• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.64-73
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• 2011

The problem of Euclidean minimum spanning tree (EMST) is to connect given nodes in a plane with minimum cost. There are many algorithms for the polynomial time problem as EMST. However, for numerous nodes, the algorithms consume an enormous amount of time to find an optimal solution. In this paper, an approximation scheme using a polynomial time approximation scheme (PTAS) algorithm with dividing and parallel processing for the problem is suggested. This scheme enables to construct a large, approximate EMST within a short duration. Although initially devised for the non-polynomial problem, we employ naive PTAS to construct a vast EMST with dynamic programming. In an experiment, the approximate EMST constructed by the proposed scheme with 15,000 input terminal nodes and 16 partition cells shows 89% and 99% saving in execution time for the serial processing and parallel processing methods, respectively. Therefore, our scheme can be applied to obtain an approximate EMST quickly for numerous input terminal nodes.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.87-95
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• 2012

In this paper, PTAS three-dimensional Steiner minimum tree connecting numerous input nodes rapidly in 3D space is proposed. Steiner minimum tree problem belongs to NP problem domain, and when properly devised heuristic introduces, it is generally superior to other algorithms as minimum spanning tree affiliated with P problem domain. But when the number of input nodes is very large, the problem requires excessive execution time. In this paper, a method using PTAS is proposed to solve the difficulty. In experiments for 70,000 input nodes in 3D space, the tree produced by the proposed 8 space partitioned PTAS method reduced 86.88% execution time, compared with the tree by naive 3D steiner minimum tree method, though increased 0.81% tree length. This affirms the proposed method can work well for applications that many nodes of three dimensions are need to connect swifty, enduring slight increase of tree length.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.149-155
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• 2013

Commonly, one seeks a particular pattern suitable for stock cutting and the number of such patterns through linear programming. However, since the number of the patterns increases exponentially, it is nearly impossible to predetermine all the existing patterns beforehand. This paper thus proposes an algorithm whereby one could accurately predetermine the number of existing patterns by applying Suliman's feasible pattern method. Additionally, this paper suggests a methodology by which one may obtain exact polynomial-time solutions for feasible patterns without applying linear programming or approximate algorithm. The suggested methodology categorizes the feasible patterns by whether the frequency of first occurrence of all the demands is distributed in 0 loss or in various losses. When applied to 2 data sets, the proposes algorithm is found to be successful in obtaining the optimal solutions.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.153-161
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• 2011

As the problem of GOSST building belongs to NP compete domain, heuristics for the problem ask for immense amount execution time and computations in large scale inputs. In this paper, we propose an efficient mechanism for GOSST construction using space locality PTAS. For 40,000 input nodes with maximum weight 100, the proposed space locality PTAS GOSST with 16 unit areas can reduce about 4.00% of connection cost and 89.26% of execution time less than weighted minimum spanning tree method. Though the proposed method increases 0.03% of connection cost more, but cuts down 96.39% of execution time less than approximate GOSST method (SGOSST) without PTAS. Therefore the proposed space locality PTAS GOSST mechanism can work moderately well to many useful applications where a greate number of weighted inputs should be connected in short time with approximate minimum connection cost.