• 한국해양학회지
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• 제16권2호
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• pp.99-107
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• 1981

A partial differential equation for the adjusted sea level, obtained from the long wave equations in shallow water, is reduced to a simpler one by the use of physically reasonable approximations based on the observations. The similar equation for the stream function indicates that shelf waves are generated by the longshore wind stress. This indication is in good agreement with the high correlation between the adjusted sea levels and the longshore wind stress. From the dispersion relationship and the boundary conditions, there exist a countable infinite number of modes which satisfy a first-order wave equations. The adjusted sea level for a given wind stress can easily be calculated by utilizing the convolution and the Fourier transformation. Some detailed solutions are presented here for sinusoidal and exponential wind stress.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• 대한전기학회논문지
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• 제32권7호
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• pp.234-238
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• 1983

이 논문은 단일 전하로 전달되는 이중(금속-반도체-금속) 접착 소자에서 일차원적인 수송 방정식을 정상 상태에서 마이크로 컴퓨터로 해를 구하였다. 수송방정식을 해석적으로 풀이 하기 위해 일반적으로 행하여왔던 대부분의 가정과 개략치는 본 논문에서는 배제하였다. 결과는 에너지 상태, 밀도상태, 전류-전압 특성등에 관하여 중점을 두엇다. 인가 전압의 함수로 나타낸 미분 정전 용얄의 컴퓨터에 의한 해를 제시히고 영상전하 효과로 수정법도 제시 하였다.

• 전기학회논문지
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• 제56권9호
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• pp.1549-1555
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• 2007

The complexity of modern power systems often makes it impractical to model it in its entirety for electromagnetic transient studies. Therefore areas outside the immediate area of interest must be represented by some form of Frequency Dependent Network Equivalent (FDNE). The advantage of using z-domain fitting is that it can be directly implemented in a digital simulation program without any loss of accuracy. Fitting in the s-domain always requires "discretizing" a continuous system and the inherent approximations. This paper presents z-domain rational function formulation and demonstrates the use of it for the assessment of the transient response of the Lower South Island of New Zealand. Moreover by using a well publicized test system and providing complete information on the developed FDNE coefficients other researchers easily benchmark their work against this.

• 한국유체기계학회 논문집
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• 제12권3호
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• pp.7-12
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• 2009

In this paper, shape optimization based on three-dimensional flow analysis has been performed for impeller design of centrifugal compressor. To evaluate the objective function of an isentropic efficiency, Reynolds-averaged Navier-Stokes equations are solved with SST (Shear Stress Transport) turbulence model. The governing equations are discretized by finite volume approximations. The optimization techniques based on the radial basis neural network method are used for the optimization. Latin hypercube sampling as design of experiments is used to generate thirty design points within design space. Sequential quadratic programming is used to search the optimal point based on the radial basis neural network model. Four geometrical variables concerning impeller shape are selected as design variables. The results show that the isentropic efficiency is enhanced effectively from the shape optimization by the radial basis neural network method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.166-171
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• 2004

In this study, shape optimization of micro-static mixer with a cantilever beam was accomplished for mixing the mixing efficiency by using successive response surface approximations. Variables were chosen as the length of cantilever beam and the angle between horizontal and the cantilever beam. Sequential approximate optimization method was used to deal with both highly nonlinear and non-smooth characteristics of flow field in a micro-static mixer. Shape optimization problem of a micro-static mixer can be divided into a series of simple subproblems. Approximation to solve the subproblems was performed by response surface approximation, which does not require the sensitivity analysis. To verify the reliability of approximated objective function and the accuracy of it, ANOVA analysis and variables selection method were implemented, respectively. It was verified that successive response surface approximation worked very well and the mixing efficiency was improved very much comparing with the initial shape of a micro-static mixer.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.527-538
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• 2019

This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

• Structural Engineering and Mechanics
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• 제24권5호
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• pp.529-541
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• 2006

This work gives an application of stochastic techniques for the optimization of stiffened plates in vibration. The search strategy consists of substituting, for finite element calculations in the optimization process, an approximate response from a Rayleigh-Ritz method. More precisely, the paper describes the use of a Rayleigh-Ritz method in creating function approximations for use in computationally intensive design optimization based on genetic algorithms. Two applications are presented; their deal with the optimization of stiffeners on plates by varying their positions, in order to maximize some natural frequencies, while having well defined dimensions. In other words, this work gives the fundamental idea of using a Ritz approximation to the response of a plate in vibration instead of finite element analysis.

• Structural Engineering and Mechanics
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• 제30권1호
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• pp.119-129
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• 2008

A small strain and elastoplastic formulation of Polygonal Element Method (PEM) is developed for efficient analysis of elastoplastic solids. In this work, the polygonal elements are constructed based on traditional triangular finite meshes. The construction method of polygonal mesh can directly utilize the sophisticated triangularization algorithm and reduce the difficulty in generating polygonal elements. The Wachspress rational finite element basis function is used to construct the approximations of polygonal elements. The incremental variational form and a von Mises type model are used for non-linear elastoplastic analysis. Several small strain elastoplastic numerical examples are presented to verify the advantages and the accuracy of the numerical formulation.

• ETRI Journal
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• 제35권6호
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• pp.1168-1171
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• 2013

We propose an improvement of particle swarm optimization (PSO) based on the stabilization of particle movement (PM). PSO uses a stochastic variable to avoid an unfortunate state in which every particle quickly settles into a unanimous, unchanging direction, which leads to overshoot around the optimum position, resulting in a slow convergence. This study shows that randomly located particles may converge at a fast speed and lower overshoot by using the proportional-integral-derivative approach, which is a widely used feedback control mechanism. A benchmark consisting of representative training datasets in the domains of function approximations and pattern recognitions is used to evaluate the performance of the proposed PSO. The final outcome confirms the improved performance of the PSO through facilitating the stabilization of PM.