• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.176-179
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• 2007

In this paper, we develop the path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1].

• International Journal of Contents
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• v.7 no.3
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• pp.19-28
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• 2011

This paper presents a fast and practical motion synthesis algorithm for massive number of quadruped animals. The algorithm constructs so called speed maps that contain a set of same style motions but different speed from a single cyclic motion by using IK(Inverse Kinematics) solver. Then, those speed maps are connected each other to form a motion graph. At run time, given a point trajectory that obtained from user specification or simulators, the algorithm retrieves proper speed motions from the graph, and modifies and stitches them together to create a long seamless motion in real time. Since our algorithm mainly targets on the massive quadruped animal motions, the motion graph create wide variety of different size of characters for each trajectory and automatically adjusted synthesized motions without causing artifact such as foot skating. The performance of algorithm is verified through several experiments

• Management Science and Financial Engineering
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• v.11 no.1
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• pp.49-61
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• 2005

We consider the graph disconnection problem, which is to find a set of edges such that the total cost of destroying the edges is no more than a given budget and the weight of nodes disconnected from a designated source by destroying the edges is maximized. The problem is known to be NP-hard. We present an integer programming formulation for the problem and develop an algorithm that includes a preprocessing procedure for reducing the problem size, a heuristic for providing a lower bound, and a cutting plane algorithm for obtaining an upper bound. Computational results for evaluating the performance of the proposed algorithm are also presented.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2323-2333
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• 1998

Software engineers generally analyze the program behavior under the test case that revealed the error, not under any teneric est case. In this paper we discuss the dynamic slice consisting of all statements that actually affect the value of a variable occurrence for a given program input. We propose an efficient algorithm to make dynamic program slices. The eficiency of this algorithm is evaluated on some developed program. results are shown by a marking table of execution history. Dynamic Dependence Graph, and Reduced Dynamic Dependence Graph, Consequently, the efficiency of the proosed algorithm is also presented by the compariso with algorithm that was announced previously.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.107-116
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• 2002

A hybrid image segmentation algorithm is proposed which integrates edge-based and region-based techniques through the watershed algorithm. First, by using mean curvature diffusion coupled to min/max flow, noise is eliminated and thin edges are preserved. After images are segmented by watershed algorithm, the segmented regions are combined with neighbor regions. Region adjacency graph (RAG) is employed to analyze the relationship among the segmented regions. The graph nodes and edge costs in RAG correspond to segmented regions and dissimilarities between two adjacent regions respectively. After the most similar pair of regions is determined by searching minimum cost RAG edge, regions are merged and the RAG is updated. The proposed method efficiently reduces noise and provides one-pixel wide, closed contours.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.331-341
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• 2002

Traditional slicing techniques make slices through dependence graph and improve the accuracy of slices. However, traditional slicing techniques require many vertices and edges in order to express a data communication link because they are based on static slicing techniques. Therefore the graph becomes very complicated. We propose the representation of a dynamic system dependence graph so as to process the slicing of a software system that is composed of related programs in order to process certain jobs. We also propose programs on efficient slicing algorithm using relations of relative tables in order to compute dynamic slices of a software system. Using a marking table from results of the proposed algorithm can make dynamic system dependence graph for dynamic slice generation. Tracing this graph can generate final slices. We have illustrated our example with C program environment. Consequently, the efficiency of the proposed dynamic system dependence graph technique is also compared with the dependence graph techniques discussed previously. As the results, this is certifying that the dynamic system dependence graph is more efficient in comparison with system dependence graph.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.231-234
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• 2001

This paper proposes a face recognition technique that effectively combines elastic graph matching (EGM) and Fisherface algorithm. EGM as one of dynamic lint architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, the linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional method, the proposed approach could obtain satisfactory results in the perspectives of recognition rates and speeds. Especially, we could get maximum recognition rate of 99.3% by leaving-one-out method for the experiments with the Yale Face Databases.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.529-532
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• 1988

This paper proposes a graph matching algorithm based on simulated annealing, which assures the globally optimal solution for circuit partitioning for the placement in the rectilinear region occurring as a result of the pre-placement of some macro cells, or onto the nonplanar surface in some military or space applications. The circuit graph ($G_{C}$) denoting the circuit topology is formed by a hierarchical bottom-up clustering of cells, while another graph called region graph ($G_{R}$) represents the geometry of a planar rectilinear region or a nonplanar surface for circuit placement. Finding the optimal many-to-one vertex mapping function from $G_{C}$ to $G_{R}$, such that the total mismatch cost between two graphs is minimal, is a combinatorial optimization problem which was solved in this work for various examples using simulated annealing.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.267-271
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• 2005

The goal of data mining is to extract new and useful knowledge from large scale datasets. As the amount of available data grows explosively, it became vitally important to develop faster data mining algorithms for various types of data. Recently, an interest in developing data mining algorithms that operate on graphs has been increased. Especially, mining frequent patterns from structured data such as graphs has been concerned by many research groups. A graph is a highly adaptable representation scheme that used in many domains including chemistry, bioinformatics and physics. For example, the chemical structure of a given substance can be modelled by an undirected labelled graph in which each node corresponds to an atom and each edge corresponds to a chemical bond between atoms. Internet can also be modelled as a directed graph in which each node corresponds to an web site and each edge corresponds to a hypertext link between web sites. Notably in bioinformatics area, various kinds of newly discovered data such as gene regulation networks or protein interaction networks could be modelled as graphs. There have been a number of attempts to find useful knowledge from these graph structured data. One of the most powerful analysis tool for graph structured data is frequent subgraph analysis. Recurring patterns in graph data can provide incomparable insights into that graph data. However, to find recurring subgraphs is extremely expensive in computational side. At the core of the problem, there are two computationally challenging problems. 1) Subgraph isomorphism and 2) Enumeration of subgraphs. Problems related to the former are subgraph isomorphism problem (Is graph A contains graph B?) and graph isomorphism problem(Are two graphs A and B the same or not?). Even these simplified versions of the subgraph mining problem are known to be NP-complete or Polymorphism-complete and no polynomial time algorithm has been existed so far. The later is also a difficult problem. We should generate all of 2$^n$ subgraphs if there is no constraint where n is the number of vertices of the input graph. In order to find frequent subgraphs from larger graph database, it is essential to give appropriate constraint to the subgraphs to find. Most of the current approaches are focus on the frequencies of a subgraph: the higher the frequency of a graph is, the more attentions should be given to that graph. Recently, several algorithms which use level by level approaches to find frequent subgraphs have been developed. Some of the recently emerging applications suggest that other constraints such as connectivity also could be useful in mining subgraphs : more strongly connected parts of a graph are more informative. If we restrict the set of subgraphs to mine to more strongly connected parts, its computational complexity could be decreased significantly. In this paper, we present an efficient algorithm to mine frequent subgraphs that are more strongly connected. Experimental study shows that the algorithm is scaling to larger graphs which have more than ten thousand vertices.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.181-188
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• 2007

Undirected Rural Postman Problem(URPP) is a problem that finds a shortest tour traversing the given arcs at least once in a given network. The URPP is one of the basic network problems used in solving the various real-world problems. And it is known as NP-Complete. URPP is an arc-oriented problem that the direction of a tour in an arc has to be considered. Hence, In URPP, it is difficult to use the algorithm for Traveling Salesman Problem (TSP), which is a node-oriented problem, directly. This paper proposes the decoding algorithm using graph transformation in the genetic algorithm for URPP. That is, you can find the entire tour traversing without considering the direction of arcs by transforming the arc-oriented graph into the node-oriented graph. This paper compares the performances of the proposed algorithm with an existing algorithm. In the simulation results, the proposed algorithm obtained better than the existing algorithm