• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.768-771
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• 2008

This paper presents an algorithm for generating inverse element over finite fields $GF(3^m)$, and constructing method of inverse element generator based on inverse element generating algorithm. The method need to compute inverse of an element eve. $GF(3^m)$ which corresponds to a polynomial eve. $GF(3^m)$ with order less than equal to m-1. Here, the computation is based on multiplication, square and cube method derived from the mathematics properties over finite fields.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.69-76
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• 1999

Recently, it has been studied the methods to materialize and precompute the query results for complexed spatial aggregation queries with high response time and the popular use in spatial data warehouse. In this paper, we propose extended selective materialization algorithm and present the way to materialize selectively which is considered access frequency and computation time of spatial operation according to spatial measures of spatial views for improvement of existing selective materialization algorithms.

• Textile Coloration and Finishing
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• v.2 no.2
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• pp.20-32
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• 1990

In this paper, a new method by Cubic Spline to analyze Munsell Value Function is proposed. The values calculated by this method are compared with ones by Judd's Polynomial and Cube Root Functions, etc. For performing these computation algorithms have been developed.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.6
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• pp.753-765
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• 2003

A finite element code for the numerical solution of the Navier-Stokes equation is parallelized by vertex-oriented domain decomposition. To accelerate the convergence of iterative solvers like conjugate gradient method, parallel block ILU, iterative block ILU, and distributed ILU methods are tested as parallel preconditioners. The effectiveness of the algorithms has been investigated when P1P1 finite element discretization is used for the parallel solution of the Navier-Stokes equation. Two-dimensional and three-dimensional Laplace equations are calculated to estimate the speedup of the preconditioners. Calculation domain is partitioned by one- and multi-dimensional partitioning methods in structured grid and by METIS library in unstructured grid. For the domain-decomposed parallel computation of the Navier-Stokes equation, we have solved three-dimensional lid-driven cavity and natural convection problems in a cube as benchmark problems using a parallelized fractional 4-step finite element method. The speedup for each parallel preconditioning method is to be compared using upto 64 processors.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.199-204
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• 2018

In this paper, we propose a fast collision detection method for interacting with objects in virtual space. First, in the mesh grouping step, the entire space is divided into small spaces of a predetermined size, and the positions and sizes of the smallest basic stereoscopic shapes (cube or sphere) including all of the meshes belonging to each small space are determined. In the collision detection step, it is checked whether a haptic interface point (HIP) is included in a three-dimensional figure representing a group. When a collision with a specific three-dimensional figure is confirmed, searching is performed only for the meshes in the group to find a mesh on which a possible real collision with HIP occurred. The effectiveness of the proposed algorithm is verified by measuring and comparing the computation time of the proposed method with and without the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.6
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• pp.230-237
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• 1985

Shortest path calculations for a large-scale network have to be performed using a decomposition techniqre, since the calculations require large memory size which increases by the square of the number of vertices in the network. Also, the calculation time increases by the cube of the number of vertices in the network. In the decomposition technique,the network is broken into a number of smaller size subnetworks for each of which shortest paths are computed. A union of the solutions provides the solution of the original network. In all of the decomposition algirithms developed up to now, boundary vertices which divide all the subnetworks have to be included in computing shortest paths for each subnetwork. In this paper, an improved algorithm is developed to reduce the number of boundary vertices to be engaged. In the algorithm, only those boundary vertices that are directly connected to the subnetwork are engaged. The algorithm is suitable for an application to real time computation using a parallel processor system which consists of a number of micro-computers or prcessors. The algorithm has been applied to a 39- vertex network and a 232-vertex network. The results show that it is efficient and has better performance than any other algorithms. A parallel processor system has been built employing an MZ-80 micro-computer and two Z-80 microprocessor kits. The former is used as a master processor and the latter as slave processors. The algorithm is embedded into the system and proven effective for real-time shortest path computations.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.783-786
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• 2014

맵리듀스(MapReduce)는 대용량의 데이터를 여러 컴퓨터에서 분산, 병렬 처리하는 프레임워크이다. Grouping sets 질의는 사용자가 지정한 여러 개의 group-by들을 모두 구하는 질의로서, 롤업(rollup)과 큐브(cube)가 너무 많은 결과를 반환하는 단점을 보완하여 원하는 group-by들에 대한 결과만 얻을 수 있도록 한다. 본 논문은 맵리듀스 환경에서 grouping sets 질의를 효율적으로 계산하는 방법을 제안한다. 제안 방법은 grouping sets 질의를 2개의 맵리듀스 잡(job)을 통해 단계적으로 계산한다. 첫 번째 맵리듀스 잡은 grouping sets 질의에 포함된 group-by들이 모두 계산될 수 있는 '부모' group-by를 먼저 계산한다. 두 번째 맵리듀스 잡은 부모 group-by를 입력으로 하여 grouping sets 질의에 포함된 group-by들을 각각 계산한다. 부모 group-by의 크기가 입력 데이터의 크기에 비해 매우 작은 경우, 제안 방법은 입력 데이터로부터 각 group-by를 독립적으로 구하는 단순 방법보다 좋은 성능을 보인다. 실험을 통해 제안 방법이 각 group-by를 독립적으로 구하는 단순 방법보다 좋은 성능을 가짐을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.715-718
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• 2014

맵리듀스(MapReduce)는 대용량 데이터의 병렬 처리에 사용되는 프로그래밍 모델이다. 데이터 큐브(data cube)는 대용량 데이터의 다차원 분석에 널리 사용되는 연산자로서, 주어진 차원 애트리뷰트들의 모든 가능한 조합에 대한 group-by 를 계산한다. 차원 애트리뷰트가 n 개일 때, 데이터 큐브는 총 $2^n$ 개의 group-by 를 계산한다. 본 논문은 맵리듀스 환경에서 데이터 큐브를 효율적으로 계산하는 방법을 제안한다. 제안 방법은 $2^n$ 개의 group-by 를 분할하고 이들을 ${\lceil}n/2{\rceil}$개의 맵리듀스 잡(job)을 통해 단계적으로 계산한다. 제안 방법은 각 맵리듀스 잡에서 맵 함수가 출력하는 중간결과의 크기를 최소화함으로써 총 계산 비용을 크게 줄인다. 실험을 통해 제안 방법은 기존 방법에 비해 데이터 큐브를 더 빠르게 계산함을 보인다.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.25-36
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• 2004

This paper proposes and measures the performance of a distributed EIT (Electrical Impedance Tomography) image reconstruction algorithm which has a master-slave structure. The image construction is a computation based application of which the execute time is proportional to the cube of the unknowns. After receiving a specific frame from the master, each computing node extracts the basic elements by executing the first iteration of Kalman Filter in parallel. Then the master merges the basic element lists into one group and then performs the sequential iterations with the reduced number of unknowns. Every computing node has MATLAB functions as well as extended library implemented for the exchange of MATLAB data structure. The master implements another libraries such as threaded multiplication, partitioned inverse, and fast Jacobian to improve the speed of the serial execution part. The parallel library reduces the reconstruction time of image visualization about by half, while the distributed grouping scheme further reduces by about 12 times for the given target object when there are 4 computing nodes.