• 전산구조공학
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• 제11권1호
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• pp.171-178
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• 1998

점탄성감쇠기가 장치된 건물은 감쇠력과 강성이 증가하며 부가되는 감쇠력에 의하여 비고전적 감쇠시스템이 된다. 이러한 경우 비감쇠시스템에서 구한 고유값을 이용하여 감쇠행렬을 대각행렬로 변환할 수 없으므로 일반적으로 운동방정식을 2n크기 행렬의 1차 미분방정식 형태로 변환하여 해석하게 된다. 이러한 방법은 일반적인 고전적 감쇠시스템에 비해 복잡하므로 감쇠행렬의 비대각항을 무시하고 해석하는 방법이 이용되기도 한다. 본 논문에서는 이러한 근사적인 방법의 타당성과 이론적 근거를 검증하고 정해와 근사해법을 이용하여 3층 전단건물의 진동특성을 구하여 비교하였다. 결과에 따르면 부가되는 감쇠력이 작을 때는 근사해와 정해가 매우 근접하나 감쇠력이 커질수록 그 오차가 커지는 것으로 나타났다.

• 한국정밀공학회지
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• 제20권3호
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• 한국경영과학회지
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• 제30권1호
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• pp.161-176
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• 2005

In a situation that ordinal preferences on multiattribute weights are captured, we present two solution approaches: an exact approach and an approximate method. The former, an exact solution approach via interaction with a decision-maker, pursues the progressive reduction of a set of non-dominated alternatives by narrowing down the feasible attribute weights region. Subsequent interactive questions and responses, however, sometimes may not guarantee the best alternative or a complete rank order of a set of alternatives that the decision-maker desires to have. Approximate solution approaches, on the other hand, can be divided into three categories including surrogate weights methods, dominance value-based decision rules, and three classical decision rules. Their efficacies are evaluated in terms of choice accuracy via a simulation analysis. The simulation results indicate that a proposed hybrid approach, intended to combine an exact solution approach through interaction and a dominance value-based approach, is recommendable for aiding a decision making in a case that a final choice is seldom made at single step under attribute weights that are imprecisely specified beyond ordinal descriptions.

• 대한기계학회논문집A
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• 제29권6호
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• pp.842-851
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• 2005

In this paper, a new method for constrained optimization of noisy functions is proposed. In approximate optimization using response surface methods, if constraints have severe noise, the approximate feasible region defined by approximate constraints is apt to include some of the infeasible region defined by actual constraints. This can cause the approximate optimum to converge into the infeasible region. In the proposed method, the approximate optimization is performed with the approximate constraints shifted by their deviations, which are calculated using a diagonal quadratic response surface method. This can prevent the approximate optimum from converging into the infeasible region. To fit the objective and constraints into diagonal quadratic models, we select the center and 4 additional points along each axis of design variables as experimental points. The deviation of each function is calculated using the differences between the real and approximate function values at the experimental points. A sequential approximate optimization technique based on the trust region algorithm is adopted to manage approximate models. The proposed approach is validated by solving some design problems. The results of the problems show the effectiveness of the proposed method.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.231-232
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• 2002

Various methods that utilize erosion rate measurement of standard cannon, 155mm Howitzer M185, as reference, are being used to calculate erosion rate of an interested unknown cannon tubes. We know ten measured erosion values of the standard cannon from 391 rounds to 4.000. An approximate function fitting these value s is derived. The new erosion equation is also suggested and computer simulations arc presented.

• Journal of applied mathematics & informatics
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• 제8권1호
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• pp.151-164
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• 2001

In this paper, a new approach for finding the approximate solution of the Stokes problem is introduced. In this method the problem is transformed to an equivalent optimization problem. Then, by considering it as a distributed parameter control system, the theory of measure is used to approximate values of pressure are obtained by a finite difference scheme.

• 반도체디스플레이기술학회지
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• 제9권4호
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• pp.45-50
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• 2010

This paper explores a new approach for modeling of decision-making problems that involve uncertain, time-dependent and sequence-dependent processes which can be applied to semiconductor industry. In the proposed approach, which is based on probability theory, approximate sample paths are required to be specified by probability and statistic characteristics. Completely specified sample paths are seen to be elementary and fundamental outcomes of the related experiment. The proposed approach is suitable for modeling real processes more accurately. A case study is applied to a single item production planning problem with continuous and uncertain demand and the solution obtained by the approximate path specification method shows less computational efforts and practically desirable features. The application possibility and general plan of the proposed approach in semiconductor manufacturing process is also described in the paper.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.141-144
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• 2007

Given a number of training data, a traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper describes the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The modified BPN based meta-model is obtained by including the decision condition between lower/upper bounds of a constraint and an approximate value. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem.

• Industrial Engineering and Management Systems
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• 제7권1호
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• pp.1-8
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• 2008

Having previously presented an article entitled "Further approximate optimum inspection intervals" in this Journal, here the author derives an alternative set of general explicit formulae using Cardan's solution to a cubic equation and presents a modified heuristic algorithm for solving Baker's model. The examples show that this new alternative approximate solution procedure for determining near optimum inspection intervals is as accurate and computationally efficient as the one suggested in the previous article. Through the examples, the author also indicates the relative merits and demerits of the two algorithms.

• 한국CDE학회논문집
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• 제17권4호
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• pp.282-293
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• 2012

This paper presents an error-bounded B-spline fitting technique to approximate unorganized data within a prescribed error tolerance. The proposed approach includes two main steps: leastsquares minimization and error-bounded approximation. A B-spline hypervolume is first described as a data representation model, which includes its mathematical definition and the data structure for implementation. Then we present the least-squares minimization technique for the generation of an approximate B-spline model from the given data set, which provides a unique solution to the problem: overdetermined, underdetermined, or ill-conditioned problem. We also explain an algorithm for the error-bounded approximation which recursively refines the initial base model obtained from the least-squares minimization until the Euclidean distance between the model and the given data is within the given error tolerance. The proposed approach is demonstrated with some examples to show its usefulness and a good possibility for various applications.