• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2561-2563
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• 2003

본 논문은 비-스플라인(B-spline) 보간법을 이용한 영상의 변환에 대하여 논한다. 국소적인 영상의 정보나 세분화된 영상의 정보를 얻기 위해 영상의 확대 변환이 필요하다. 본 논문에서는 영상의 확대 변환을 위해 선형 (linear), 큐빅 (cubic), 인근치 (nearest neighbour)등의 보간법 [2]과 비-스플라인(B-Spline) 보간법[1][3][4]을 적용하였다. 실험을 통하여 비-스플라인 보간법이 현재 많이 사용되고있는 인근치 보간법, 선형 보간법, 큐빅 보간법들 보다 상대적으로 우월한 영상의 질을 가져옴을 보였다. 결론적으로, 영상의 기하학적 변환에 있어 기존의 세 가지 보간법들 보다 비-스플라인 보간법을 사용한 경우에 더 좋은 결과를 가지며, 비-스플라인 함수의 차수가 고차로 갈수록 영상의 질이 향상됨을 알 수 있다. 렌즈 등에 의한 왜곡현상을 가지고 있는 위성 사진이나 의료 영상을 기하학적 변환을 통하여 보정하는데 비-스플라인 보간법을 적용할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.590-593
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• 2002

A study on gray image morphing using 2D spline interpolation and 2D wavelet transform is presented. The B-spline of splines is used for interframe specified points to determine and the wavelet transform of transforms is applied for generating interframe images. The results are a smoothing image transfer by 2D spline and a removed degrading images as a blotting by 2D wavelet transform is making a good morphing image.

• 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 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.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1246-1251
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• 2006

본 연구에서는 영역 분할 알고리즘과 3차 스플라인 보간법을 이용하여 스테인드 글라스 렌더링을 위한 개선된 유리 타일 생성 알고리즘을 제안하였다. 먼저 유리 타일의 초기 형태를 추출하기 위하여 입력 영상에 Mean shift 분할 알고리즘을 적용하였다. Mean shift 분할 알고리즘은 영상의 각 픽셀(pixel)에서의 지역 밀도 최대 점(local density maximum)을 찾아 클러스터링(clustering)하는 알고리즘으로 영상을 효과적으로 분할할 수 있다. 그리고 분할된 영역에서 영역을 사용자 입력으로 병합하고, 영역에서 부적절한 형태를 없애기 위해 본 연구에서는 형태론적 연산(morphological operation)을 이용하였다. 추출된 영역으로부터 유리 타일의 형태로 만들기 위하여 추출된 각각의 영역에 3차 스플라인 보간법(cubic spline interpolation)을 적용하여 경계가 완화된 영역과 납틀(leading)의 형태를 얻는다. 그 다음 영역을 스플라인 곡선(spline curve)을 이용하여 재분할하고, 각 영역에 변환(transformation)된 색상을 적용하여 최종적인 유리 타일을 만들어낸다. 본 연구에서는 3차 스플라인 보간법을 이용하여 실제 스테인드 글라스에서 생길 수 있는 부드러운 경계를 갖는 유리 타일의 형태를 만들어 이를 스테인드 글라스 렌더링에 이용하였다. 이 방법은 기존의 영역 분할 알고리즘에 형태론적 연산만을 적용하여 유리 타일의 형태를 생성하는 것보다 효과적으로 유리 타일의 형태를 생성할 수 있다. 또한, 생성된 영역에 재분할 과정을 거쳐서 작은 유리 타일이 모여서 이루는 조형적인 형태를 이룰 수 있도록 하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.413-418
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• 2011

The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.508-510
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• 2000

본 논문은 서로 다른 스플라인 곡선들간의 위상적 상호관계로서 곡선과 곡면 설계상에서 중요한 작업인 주어진 영역 안에 한정된 보간곡선 제어방법을 제안한다. 위상적 상호관계는 곡선들간의 영향범위 관계 그리고 스플라인 곡선들과 곡면간의 기하학적 관계를 의미한다. 기존의 방법은 선형 분모를 가지는 분수식 3차 보간법을 사용하여 주어진 영역에서 제한된 보간 곡선을 제어하는 방법을 제안하였으나, 일반적인 경우에서 실행 상의 많은 계산량과 오차가 나타나는 문제점을 나타내었다. 본 논문은 이러한 문제점을 해결하기 위한 선형분모를 가지는 가중치된 분수식 3차 보간법을 제안한다. 이 방법은 변형 보간된 물체상의 변화량을 계산하여 불규칙한 패치들간의 결합부분과 제어 및 국부수정의 변형을 제어하는 방법을 제안한다.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.479-491
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• 2004

Location information of mobile objects is applied to vehicle tracking, digital battlefields, location based services, and telematics. Their location coordinates are periodically measured and stored in the application systems. The linear function is mainly used to estimate the location information that is not in the system at the query time point. However, a new method is needed to improve uncertainties of the location representation, because the location estimation by linear function induces the estimation error. This paper proposes an application method of the cubic spline interpolation in order to reduce deviation of the location estimation by linear function. First, we define location information of the mobile object moving on the two-dimensional space. Next, we apply the cubic spline interpolation to location estimation of the proposed data model and describe algorithm of the estimation operation. Finally, the precision of this estimation operation model is experimented. The experimentation comes out more accurate results than the method by linear function, although the proposed location estimation function uses the small amount of information. The proposed method has an advantage that drops the cost of data storage space and communication for the management of location information of the mobile objects.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.205-208
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• 2008

In numerical analysis numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But most of the thermodynamic properties of the steam table are determined by experiment. Therefore they are supposed to have measurement errors. In order to make noised thermodynamic properties corresponding to errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. the neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure. It was proved that the neural networks give smaller percentage error compared with quadratic spline interpolation. From this fact it was confirmed that the neural networks trace the original values of thermodynamic properties better than the quadratic interpolation method.