• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.419-430
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• 2009

Reading comprehension (RC) systems are to understand a given text and return answers in response to questions about the text. Many previous studies extract sentences that are the most similar to questions as answers. However, texts for RC tests are generally short and facts about an event or entity are often expressed in multiple sentences. The answers for some questions might be indirectly presented in the sentences having few overlapping words with the questions. This paper proposes a conceptual graph matching method towards RC tests to extract answer strings. The method first represents the text and questions as conceptual graphs, and then extracts subgraphs for every candidate answer concept from the text graph. All candidate answer concepts will be scored and ranked according to the matching similarity between their sub-graphs and question graph. The top one will be returned as answer seed to form a concise answer string. Since the sub-graphs for candidate answer concepts are not restricted to only covering a single sentence, our approach improved the performance of answer extraction on the Remedia test data.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.556-564
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• 2003

A Star and Pancake graph also have such a good property of a hypercube and have a low network cost than the hypercube. A Macro-star graph which has the star graph as a basic module has the node symmetry, the maximum fault tolerance, and the hierarchical decomposition property. And, it is an interconnection network which improves the network cost against the Star graph. In this paper, we propose a method to embed between Star graph, Pancake graph, and Macro-star graph using the edge definition of graphs. We prove that the Star graph $S_n$ can be embedded into Pancake graph $P_n$ with dilation 4, and Macro-star graph MS(2,n) can be embedded into Pancake graph $P_{2n＋1}$ with dilation 4. Also, we have a result that the embedding cost, a Pancake graph can be embedded into Star and Macro-star graph, is O(n).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.298-301
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• 2012

This paper presents a graph-based method of detecting and tracking moving objects in H.264/SVC bitstreams for video surveillance applications that makes use the information from spatial base and enhancement layers of the bitstreams. In the base layer, segmentation of real moving objects are first performed using a spatio-temporal graph by removing false detected objects via graph pruning and graph projection, followed by graph matching to precisely identify the real moving objects over time even under occlusion. For the accurate detection and reliable tracking of moving objects in the enhancement layer, as well as saving computational complexity, the identified block groups of the real moving objects in the base layer are then mapped to the enhancement layer to provide accurate and efficient object detection and tracking in the bitstreams of higher resolution. Experimental results show the proposed method can produce reliable results with low computational complexity in both spatial layers of H.264/SVC test bitstreams.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.27-34
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• 2005

In this paper, we propose the Q-edge labeling method related to the graph embedding problem in binomial trees. This result is able to design a new reliable interconnection networks with maximum connectivity using Q-edge labels as jump sequence of circulant graph. The circulant graph is a generalization of Harary graph which is a solution of the optimal problem to design a maximum connectivity graph consists of n vertices End e edgies. And this topology has optimal broadcasting because of having binomial trees as spanning tree.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.59-66
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• 2004

We define a SG(String Graph) and an ASG(Alive String Graph) to the purpose to do static analysis. For a life and death judgment, we apply the rule to the situation which the stone is included and not included. We define the rules that are SR(String Reduction), ER(Empty Reduction), ET(Edge Transform), and CG(Circular Graph), when the stone is not included. We define the rules that are DESR(Dead Enemy Strings Reduction) and SCSR(Same Color String Reduction), when the stone is included. We evaluate a SG that it is an ASG or not by using rules. And we use APC(Articulation Point Check) nile according to number of articulation points lot a life and death judgment. The performance of our method has been tested on the problem set IGS_31_counted form the Computer Go Test Collection. The test set contains 11,191 Points and 1,123 Strings. We obtain 92.5% accuracy of Points and 95.7% accuracy of Strings.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.103-110
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• 2015

Sentiment lexicon is an essential element for expressing sentiment on a text or recognizing sentiment from a text. We propose a graph-based semi-supervised learning method to construct a sentiment dictionary as sentiment lexicon set. In particular, we focus on the construction of domain-specific sentiment dictionary. The proposed method makes up a graph according to lexicons and proximity among lexicons, and sentiments of some lexicons which already know their sentiment values are propagated throughout all of the lexicons on the graph. There are two typical types of the sentiment lexicon, sentiment words and sentiment phrase, and we construct a sentiment dictionary by creating each graph of them and infer sentiment of all sentiment lexicons. In order to verify our proposed method, we constructed a sentiment dictionary specific to the movie domain, and conducted sentiment classification experiments with it. As a result, it have been shown that the classification performance using the sentiment dictionary is better than the other using typical general-purpose sentiment dictionary.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• Journal of the Korean Mathematical Society
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• v.55 no.5
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• pp.1091-1101
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• 2018

We concern in this paper the graph Kazdan-Warner equation $${\Delta}f=g-he^f$$ on an infinite graph, the prototype of which comes from the smooth Kazdan-Warner equation on an open manifold. Different from the variational methods often used in the finite graph case, we use a heat flow method to study the graph Kazdan-Warner equation. We prove the existence of a solution to the graph Kazdan-Warner equation under the assumption that $h{\leq}0$ and some other integrability conditions or constrictions about the underlying infinite graphs.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.8
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• pp.79-87
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• 1997

For the representation of a complex object, the object is decomposed into several parts, and it is described by these decomposed parts and their relations. In genral, the parts can be the primitive elements that can not be decomposed further, or can be decomposed into their subparts. Therefore, the hierarchical description method is very natural and it si represented by a hierarchical attributed graph whose vertieces represent either primitive elements or graphs. This graphs also have verties which contain primitive elements or graphs. When some uncertainty exists in the hierarchical description of a complex object either due to noise or minor deformation, a probabilistic description of the object ensemble is necessary. For this purpose, in this paper, we formally define the hierarchical attributed random graph which is extention of the hierarchical random graph, and erive the equations for the entropy calculation of the hierarchical attributed random graph, and derive the equations for the entropy calculation of the hierarchical attributed random graph. Finally, we propose the application areas to use these concepts.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.425-430
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• 2005

Many problems can be formulated in terms nf graphs and thus solved by graph-theoretic algorithms. This paper is concerned with finding paths between nodes over the distributed and overlapped graphs. The proposed method allows multiple agents to cooperate to find paths without merging the distributed graphs. For each graph there is a designated agent which is charged of providing path-finding service for hot graph and initiating the path-finding tasks of which path starts from the graph. The proposed method earlier on constructs an abstract graph so-called viewgraph for the distributed overlapped graphs and thus enables to extract the information about how to guide the path finding over the graphs. The viewgraph is shared by all agents which determine how to coordinate other agents for the purpose of finding paths. Each agent maintains the shortest path information among the nodes which are placed in different overlapped subgraphs of her graph. Once an agent is asked to get a path from a node on her graph to another node on another's graph, she directs other agents to provide the necessary information for finding paths.