• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.753-758
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• 2017

The ray-shooting problem is to find the first intersection point on the surface of given geometric objects where a ray moving along a straight line hits. Since rays are usually given in the form of queries, this problem is typically solved as follows. First, a data structure for a collection of objects is constructed as preprocessing. Then, the answer for each query ray is quickly computed using the data structure. In this paper, we consider the ray-shooting problem about the set of vertical line segments on the x-axis. We present a new data structure called a convex layer tree for n vertical line segments given by input. This is a tree structure consisting of layers of convex hulls of vertical line segments. It can be constructed in O(n log n) time and O(n) space and is easy to implement. We also present an algorithm to solve each query in O(log n) time using this data structure.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.3
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• pp.133-140
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• 2008

This paper proposes an effective k-D tree traversal algorithm for ray tracing on a GPU. The previous k-D tree traverse algorithm based on GPU uses bottom-up searching from a leaf to the root after failing to find the ray intersected primitive in the leaf node. During the bottom-up search the algorithm decides the current node is visited or not from the parent node. In such a way, we need to visit the parent node which was already visited and the duplicated bounding box intersection tests. The new k-D tree traverse algorithm reduces the brother and parent duplicated visit by using an efficient method which decides whether the brother node is already visited or not during the bottom-up search. Also the algorithm take place bounding box intersection tests only for the nodes which is not yet done. As a result our experiment shows the new algorithm is about 30% faster than the previous.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.25 no.1
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• pp.23-34
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• 1997

Seismic tomography using the graph theoretical method of ray tracing is performed in two synthetic data sets with laterally varying velocity structures. The straight-ray tomography shows so poor results in imaging the laterally varying velocity structure that the ray-traced tomographic techniques should be used. Conventional ray tracing methods have serious drawbacks, i.e. problems of convergence and local minima, when they are applied to seismic tomography. The graph theretical method finds good approximated raypaths in rapidly varying media even in shadow zones, where shooting methods meet with convergence problems. The graph theoretical method ensures the globally minimal traveltime raypath while bending methods often cause local minima problems. Especially, the graph theoretical method is efficient in case that many sources and receivers exist, since it can find the traveltimes and corresponding raypaths to all receivers from a specific source at one time. Moreover, the algorithm of graph theoretical method is easily applicable to the ray tracing in anisotropic media, and even to the three dimensional case. Among the row-active inversion techniques, the conjugate gradient (CG) method is used because of fast convergence and high efficiency. The iterative sequence of the ray tracing by the graph theoretical method and the inversion by the CG method is an efficient and robust algorithm for seismic tomography in laterally varying velocity structures.

• Journal of the Korean Society of Radiology
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• v.12 no.6
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• pp.793-799
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• 2018

As interest in artificial intelligence has increased, artificial intelligence has been actively studied in the medical field. In Korea, artificial intelligence has been applied to medical imaging devices such as X-ray imaging, Computer Tomography and Magnetic Resonance Imaging and artificial intelligence capable of acquiring radiation images of patients without radiologists in the future Medical devices are expected to be invented. This study was an initial study on the automation of patient positioning in X - ray imaging. We used x-ray equipment and human phantoms to evaluate the positioning. The program used Visual Studio 2010 MFC and the image was in the size $1450{\times}1814$. The pixel values were converted to contrasts with values of 0 to 255 that can be visually recognized and output to the monitor. We developed a procedure algorithm program that predicts the angle of the output image through three pixel coordinate values and induces the patient to perform correct positioning according to the voice guidance according to the angle. In the next study, we will study the artificial intelligence to grasp the structure itself and calculate the angle, rather than conveying the reference of coordinates to artificial intelligence. In the future, it is expected that it will be helpful in the study of artificial intelligence from shooting to positioning through the automation of positioning.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.12
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• pp.982-992
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• 2017

In this paper, we introduce a method of selection for the optimal transmission polarization of a W-band seeker through the extraction of the one-dimensional scattering center of a ground tank target. We calculated the surface scattering and edge scattering using the shooting and bouncing ray tracing method of the CST A-solver. Based on 4-channel RCS data, using the one-dimensional RELAX algorithm, which is a kind of spectral estimation technique, scattering centers of ground targets were extracted. According to the changes in the polarization state and look angle, we compared and analyzed the scattering center results. Through simulation, we verified that the scattering center results can be applied when feature vectors are used for target recognition.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.555-562
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• 2004

Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute $L_2$ Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.