• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.527-533
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• 2018

Recently, the device-to-device (D2D) communication has been conceived as the key technology for the next-generation mobile communication systems. The neighbor discovery in which the nearby users are found, is essential for the proper operation of the D2D communication. In this paper, we propose new neighbor discovery scheme based on deep learning technology which has gained a lot of attention recently. In the proposed scheme, the neighboring users can be found using the uplink pilot transmission of users only, unlike conventional neighbor discovery schemes in which direct pilot communication among users is required, such that the signaling overhead can be greatly reduced in our proposed scheme. Moreover, the neighbors with different proximity can also be classified accordingly which enables more accurate neighbor discovery compared to the conventional schemes. The performance of our proposed scheme is verified through the tensorflow-based computer simulations.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.12
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• pp.3159-3174
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• 1994

A simple octree encoding algorithm based on a tetrahedron root has been developed to be used for fully automatic generation of three dimensional finite element meshes. This algorithm starts octree decomposition from a tetrahedron root node instead of a hexahedron root node so that the terminal mode has the same topology as the final tetrahedral mesh. As a result, the terminal octant can be used as a tetrahedral finite element without transforming its topology. In this part(I) of the thesis, an efficient algorithm for the tetrahedron-based octree is proposed. For this development, the following problems have been solved, : (1) an efficient data structure for storing the octree and finite elements, (2) an encoding scheme of a tetrahedral octree, (3) a neighbor finding technique for the tetrahedron-based octree.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.183-191
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• 2015

If the damage analysis on river-side facilities such as dam, river bank structures and bridges caused by disasters such as typhoon, flood, etc. becomes available, it can be a great help for disaster recovery and decision-making. In this research, We tried to extract a Digital Surface Model (DSM) and analyze the accuracy from Unmanned Air Vehicle (UAV) images on river-side facilities. We tried to apply stereo image-based matching technique, then extracted match results were united with one mosaic DSM. The accuracy was verified compared with a DSM derived from LIDAR data. Overall accuracy was around 3m of absolute and root mean square error. As an analysis result, we confirmed that exterior orientation parameters exerted an influence to DSM accuracy. For more accurate DSM generation, accurate EO parameters are necessary and effective interpolation and post process technique needs to be developed. And the damage analysis simulation with DSM has to be performed in the future.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.39-47
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• 2010

Constraint satisfaction optimization problem is a kind of optimization problem involving cost minimization as well as complex constraints. Local search and constraint programming respectively have been used for solving such problems. In this paper, I propose a method to integrate local search and constraint programming to improve search performance. Basically, local search is used to solve the given problem. However, it is very difficult to find a feasible neighbor satisfying all the constraints when we use only local search. Therefore, I introduced constraint programming as a tool for neighbor generation. Through the experimental results using weighted N-Queens problems, I confirmed that the proposed method can significantly improve search performance.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.4
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• pp.109-117
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• 1998

In a preliminary design available information is limited, so it makes attractive to rely on the design cases of existing ships to design new ships. In this paper a prototype of the case-based conceptual design system of a ship is developed to support systematically the design process. This system not only generates design candidates through a case indexing, but also determines the priorities of the candidates by using nearest neighbor matching algorithm. The final solution is presented through adaptation process. The validation of the system was examined and verified by applying to conceptual ship design stage.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.1266-1269
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• 2007

IPv6 환경에서는 NDP(Neighbor Discovery Protocol)를 이용한 주소 자동 설정 메커니즘을 지원한다. 그러나, NDP 는 메시지 내 중요 정보가 네트워크 상에 그대로 노출됨으로 인해 각종 공격에 취약하다. 이러한 취약성을 극복하기 위해, CGA(Cryptographically Generated Address)를 사용하여 주소의 소유권 증명이 가능한 SEND(SEcure Neighbor Discovery)가 도입되었다. 그러나 SEND 는 높은 비용 연산으로 인해 모바일 기기 등에 적용하는데 한계점을 가진다. SEND 의 한계점을 보완하고자 해쉬 함수를 이용해 주소 자동 설정에 사용되는 임시 주소를 감추는 기법이 제안되었다. 이 기법은 DAD(Duplicate Address Detection) 과정 중 SEND 수준의 보안을 제공하면서도 빠르게 동작할 수 있는 장점을 갖는다. 본 논문에서는 리눅스 환경에서 제안 기법을 구현해 보고, 주소 생성 시간 측정 및 DAD 과정에서 드러난 서비스 거부 공격에 대한 안전성을 검증한다.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• Journal of Korea Multimedia Society
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• v.9 no.8
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• pp.1045-1053
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• 2006

We introduce a new method of painterly rendering. Instead of using the gradient direction of the source image to generate a brush stroke, we extract regions that can be drawn in one stroke using MHN filtering followed by identification of connected components, and make a brush stroke from each, based on an approximation to the medial axis. This method results in realistic-looking brush strokes of varying width that have an irregular directions where necessary.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1283-1287
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• 2003

Reverse engineering technology refers to the process that creates a CAD model of an existing part using measuring devices. Recently, non-contact scanning devices have become more accurate and the speed of data acquisition has increased drastically. However, they generate thousands of points per second and various types of point data. Therefore. it becomes a important to handle the huge amount and various types of point data to generate a surface model efficiently. This paper proposes a new triangular mesh generation method using 3D grids. The geometric information of a part can be obtained from point cloud data by estimating normal values of the points. In our research, the non-uniform 3D grids are generated first for feature based data reduction based on the geometric information. Then, triangulation is performed with the reduced point data. The grid structure is efficiently used not only for neighbor point search that can speed up the mesh generation process but also for getting surface connectivity information to result in same topology surface with the point data. Through this integrated approach, it is possible to create surface models from scanned point data efficiently.