• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.485-487
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• 2011

우리는 그래프 그리기를 위한 셀룰러 오토마타 모델을 제안한다. 셀룰러 오토마타는 간단한 전이 규칙들의 집합을 정의함으로써 비교적 복잡한 자연현상이나 사회현상을 재현해낼 수 있는 특징을 갖고 있다. 우리는 기존에 Eades가 제안했던 하중-기초 그래프 그리기 알고리즘을 기반으로 하여 육각형 격자상에서의 그래프 그리기를 위한 셀룰러 오토마타 모델을 제안한다. 제안된 모델은 기존의 알고리즘에 비해 더 효율적인 연산과정을 거치게 되며 보다 대칭적인 그래프를 그려준다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2615-2626
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• 1998

In this paper, we propose a new interconnection network called RFM graph, whichis the merger of the directed rotator and Faber-Moore graph, and analyze fault tolerance, routing algorithm node disjoint cycles and broadcasting algorithm. We also describe methods to embed star graph, 2 dimesional torus and bubblesort graph into RFM graph with unit expasion and dilation 2.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.341-346
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• 2008

Minimum Weight Triangulation (MWT) problem is an optimization problem searching for the triangulation of a given graph with minimum weight. Like many other graph problems this problem is also known to be NP-hard for general graphs. Several heuristic algorithms have been proposed for this problem including simulated annealing and genetic algorithm. In this paper, we propose a new genetic algorithm called GA-FF and show that the performance of the proposed genetic algorithm outperforms the previous one.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.234-242
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• 2010

In this paper, we deal with the visualization of graphs, which are one of the most important data structures. As the size of a graph increases, it becomes more difficult to check the graph visually because of the increase of edge intersections. We propose a new method of overcoming such problem. Most of previous studies considered only the minimization of edge intersections, but we additionally pursue to preserve distances between vertices. We present a novel genetic algorithm using an evaluation function based on a weighted sum of two objectives. Our experiments could show effective visualization results.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.234-242
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• 2024

We propose a passive sonar signal classification algorithm using Graph Neural Network (GNN). The proposed algorithm segments spectrograms into image patches and represents graphs through connections between adjacent image patches. Subsequently, Graph Convolutional Network (GCN) is trained using the represented graphs to classify signals. In experiments with publicly available underwater acoustic data, the proposed algorithm represents the line frequency features of spectrograms in graph form, achieving an impressive classification accuracy of 92.50 %. This result demonstrates a 8.15 % higher classification accuracy compared to conventional Convolutional Neural Network (CNN).

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2367-2372
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• 2009

Recently, Many studies have been progressing to path-finding adapted dynamic information on the Terminal Based Navigation System(TBNS). The algorithms proposed are based on $A^*$ algorithm. Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost. Path-finding with an abstract graph which expresses real road network as a simple graph is proposed for reducing dependency of heuristic and exploring cost. In this paper, two abstract graph that are different method of construction, Homogeneous Node merging($AG^H$) and Connected Node Merging($AG^C$), are implemented. In result of evaluation of performance, $AG^C$ has better performance than $AG^H$ at construction cost and the number of node access but $AG^C$ has worse performance than AGH at exploring cost.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.245-248
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• 2009

Recently, Many studies have been progressing to path-finding with dynamic information on the Terminal Based Navigation System(TBNS). However, the most of existing algorithms are based on $A{\ast}$ algorithm. Path-finding algorithms which use heuristic function may occur a problem of the increase of exploring cost in case of that there is no way determined by heuristic function or there are 2 way more which have almost same cost. In this paper, two abstract graph(AG) that are different method of construction, Homogeneous Node merging($AG^H$) and Connected Node Merging($AG^C$), are implemented. The abstract graph is a simple graph of real road network. The method of using the abstract graph is proposed for reducing dependency of heuristic and exploring cost. In result of evaluation of performance, $AG^C$ has better performance than $AG^H$ at construction cost but $AG^C$ has worse performance than $AG^H$ exploring cost.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.1-6
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• 2022

This paper deals with the maximum cardinality matching(MCM) problem. The augmenting path technique is well known in MCM. MCM is obtained by $O({\sqrt{n}}m)$ time complexity augmenting path algorithm for the general graph, and O(m log n) algorithm for the bipartite graph. On the other hand, this paper suggests O(n) linear time algorithm. The proposed algorithm based on the basic principle of as possible as largest selected inter-vertex edges in order to obtain the MCM. This paper simply selects edge {u,𝜐} that the minimum degree vertex u and minimum degree vertex 𝜐 in N_G(u) 𝜈(G)=k times iteration. For various general and bipartite graphs experimental data, this algorithm can be get the 𝜈(G) exactly.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.51-61
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• 2012

This paper suggests an algorithm that obtains Directed Graph Minimum Spanning Tree (DMST), using Prim MST algorithm which is Minimum Spanning Tree (MST) of undirected graph. At first, I suggested the Prim DMST algorithm that chooses Minimum Weight Arc(MWA) from out-going nodes from each node, considering differences between undirected graph and directed graph. Next, I proved a disadvantage of Prim DMST algorithm and Chu-Liu/Edmonds DMST (typical representative DMST) of not being able to find DMST, applying them to 3 real graphs. Last, as an algorithm that can always find DMST, an advanced Prim DMST is suggested. The Prim DMST algorithm uses a method of choosing MWA among out-going arcs of each node. On the other hand, the advanced Prim DMST algorithm uses a method of choosing a coinciding arc from the out-going and in-going arcs of each node. And if there is no coinciding arc, it chooses MWA from the out-going arcs from each node. Applying the suggested algorithm to 17 different graphs, it succeeded in finding the same DMST as that found by Chu-Liu/Edmonds DMST algorithm. Also, it does not require such a complicated calculation as that of Chu-Liu/Edmonds DMST algorithm to delete the cycle, and it takes less time for process than Prim DMST algorithm.