• 한국멀티미디어학회논문지
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• 제11권4호
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• pp.525-543
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• 2008

최근 들어 소프트웨어 개발에 있어서 사용자 인터페이스가 차지하는 비중이 급증하고 있다. 이로 인해 스윙, MFC, Web 2.0 등과 같은 다양한 사용자 인터페이스 개발 관련 기술들이 소개되고 있다. 그러나 현재 대부분의 소프트웨어 개발에 있어서는 사용자 인터페이스 부분과 비즈니스 부분을 별도로 개발하는 프로세스로 진행되고 있다. 이로 인해 통합 과정에서의 어려움, 개발 기간의 연장, 개발 모델의 재사용성의 저하등과 같은 문제점들이 발생하고 있다. 즉, UI 모델링이 체계적이고 계층적이지 못하며, UI 모델링과 비즈니스 모델링간의 일관성있는 통합 기법을 제공치 않아, 구축 모델의 확장성과 재사용성의 저하를 초래하고 있다. 본 논문은 이를 해결하기 위해 개발 단계의 추상화 수준에 따른 계층적 메타모델을 사용해서 단일화되고 체계적인 UML 기반의 사용자 인터페이스 모델링 프로세스를 제시한다. 이를 위해, 개발의 성숙도에 준거하여 PIM/PSM으로 UI 모델의 모델링 요소를 계층화하여 메타모델을 제시한다. 3 단계 모델링(개념/명세/상세)에 의해 UI 및 비즈니스 메타모델을 적용해서 UI 모델링과 비즈니스 모델링이 통합된 계층적인 모델링 프로세스를 정립한다. 제시한 프로세스를 인터넷 쇼핑몰에 적용해 봄으로써 실효성을 제시한다. 실험 결과를 통해 계층적 UI 메타모델 및 프로세스가 체계적이고 계층적인 UI 모델을 구축할 수 있다. 이는 모델의 품질과 재 사용성을 향상시킬 수 있었다.

• 한국자동차공학회논문집
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• 제21권5호
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• pp.8-14
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• 2013

In this study, we propose the method of optimally selecting material of front pillar (A-pillar) and package tray for minimizing weight while satisfying vehicle requirements on static stiffness and dynamic stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness and dynamic stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the radial basis function regression (RBFr). Using the RBFr models, optimization is carried out an evolutionary algorithm that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 49.8% while satisfying all the design constraints.

• 정보처리학회논문지D
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• 제11D권3호
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• pp.577-590
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• 2004

오늘날 소프트웨어 산업의 발전은 소프트웨어 패키지 제품들의 양적 팽창을 이루게 하고 있다. 이러한 급속한 소프트웨어 패키지 제품의 증가 추세에 따라서, 사용자가 선택하는 소프트웨어 제품에 대한 품질 인증 요구가 대두되었다. 공산품의 품질 인증과는 달리, 소프트웨어 제품의 경우 아직 인증 역사가 길지 않고 이를 위한 소프트웨어 품질 평가 및 인증 방법 연구는 성숙되지 않았다. 소프트웨어 제품 인증 업무 시 중요한 요소 중의 하나가 평가 집합의 체계적인 생성이다. 평가 집합이란 소프트웨어 제품 유형의 분류에 따라서 소프트웨어 품질 인증을 위한 기준과 메트릭을 명시한 체크리스트를 포함한다. 본 논문에서는 평가 집합의 체계적 생성 및 유지 관리를 위한 베타 모델을 제안한다. 그리고 메타 모델의 유효성을 확인하기 위하여 프로토타입 수준의 평가 집합을 생성한다

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.501-506
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• 2001

Computer modeling is essential to evaluate possible design of suspension for a railway vehicles. By creating a simulation, the engineers are able to assess the feasibility of a given design and change the design factors to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have turned to surrogate modeling. A surrogate model is essentially a regression performed on a data sampling of the simulation. In the most general sense, metamodels(surrogate model) take the form $y(x)=f(x)+{\varepsilon}$, where y(x) is the true simulation output, f(x) is the metamodel output, and $\varepsilon$ is the error between the two. In this paper, a second order polynomial equation is partially used as a metamodel to represent the forty-six dynamic performances for high speed train. The number of factors as design variables of the metamodel is twenty-nine, which are composed the dynamic characteristics of suspension. This metamodel is used to search the optimum values of suspension characteristics which minimize the dynamic responses for high speed train. This optimization is a multi-objective problem which have many design variables. This paper shows that the response surface model which is made through the design of analysis of computer experiments method is very efficient to solve this complex optimization problem.

• 대한기계학회논문집A
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• 제33권10호
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• 대한기계학회논문집A
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• 제30권8호
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• pp.985-992
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• 2006

Many methods for reliability analysis have been studied and one of them, a moment method, has the advantage that it doesn't require sensitivities of performance functions. The moment method for reliability analysis requires the first four moments of a performance function and then Pearson system is used for the probability of failure where the accuracy of the probability of failure greatly depends on that of the first four moments. But it is generally impossible to assess them analytically for multidimensional functions, and numerical integration is mainly used to estimate the moment. However, numerical integration requires many function evaluations and in case of involving finite element analyses, the calculation of the first fo 따 moments is very time-consuming. To solve the problem, this research proposes a new method of approximating the first four moments based on kriging metamodel. The proposed method substitutes the kriging metamodel for the performance function and can also evaluate the accuracy of the calculated moments adjusting the approximation range. Numerical examples show the proposed method can approximate the moments accurately with the less function evaluations and evaluate the accuracy of the calculated moments.

• 대한기계학회논문집A
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• 제33권12호
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• pp.1464-1470
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• 2009

RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.

• 대한기계학회논문집A
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• 제31권1호
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• pp.55-61
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• 2007

Recently simulation model becomes an essential tool for analysis and design of a system but it is often expensive and time consuming as it becomes complicate to achieve reliable results. Therefore, high-fidelity simulation model needs to be replaced by an approximate model, the so-called metamodel. Metamodeling techniques include 3 components of sampling, metamodel and validation. Cross-validation approach has been proposed to provide sequnatially new sample point based on cross-validation error but it is very expensive because cross-validation must be evaluated at each stage. To enhance the cross-validation of metamodel, sequential sampling method using candidate points and representative cross-validation is proposed in this paper. The candidate and representative cross-validation approach of sequential sampling is illustrated for two-dimensional domain. To verify the performance of the suggested sampling technique, we compare the accuracy of the metamodels for various mathematical functions with that obtained by conventional sequential sampling strategies such as maximum distance, mean squared error, and maximum entropy sequential samplings. Through this research we team that the proposed approach is computationally inexpensive and provides good prediction performance.

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.641-649
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• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3359-3366
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• 2021

An engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass, heat energy from the canisters is released to the surrounding buffer material. It is vital that this heat energy is rapidly dissipated to the near-field rock mass, and thus the thermal conductivity of the buffer is a key parameter to consider when evaluating the safety of the overall disposal system. Therefore, to take into consideration the sizeable amount of heat being released from such canisters, this study investigated the thermal conductivity of Korean compacted bentonites and its variation within a temperature range of 25 ℃ to 80-90 ℃. As a result, thermal conductivity increased by 5-20% as the temperature increased. Furthermore, temperature had a greater effect under higher degrees of saturation and a lower impact under higher dry densities. This study also conducted a regression analysis with 147 sets of data to estimate the thermal conductivity of the compacted bentonite considering the initial dry density, water content, and variations in temperature. Furthermore, the Kriging method was adopted to establish an uncertainty metamodel of thermal conductivity to verify the regression model. The R² value of the regression model was 0.925, and the regression model and metamodel showed similar results.