• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.122-126
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• 2008

Integrating a 3D solid modeler with a general purpose FEM code, an automatic stress intensity factor analysis system of the 3D crack problems has been developed. A geometry model, i.e. a solid containing one or several 3D cracks is defined. Several distributions of local node density are chosen, and then automatically superposed on one another over the geometry model by using the fuzzy knowledge processing. Nodes are generated and quadratic tetrahedral solid elements are generated by the Delaunay triangulation techniques. Finally, the complete finite element(FE) model generated, and a stress analysis is performed. This paper describes the methodologies to realize such functions, and demonstrates the validity of the present system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.87-90
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• 2009

영상의 깊이 정보를 추출하는 것은 매우 어려운 연구이다. 다양한 유형의 영상 구조의 분석이 필요하지만 많은 경우에 주관적인 판단의 도움이 필요하다. 본 논문에서는 로스 텍스처 필터를 기반으로 정지 영상의 깊이를 자동으로 생성하는 방법을 제안한다. 로스 텍스처 필터는 단안 비전에서 3D 깊이를 얻기 위한 방법으로 활용되었는데, 실제 2D 영상에서 깊이를 예측하기 위해 텍스처 편차, 텍스처 기울기, 색상 등을 활용한다. 로스 필터는 $1{\times}5$ 벡터로부터 콘볼루션을 이용하여, 20여개의 $5{\times}5$ 콘볼루션 필터가 구해지는데, 영상에 필터를 적용하여 로스 에너지를 계산한다. 구해진 에너지를 깊이 맵으로 변환하고, 깊이 맵에서 특징 점을 구하고, 특징 점들로부터 델러노이 삼각화를 이용하여 삼각형 깊이 메쉬를 얻는다. 구해진 깊이 맵의 성능을 측정하기 위해 카메라 시점을 변경하면서 영상의 3D 구조를 분석하였으며, 입체영상을 생성하여 3D 입체 시청 결과를 분석하였다. 실험에서는 로스 텍스처 필터를 이용하는 깊이 생성 방법이 좋은 효과를 얻는 것을 확인하였다.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.10
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• pp.768-776
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• 2006

Given an unstructured point set, we use an MLS (melting least-squares) approximation to estimate the local curvatures and their derivatives at a point by means of an approximation surface Then, we compute neighbor information using a Delaunay tessellation. feature points can then be detected as zero-crossings, and connected using curvature directions. Also this approach has a fast computation time than previous methods, which based on triangle meshes. We demonstrate our method on several large point-sampled models, rendered by point-splatting, on which the feature lines are rendered with line width determined from curvatures.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1027-1036
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• 2020

In this study, a cloud-based processing method using Agisoft Metashape, a commercial software, and Amazon web service, a cloud computing service, is introduced and evaluated to quickly generate high-precision 3D realistic data from large volume UAV images acquired in disaster sites. Compared with on-premises method using a local computer and cloud services provided by Agisoft and Pix4D, the processes of aerial triangulation, 3D point cloud and DSM generation, mesh and texture generation, ortho-mosaic image production recorded similar time duration. The cloud method required uploading and downloading time for large volume data, but it showed a clear advantage that in situ processing was practically possible. In both the on-premises and cloud methods, there is a difference in processing time depending on the performance of the CPU and GPU, but notso much asin a performance benchmark. However, it wasfound that a laptop computer equipped with a low-performance GPU takes too much time to apply to in situ processing.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1022-1030
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• 2006

The location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet in spite of many researches. Among various location tracking systems, we choose the RFID system due to its wide applications. However, the sensed RSSI signal is too sensitive to the direction of a RFID reader antenna, the orientation of a RFID tag, the human interference, and the propagation media situation. So, the existing location tracking method in spite of using the particle filter is not working well. To overcome this shortcoming, we suggest a robust location tracking method with a double layered structure, where the first layer coarsely estimates a tag's location in the block level using a regression technique or the SVM classifier and the second layer precisely computes the tag's location, velocity and direction using the particle filter technique. Its layered structure improves the location tracking performance by restricting the moving degree of hidden variables. Many extensive experiments show that the proposed location tracking method is so precise and robust to be a good choice for implementing the location estimation of a person or an object in the ubiquitous computing. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complicate workplace.

• Economic and Environmental Geology
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• v.38 no.4 s.173
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• pp.481-492
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• 2005

The purpose of this study is to determine geochemical characteristics for stream sediments in the Gwangju area. We collect the stream sediments samples by wet sieving along the primary channels and dry these samples slowly in the laboratory and grind to under 200mesh using an alumina mortar fur chemical analysis. Major elements, trace and rare earth elements are determined by XRF, ICP-AES and NAA analysis methods. For geochemical characteristics on geological groups of stream sediments, we separate geologic groups which are derived from Precambrian granite gneiss area, Jurassic granite area and Cretaceous Hwasun andesite area. Contents range of major elements for stream sediments in the Gwangju area are $SiO_2\;51.89\~70.63\;wt.\%,\;Al_2O-3\;12.91\~21.95\;wt.\%,\;Fe_2O_3\;3.22\~9.89\;wt.\%,\;K_2O\;1.85\~4.49\;wt.\%,\;MgO\;0.68\~2.90\;wt.\%,\;Na_2O\;0.48\~2.34\;wt.\%,\;CaO\;0.42\~6.72\;wt.\%,\;TiO_2\;0.53\~l.32\;wt.\%,\;P_2O_5\;0.06\~0.51\;wt.\%\;and\;MnO\;0.05\~0.69\;wt.\%.$ According to the AMF diagram for stream sediments and rocks, the stream sediments are plotted on boundary of tholeiitic series and calk alkaline series, which shows that contents of $Fe_2O_3$ are higher in stream sediments than rocks. According to variation diagram of $SiO_2$ versus $(K_2O+Na_2O),$ stream sediments are plotted on subalkaline series. Contents range of trace and rare earth elements for stream sediments in the Gwangiu area are Ba$590\~2170$ppm, Be1\~2.4$ppm, Cu$13\~79$ppm, Nb$20\~34$ppm, Ni$10\~50$ppm, Pb$17\~30$ppm, Sr$70\~1025$ ppm, V$42\~135$ppm, Zr$45\~171$ppm, Li$19\~77$ppm, Co$4.3\~19.3$ppm, Cr$28\~131$ppm, Cs$3.1\~17.6$ppm, Hf$5\~27.6$ppm, Rb$388\~202$ppm, Sb$0.2\~l.2$ ppm, Sc$6.4\~17$ppm, Zn$47\~389$ppm, Pa$8.8\~68.8$ppm, Ce$62\~272$ppm, Eu$1\~2.7$ppm and Yb$0.9\~6$ppm.