• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.544-546
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• 2000

이 논문에서는 CAGD 및 기하모델링 분야에서 최근 발표된 Universal Parametrization의 계산적 또는 응용적 특성을 고찰하고자 한다. Universal Parametrization을 이용하여 구한 B-spline의 곡선이나 곡면에 아주 자연스러운 특성을 가지고 있다. 뿐만 아니라, 다른 매개변수(Parametrization)을 이용하는 경우, 점들의 기하학적 분포나 곡선/곡면의 차수에 따라 결과의 차이가 심한 경우가 있으나 새로운 방법은 B-spline이라는 기초함수의 특성을 고려한 매개변수법이므로 이러한 결과의 차이를 최대한 줄이는 특성이 있다. 또한 점 데이터에 관해서 Affine Invariant하고 Semi-localness의 특성을 보인다. 이외에도, 계산적인 관점에서 강인성을 보유하고 있고, 많은 응용분야에서 쉽게 자유곡선이나 자유곡면 모델링을 할 수 있도록 한다. 한 예로, 3D 다각형 메쉬로부터 B-spline을 이용한 자유곡면 모델을 구하는 소프트웨어 툴을 설명한다.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.4
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• pp.127-138
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• 1997

In this study, a new technique is presented, by which one can define ship hull form with full fairness from the input data of lines. For curve modeling, the de Casteljau Algorithm and Bezier control points are used to express free curves and to establish the unified curve modeling technique which enables one to convert non-uniform B-spline (NUB) curve or cubic spline curve into composite Bezier curves. For surface modeling, the mesh curve net which is required to define surface of ship hull form is interpolated by the method of the unified curve modeling, and the boundary curve segments of Gregory surface patches are generated by remeshing(rearranging) the given mesh curve net. From these boundary information, composite Gregory surfaces of good quality in fairness can be formulated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.246-249
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• 2007

In numerical analysis for phase change material, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be used without modelling. In this study applicability of neural networks in modelling superheated vapor region of water was examined by comparing with the quadratic spline. neural network consists of an input layer with 2 nodes, two hidden layers and an output layer with 3 nodes. Quadratic spline interpoation method was also applied for comparison. Neural network model revealed smaller percentage error to quadratic spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the superheated range of the steam table.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.45-50
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• 1998

This paper presents the process generating the 3-dimensional free f o r m hull form by using an ASMOD(Adaptive Spline Modeling of Observation Data) and a hybrid curve approximation. For example, we apply an ASMOD to the generation of a SAC(Sectiona1 Area Curve) in an initial hull form design. That is, we define SACS of real ships as B-spline curves by a hybrid curve approximation (which is the combination method of a B-spline fitting method and a genetic algorithm) and accumulate a database of control points. Then we let ASMOD learn from the correlation of principal dimensions with control points and make the ASMOD model for SAC generation. Identically, we apply an ASMOD to the generation of other hull form characteristic curves - design waterline curve, bottom tangent line, center profile line. Conclus~onally we can generate a design hull form from these hull form characteristic curves.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.685-690
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• 2008

In numerically evaluating the thermal performance of the heat exchanger, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be directly used without modelling. In this study the applicability of neural networks in modelling superheated water vapor was examined. The multi-layer neural networks consist of an input layer with 2 nodes, two hidden layers with 15 and 25 nodes respectively and an output layer with 3 nodes. Quadratic spline interpolation was also applied for comparison. Neural networks model revealed smaller percentage error compared with spline interpolation. From this result, it is confirmed that the neural networks could be a powerful method in modelling the superheated water vapor.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.391-400
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• 2002

본 연구는 3차원 컴퓨터 그래픽 데이터의 디지털 저작권 보호를 위하여 3차원 데이터에 저작권을 주장하는 워터마크를 삽입하고 원본 없이도 추출할 수 있는 방법을 제안한다. 본 연구에서는 벡터의 개념을 사용하는 3차원 모델 가운데서도 많이 사용되고 있는 Spline방식으로 모델링 된 그래픽 데이터에 정보를 삽입, 추출하는 기술로서 추출 시에는 원본을 필요로 하지 않는다는 것이 특징이다. 좌표자체를 변경하지 않기 때문에 정보를 삽입한 후 3차원 데이터의 양에 작은 변화가 있지만 시각적으로 구분이 안될 정도로 3차원 모델의 형태를 완벽히 유지한다. 또한 삽입된 정보가 쉽게 노출되지 않으며 많은 양의 데이터를 삽입할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.433-436
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• 2012

Today due to the development of technology, 3D graphics have become an essential element of the game graphic. When game companies create a game using 3D graphics, game users can enjoy a better game graphics like photo-realistic live action than 2D game graphics. And because the game companies have many advantages in creating games which are easy to deal with many basic effects and special effects, in video game business field, 3D game have become the mainstream. This paper will inquire the characteristics of 2D spline which is basic to various kinds of 3D modeling necessary to making 3D game graphics, compare and analyze the merits and demerits of each kind of spline and find out its development history.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2008-2013
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• 2006

In numerical analysis on evaluating the thermal performance of the thermal equipment, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table itself cannot be used without modelling. In this study applicability of neural networks in modelling the wide temperature range of wet saturated vapor region was examined. the multi-layer neural network consists of a input layer with 1 node, two hidden layers with 10 and 20 nodes respectively and a output layer with 6 nodes. Quadratic and cubic spline interpoations methods were also applied for comparison. Neural network model revealed similar percentage error to spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the wide range of the steam table.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.653-659
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• 2011

In general, electrocardiogram(ECG) signals are sampled with a frequency over 200Hz and stored for a long time. It is required to compress data efficiently for storing and transmitting them. In this paper, a method for compression of ECG data is proposed, using by Non Uniform B-spline approximation, which has been widely used to approximation theory of applied mathematics and geometric modeling. ECG signals are compressed and reconstructed using B-spline basis function which curve has local controllability and control a shape and curve in part. The proposed method selected additional knot with each step for minimizing reconstruction error and reduced time complexity. It is established that the proposed method using B-spline approximation has good compression ratio and reconstruct besides preserving all feature point of ECG signals, through the experimental results from MIT-BIH Arrhythmia database.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.8
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• pp.875-883
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• 1999

서로 다른 기하학적 모델링 시스템에 사용되는 곡선 및 곡면의 자료 교환에서, 시스템이 지원하는 그 곡선 및 곡면의 최대 차수에 제한이 있을 때, 낮은 차수로의 차수 감소가 필요하다. 본 논문에서는 근사 변환에 의한 B-spline 곡선의 차수 감소 방법을 제시한다. 기존의 Bzier 곡선의 차수감소 방법들을 적용하고, 그 방법들을 비교 분석한다. B-spline 곡선의 knot 제거 알고리즘이 자료 감소를 위해 차수 감소 과정에 적용된다.Abstract The degree reduction of B-splines is required in exchanging parametric curves and surfaces of the different geometric modeling systems because some systems limit the supported maximal degree. We propose an approximate degree reduction method of B-spline curves using the existing Bzier degree reduction methods. Knot removal algorithm is used to reduce data in the degree reduction process.