• Journal of KIISE:Information Networking
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• v.36 no.6
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• pp.514-527
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• 2009

Wireless sensor networks (WSNs) can be used in various applications for military or environmental purpose. Recently, there are lots of on-going researches for detecting and tracking the spread of continuous objects or phenomena such as poisonous gas, wildfires, earthquakes, and so on. Some previous work has proposed techniques to detect edge nodes of such a continuous object based on the information of all the 1-hop neighbor nodes. In those techniques, however, a number of nodes are redundantly selected as edge nodes, and thus, the boundary of the continuous object cannot be presented accurately. In this paper, we propose a new edge detection method in which edge nodes of the continuous object are detected based on the information of the neighbor nodes obtained via the Localized Delaunay Triangulation so that a minimum number of nodes are selected as edge nodes. We also define the sensor behavior rule for tracking continuous objects energy-efficiently. Our simulation results show that the proposed edge detection method provides enhanced performance compared with previous 1-hop neighbor node based methods. On the average, the accuracy is improved by 29.95% while the number of edge nodes, the amount of communication messages and energy consumption are reduced by 54.43%, 79.36% and 72.34%, respectively. Moreover, the number of edge nodes decreases by 48.38% on the average in our field test with MICAz motes.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.113-122
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• 2007

The rapid growth of aerial survey and remote sensing technology has enabled the rapid acquisition of very large amounts of geographic data, which should be analyzed using real-time visualization technology. The level of detail(LOD) algorithm is one of the most important elements for realizing real-time visualization. We chose the triangulated irregular network (TIN) method to generate normalized digital elevation model(DEM) data. First, we generated TIN data using contour lines obtained from a two-dimensional(2D) digital map and created a 2D grid array fitting the size of the area. Then, we generated normalized DEM data by calculating the intersection points between the TIN data and the points on the 2D grid array. We used constrained Delaunay triangulation(CDT) and ray-triangle intersection algorithms to calculate the intersection points between the TIN data and the points on the 2D grid array in each step. In addition, we simulated a three-dimensional(3D) terrain model based on normalized DEM data with real-time visualization using a Microsoft Visual C++ 6.0 program in the DirectX API library and a quad-tree LOD algorithm.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.10
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• pp.520-529
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• 2005

A feature-based 3D modeling algorithm is presented in this paper. Since conventional methods use depth-based techniques, they need much time for the image matching to extract depth information. Even feature-based methods have less computation load than that of depth-based ones, the calculation of modeling error about whole pixels within a triangle is needed in feature-based algorithms. It also increase the computation time. Therefore, the proposed algorithm consists of three phases, which are an initial 3D model generation, model evaluation, and model refinement phases, in order to acquire an efficient 3D model. Intensity gradients and incremental Delaunay triangulation are used in the Initial model generation. In this phase, a morphological edge operator is adopted for a fast edge filtering, and the incremental Delaunay triangulation is modified to decrease the computation time by avoiding the calculation errors of whole pixels and selecting a vertex at the near of the centroid within the previous triangle. After the model generation, sparse vertices are matched, then the faces are evaluated with the size, approximation error, and disparity fluctuation of the face in evaluation stage. Thereafter, the faces which have a large error are selectively refined into smaller faces. Experimental results showed that the proposed algorithm could acquire an adaptive model with less modeling errors for both smooth and abrupt areas and could remarkably reduce the model acquisition time.

• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.299-309
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• 2008

In this paper, we propose an algorithm for object generation from model-based 3-dimensional multi-viewpoint images using OpenGL rendering. In the first step, we preprocess a depth map image in order to get a three-dimensional coordinate which is sampled as a vertex information on OpenGL and has a z-value as depth information. Next, the Delaunay Triangulation algorithm is used to construct a polygon for texture-mapping using the vertex information. Finally, by mapping a texture image on the constructed polygon, we generate a viewpoint-adaptive object by calculating 3-dimensional coordinates on OpenGL.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.230-235
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• 2009

This paper describes an automatic finite element mesh generation for finite element analysis of three-dimensional structures. It is consisting of fuzzy knowledge processing, bubble meshing and solid geometry modeler. This novel mesh generation process consists of three subprocesses: (a) definition of geometric model, i.e. analysis model, (b) generation of bubbles, and (c) generation of elements. One of commercial solid modelers is employed for three-dimensional solid structures. Bubble is generated if its distance from existing bubble points is similar to the bubble spacing function at the point. The bubble spacing function is well controlled by the fuzzy knowledge processing. The Delaunay method is introduced as a basic tool for element generation. Automatic generation of finite element for three-dimensional solid structures holds great benefits for analyses. Practical performances of the present system are demonstrated through several mesh generations for 3D geometry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2183-2188
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• 2009

This paper describes an automatic mesh generation system for finite element analysis of three-dimensional cracks. It is consisting of fuzzy knowledge processing, bubble meshing and solid geometry modeler. This novel mesh generation process consists of three sub-processes: (a) definition of geometric model, i.e. analysis model, (b) generation of bubbles, and (c) generation of elements. One of commercial solid modelers is employed for three-dimensional crack structures. Bubble is generated if its distance from existing bubble points is similar to the bubble spacing function at the point. The bubble spacing function is well controlled by the fuzzy knowledge processing. The Delaunay method is introduced as a basic tool for element generation. Practical performances of the present system are demonstrated through several mesh generations for 3D cracks.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.11-18
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• 2013

This paper presents a SFF(shape from focus) algorithm using a new focus measure based on higher order statistics for the exact depth estimation. Since conventional SFF-based 3D depth reconstruction algorithms used SML(sum of modified Laplacian) as the focus measure, their performance is strongly depended on the image characteristics. These are efficient only for the rich texture and well focused images. Therefore, this paper adopts a new focus measure using HOS(higher order statistics), in order to extract the focus value for relatively poor texture and focused images. The initial best focus area map is generated by the measure. Thereafter, the area refinement, thinning, and corner detection methods are successively applied for the extraction of the locally best focus points. Finally, a 3D model from the carefully selected points is reconstructed by Delaunay triangulation.

• Coupled systems mechanics
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• v.7 no.6
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• pp.649-668
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• 2018

In this paper, we present a numerical model for fluid-structure interaction between structure built of porous media and acoustic fluid, which provides both pore pressure inside porous media and hydrodynamic pressures and hydrodynamic forces exerted on the upstream face of the structure in an unified manner and simplifies fluid-structure interaction problems. The first original feature of the proposed model concerns the structure built of saturated porous medium whose response is obtained with coupled discrete beam lattice model, which is based on Voronoi cell representation with cohesive links as linear elastic Timoshenko beam finite elements. The motion of the pore fluid is governed by Darcy's law, and the coupling between the solid phase and the pore fluid is introduced in the model through Biot's porous media theory. The pore pressure field is discretized with CST (Constant Strain Triangle) finite elements, which coincide with Delaunay triangles. By exploiting Hammer quadrature rule for numerical integration on CST elements, and duality property between Voronoi diagram and Delaunay triangulation, the numerical implementation of the coupling results with an additional pore pressure degree of freedom placed at each node of a Timoshenko beam finite element. The second original point of the model concerns the motion of the outside fluid which is modeled with mixed displacement/pressure based formulation. The chosen finite element representations of the structure response and the outside fluid motion ensures for the structure and fluid finite elements to be connected directly at the common nodes at the fluid-structure interface, because they share both the displacement and the pressure degrees of freedom. Numerical simulations presented in this paper show an excellent agreement between the numerically obtained results and the analytical solutions.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.3
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• pp.105-111
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• 2011

Landmark Spots in 2D gel electrophoresis are used in many methods of 2DEG spot matching. Landmark Spots are obtained manually and it is a bottle neck in the entire protein analysis process. Automated landmark spots detection is a very crucial topic in processing a massive amount of 2DGE data. In this paper, Automated landmark spot detection is proposed using point pattern matching and graph theory. Neighbor spots are defined by a graph theory to use and only a centered spot and its neighbor spots are considered for spot matching. Normalized Hausdorff distance is introduced as a criterion for measuring degree of similarity. In the conclusion, the method proposed in this paper can get about 50% of the total spot pairs and the accuracy rate is almost 100%, which the requirements of landmark spots are fully satisfied.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.625-627
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• 2001

기존의 종이지도를 수치지도 처리과정으로 얻어진 등고선(contour line) 데이터는 원격탐사(Remote Sensing)와 지리정보시스템(GIS)의 응용분야에서 주로 사용되어지는 데이터이다. 이러한 등고선은 해당 지역의 DTM(Digital Terrain Model) 데이터 생성을 위해 보간(interpolation)하여 생성하는 데 연구가 집중되어 왔다. 본 논문에서는 DEM(Digital levation Model)으로부터 얻어진 등고선 데이터를 이용하여 사용자에게 3차원으로 가시화 해 줄 수 있는 기법을 소개한다. 등고선 추출을 위한 방법으로는 기존의 소개되어진 Marching Square 알고리즘을 적용하였고, 지역적인 최고점(local minimum)과 최소점(maximum)을 구하기 위해 등고선을 열린 등고선(open contour)과 닫힌 등고선(closed contour)으로 분류하게 된다. 지역적 최고, 최소점을 찾기 위한 탐색공간을 줄이기 위해 닫힌 등고선만을 닫힌 등고만을 대상으로 등고선 트리를 생성하였으며, 생성된 트리의 리프노드에 대해서 최고, 최소점에 대한 근사(approximation)를 수행하게 된다. 이렇게 구해진 근사된 장점들과 등고선 데이털 입력으로 하여 제한된 딜로니 삼각분할(Constrained Delaunay Triangulation)을 수행함으로써, 3차원 지형을 재구성할 수 있다. 실험에서 간단한 그리드 샘플데이터와 USGS로 획득한 데이터를 이용하여 속도 측정을 하였다. 결과적으로 저장공간 측면에서 적은 량의 데이터를 가지면서 등고선을 표현할 수 있는 3차원 지형을 랜더링할 수가 있음을 알 수 있다.