• Korean Journal of Computational Design and Engineering
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• v.4 no.1
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• pp.32-42
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• 1999

Approximate lofting or skinning is one of practical surface modeling techniques well used in CAD and reverse engineering applications. Presented in this paper is a method for approximately lofting a given set of curves wihin a specified tolereance. It is based on refitting input curves simultaneously on a common knot vector and interpolating them to get a resultant NURBS surface. A concept of reducing the number of interior knots of the common knot vector is well adopted to acquire more compact representation for the resultant surface. Energy minimization is newly introduced in curve refitting process to stabilize the solution of the fitting problem and get more fair curve. The proposed approximate lofting provides more smooth surface models and realizes more efficient data reduction expecially when the parameterization and compatibility of input curves are not good enough. The method has been successfully implemented in a new CAD/CAM product VX Vision? of Varimetrix Corporation.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.11B no.3
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• pp.59-63
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• 2001

This paper presents a new modeling of the two dimensional magnetic property in soft magnetic materials for the magnetic field computations. In this modeling an approximate treatment is introduced to expand the applicable exciting frequency range. The result shows that the new modeling presented here is very useful in simplicity of magnetic field analysis.

• Journal of Mechanical Science and Technology
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• v.15 no.8
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.231-238
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• 2002

As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.

• The Journal of the Acoustical Society of Korea
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• v.18 no.4
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• pp.71-77
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• 1999

Exact forward modeling of acoustic propagation is crucial in MFP such as inverse problems and various other acoustic applications. As acoustic propagation in shallow water environments become important, range dependent modeling has to be considered of which PE method is considered as one of the most accurate and relatively fast. In this paper higher order numerical rode employing the PE method is developed. To approximate the depth directional operator, Galerkin's method is used with partial collocation to lessen necessary calculations. Linearization of tile depth directional operator is achieved via expansion into a multiplication form of (equation omitted) approximation. To approximate the range directional equation, Crank-Nicolson's method is used. Final1y, numerical self stater is employed. Numerical tests are performed for various occan environment scenarios. The results of these tests are compared to exact solutions, OASES and RAM results.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.117-122
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• 1994

In this paper, we discuss the modeling of flexible manipulators. In the modeling of flexible manipulators, there are two approaches: one is based on the distributed-parameter modeling and the other on the lumped-parameter modeling. The former has been applied to control and analysis of simple manipulator requiring precision, while the latter has been applied to multi-link spatial manipulator, because of the model's simplicity. We have already proposed the lumped-parameter modeling method for simple manipulator, and investigate that model of how much degree of precision we can get. The experiments and simulations are performed, comparing these results, the approximate performance of our modeling method is discussed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.979-983
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• 2001

We develop a new method of simultaneous optimization of trajectory and shape of redundant flexible manipulators for collision-free utilizing the B-spline function and a mathematical programming method We adopt an approximate flexible manipulator model which consists of rigid bar elements and spring elements. We use B-spline function for determining the approximate trajectory and the expressions of the outline of obstacles. The used total performance index consists of 2 performance indices. The first is the driving energy, and the second is the trajectory deviation which is caused by the approximate modeling for the flexible manipulator. We design optimal collision-free trajectory of flexible manipulators by searching optimum positions of the control points for B-spline approximation which minimize the performance index subject to constraint condition for collision-free. Some examinations through numerical examples show the effectiveness of the method

• Journal of Mechanical Science and Technology
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• v.17 no.5
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• pp.698-707
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• 2003

A new quadratic response surface modeling method is presented. In this method, the incomplete small composite design (ISCD) is newly proposed to .educe the number of experimental runs than that of the SCD. Unlike the SCD, the proposed ISCD always gives a unique design assessed on the number of factors, although it may induce the rank-deficiency in the normal equation. Thus, the singular value decomposition (SVD) is employed to solve the normal equation. Then, the duality theory is used to newly develop the conservative least squares fitting (CONFIT) method. This can directly control the ever- or the under-estimation behavior of the approximate functions. Finally, the performance of CONFIT is numerically shown by comparing its'conservativeness with that of conventional fitting method. Also, optimizing one practical design problem numerically shows the effectiveness of the sequential approximate optimization (SAO) combined with the proposed ISCD and CONFIT.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.25-33
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• 1999

Exchanging parametric curves and surfaces between different geometric modeling systems often require degree reductions to approximate the curves and surfaces to the degree of supporting systems within the given tolerance. This paper is a research for approximate conversion of a degree reduction methods for Bezier curves in the data exchange between the different systems. Our approximate conversion is implemented that shows the experimental results with the others to reduce the degree from the given degree n to n-1 for the Bezier curves about the different degree reductions.