform coders was designed. A random number generator having a desired probability density function and a desired power spectral density is discussed and experimental results are presented. The technique is based on Sondhi algorithm which consists of a linear filter and a memoryless nonlinearity. Several methods of obtaining memoryless nonlinearities for some typical continuous distributions are discussed. Sondhi algorithm is analyzed in the time domain using the diagonal expansion of the bivariate Gaussian probability density function. It is shown that the Sondhi algorithm gives satisfactory results when the memoryless nonlinearity is given in an antisymmetric form as in uniform, Cauchy, binary and gamma distribution. It is shown that the Sondhi algorithm does not perform well when the corresponding memoryless nonlinearity cannot be obtained analytically as in Student-t and F distributions, and when the memoryless nonlinearity can not be expressed in an antisymmetric form as in chi-squared and lognormal distributions.