• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.95-102
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• 2015

This paper proposes a simple algorithm for circuit minimization. There are currently two effective heuristics for circuit minimization, namely manual Karnaugh maps and computable Quine-McCluskey algorithm. The latter, however, has a major defect: the runtime and memory required grow $3^n/n$ times for every increase in the number of variables n. The proposed algorithm, however, extracts the prime implicants (PI) that cover minterms of a given Boolean function by deriving an implicants table based on frequency. From a set of the extracted prime implicants, the algorithm then eliminates redundant PIs again based on frequency. The proposed algorithm is therefore capable of minimizing circuits polynomial time when faced with an increase in n. When applied to various 3-variable and 4-variable cases, it has proved to swiftly and accurately obtain the optimal solutions.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.57-64
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• 2014

As Steiner minimum tree building belongs to NP-Complete problem domain, heuristics for the problem ask for immense amount execution time and computations in numerous inputs. In this paper, we propose an efficient mechanism of euclidean Steiner minimum tree construction for numerous inputs using combination of Delaunay triangulation and Prim's minimum spanning tree algorithm. Trees built by proposed mechanism are compared respectively with the Prim's minimum spanning tree and minimums spanning tree based Steiner minimum tree. For 30,000 input nodes, Steiner minimum tree by proposed mechanism shows about 2.1% tree length less and 138.2% execution time more than minimum spanning tree, and does about 0.013% tree length less and 18.9% execution time less than minimum spanning tree based Steiner minimum tree in experimental results. Therefore the proposed mechanism can work moderately well to many useful applications where execution time is not critical but reduction of tree length is a key factor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.19-27
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• 2016

Maritime transport is now regarded as one of the main contributors to global climate change by virtue of its $CO_2$ emissions. Meanwhile, slow steaming, i.e., slower ship speed, has become a common practice in the maritime industry so as to lower $CO_2$ emissions and reduce bunker fuel consumption. The practice raised various operational decision issues in terms of shipping companies: how much ship speed is, how much to bunker the fuel, and at which port to bunker. In this context, this study addresses an operation problem in a shipping companies, which is the problem of determining the ship speed, bunkering ports, and bunkering amount at the ports over a given ship route to minimize the bunker fuel and ship time costs as well as the carbon tax which is a regulatory measure aiming at reducing $CO_2$ emissions. The ship time cost is included in the problem because slow steaming increases transit times, which implies increased in-transit inventory costs in terms of shippers. We formulate the problem as a nonlinear lot-sizing model and suggest a Lagrangian heuristic to solve the problem. The performance of the heuristic algorithm is evaluated using the data obtained from reliable sources. Although the problem is an operational problem, the heuristic algorithm is used to address various strategic issues facing shipping companies, including the effects of bunker prices, carbon taxes, and ship time costs on the ship speed, bunkering amount and number of bunkering ports. For this, we conduct sensitivity analyses of these factors and finally discuss study findings.

• Journal for History of Mathematics
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• v.22 no.3
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• pp.113-130
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• 2009

It can be said that the teaching and learning of mathematical problem solving has been greatly influenced by G. Polya. His heuristics shows down the explicit process of mathematical problem solving in detail. In contrast, Polanyi highlights the implicit dimension of the process. Polanyi's theory can play complementary role with Polya's theory. This study outlined the epistemology of Polanyi and his theory of problem solving. Regarding the knowledge and knowing as a work of the whole mind, Polanyi emphasizes devotion and absorption to the problem at work together with the intelligence and feeling. And the role of teachers are essential in a sense that students can learn implicit knowledge from them. However, our high school students do not seem to take enough time and effort to the problem solving. Nor do they request school teachers' help. According to Polanyi, this attitude can cause a serious problem in teaching and learning of mathematical problem solving.

• Journal of the Korea Computer Graphics Society
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• v.14 no.2
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• pp.9-17
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• 2008

We propose a method to determine camera parameters for character motion, which confiders the motion by itself. The basic idea is to approximately compute the area swept by the motion of the character's links that are orthogonally projected onto the image plane, which we call "Motion Area". Using the motion area, we can determine good fixed camera parameters and camera paths for a given character motion in the off-line or real-time camera control. In our experimental results, we demonstrate that our camera path generation algorithms can compute a smooth moving camera path while the camera effectively displays the dynamic features of character motion. Our methods can be easily used in combination with the method for generating occlusion-free camera paths. We expect that our methods can also be utilized by the general camera planning method as one of heuristics for measuring the visual quality of the scenes that include dynamically moving characters.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.41-48
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• 2011

In feature selection, the selective evaluation scheme for Ant Colony Optimization(ACO) has recently been proposed, which reduces computational load by excluding unnecessary or less promising candidate solutions from the actual evaluation. Its superiority was supported by experimental results. However the experiment seems to be not statistically sufficient since it used only one dataset. The aim of this paper is to analyze convergence characteristics of the selective evaluation scheme and to make the conclusion more convincing. We chose three datasets related to handwriting, medical, and speech domains from UCI repository whose feature set size ranges from 256 to 617. For each of them, we executed 12 independent runs in order to obtain statistically stable data. Each run was given 72 hours to observe the long-time convergence. Based on analysis of experimental data, we describe a reason for the superiority and where the scheme can be applied.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1302-1309
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• 2008

Traditional clustering methods, like k-means or fuzzy clustering, are prototype-based methods which are applicable only to convex clusters. On the other hand, spectral clustering tries to find clusters only using local similarity information. Its ability to handle concave clusters has gained the popularity recent years together with support vector machine (SVM) which is a kernel-based classification method. However, as is in SVM, the kernel width plays an important role and has a great impact on the result. Several methods are proposed to decide it automatically, it is still determined based on heuristics. In this paper, we proposed an adaptive method deciding the kernel width based on distance histogram. The proposed method is motivated by the fact that the affinity matrix should be formed into a block diagonal matrix to generate the best result. We use the tradition Euclidean distance together with the random walk distance, which make it possible to form a more apparent block diagonal affinity matrix. Experimental results show that the proposed method generates more clear block structured affinity matrix than the existing one does.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.1
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• pp.1-14
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• 2005

It is difficult to identify reusable and independent components in component-based development(CBD) process. Therefore existing methodologies have dealt the problem of component identification based on only developer's intuition and heuristics. As a result, it is difficult to identify the business components by common developers. Therefore, in this paper, we propose a new baseline and technique to identify the business components based on domain model such as use case diagrams, class diagrams, and sequence diagrams. proposed method identifies components through two phases; system component identification and business component identification. Especially, we consider structural characteristics as well as dependency characteristics according to methods call types and directions in identifying components. We also present a case study and comparative analysis and assessment to prove the practical use of our technique.

• Journal of KIISE:Information Networking
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• v.31 no.2
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• pp.123-130
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• 2004

Routing and wavelength assignment(RWA) problem is an important issue in optical transport networks based on wavelength division multiplexing(WDM) technique. It is typically solved using a combination of linear programming and graph coloring, or path selection based graph algorithms. Such methods are either complex or make extensive use of heuristics. In this paper we propose a novel and efficient approach which basically obtains the maximum edge disjoint paths (EDPs) for each source-destination demand pair. And those EDPs obtained are stored in Lookup Table and used for the update of weight matrix. Routes are determined in order by the weight matrix for the demand set. The comprehensive computer simulation shows that the Proposed algorithm uses similar or fewer wavelengths with significantly less execution time than bounded greedy approach (BGA) for EDP which is currently known to be effective in practice.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.195-202
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• 2009

In this paper, we propose the regions segmentation method of the white matter and the gray matter for brain MR image by using the ant colony optimization algorithm. Ant Colony Optimization (ACO) is a new meta heuristics algorithm to solve hard combinatorial optimization problem. This algorithm finds the expected pixel for image as the real ant finds the food from nest to food source. Then ants deposit pheromone on the pixels, and the pheromone will affect the motion of next ants. At each iteration step, ants will change their positions in the image according to the transition rule. Finally, we can obtain the segmentation results through analyzing the pheromone distribution in the image. We compared the proposed method with other threshold methods, viz. the Otsu' method, the genetic algorithm, the fuzzy method, and the original ant colony optimization algorithm. From comparison results, the proposed method is more exact than other threshold methods for the segmentation of specific region structures in MR brain image.