• Communications of the Korean Mathematical Society
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• v.12 no.1
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• pp.127-133
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• 1997

In order to study the limits of sequences appearing in, for example, stochastic process, A. V. Skorokhod has defined new function space topologies. We compare these topologies with the topology of compact convergence, the topology of pointwise convergence and others.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.3
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• pp.105-111
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• 2011

Landmark Spots in 2D gel electrophoresis are used in many methods of 2DEG spot matching. Landmark Spots are obtained manually and it is a bottle neck in the entire protein analysis process. Automated landmark spots detection is a very crucial topic in processing a massive amount of 2DGE data. In this paper, Automated landmark spot detection is proposed using point pattern matching and graph theory. Neighbor spots are defined by a graph theory to use and only a centered spot and its neighbor spots are considered for spot matching. Normalized Hausdorff distance is introduced as a criterion for measuring degree of similarity. In the conclusion, the method proposed in this paper can get about 50% of the total spot pairs and the accuracy rate is almost 100%, which the requirements of landmark spots are fully satisfied.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.23-28
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• 2020

A comment system is essential for communication on the Internet. However, there are also malicious comments such as inappropriate expression of others by exploiting anonymity online. In order to protect users from malicious comments, classification of malicious / normal comments is necessary, and this can be implemented as text classification. Text classification is one of the important topics in natural language processing, and studies using pre-trained models such as BERT and graph structures such as GCN and GAT have been actively conducted. In this study, we implemented a comment classification system using BERT, GCN, and GAT for actual published comments and compared the performance. In this study, the system using the graph-based model showed higher performance than the BERT.

• Journal of the Korea Convergence Society
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• v.3 no.4
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• pp.15-21
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• 2012

Image segmentation for object to extract data from ultrasound acquired is an essential preprocessing step for the effective diagnosis. Various image segmentation methods have been studied. In this study, interactive image segmentation method by graph cut algorithm is proposed to develop a variety of applications of vascular ultrasound imaging and diagnostics. General imaging and vascular ultrasound imaging segmentation by entering constrain condition such as foreground and background. In the future it will be able to develop new ultrasound diagnostics.

• Bulletin of the Korean Mathematical Society
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• v.54 no.6
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• pp.1883-1891
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• 2017

An optimally labelled graph of bandwidth 2 is an ordered pair (G, f) where G is a simple graph with bw(G) = 2 and $f:V(G){\rightarrow}[n]$ is a bijection such that bw(G, f) = 2. In this paper, the number of optimally labelled graphs of bandwidth two of order n is enumerated by counting linear forests.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.285-292
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• 2014

Intelligent systems are systems that mainly use knowledge rather than data or information. Therefore, knowledge representation is an important factor for intelligent system construction. Conceptual graph is a logical knowledge representation language which has graphical form and it can represent knowledge efficiently. It is, however, cumbersome to use conceptual graphs directly for programming. Various tools were developed to overcome this difficulties. In this paper, we survey on functions and characteristics of conceptual graph tools that can be utilized for constructing intelligent systems by using conceptual graphs. The result of this survey will be very helpful to use conceptual graphs for development of intelligent systems.

• 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 KIISE
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• v.42 no.12
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• pp.1561-1567
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• 2015

Abstract Semi-supervised learning is an area in machine learning that employs both labeled and unlabeled data in order to train a model and has the potential to improve prediction performance compared to supervised learning. Graph-based semi-supervised learning has recently come into focus with two phases: graph construction, which converts the input data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. The inference is based on the smoothness assumption feature of semi-supervised learning. In this study, we propose an enhanced label inference algorithm by incorporating the importance of each vertex. In addition, we prove the convergence of the suggested algorithm and verify its excellence.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.1-10
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• 2022

Single-view reconstruction (SVR) is a fundamental method in computer vision. Often used for reconstructing human-made environments, the Manhattan world assumption presumes that planes in the real world exist in mutually orthogonal directions. Accordingly, this paper addresses an automatic SVR algorithm for Manhattan worlds. A method for estimating the directions of planes using graph-cut optimization is proposed. After segmenting an image from extracted line segments, the data cost function and smoothness cost function for graph-cut optimization are defined by considering the directions of the line segments and neighborhood segments. Furthermore, segments with the same depths are grouped during a depth-estimation step using a minimum spanning tree algorithm with the proposed weights. Experimental results demonstrate that, unlike previous methods, the proposed method can identify complex Manhattan structures of indoor and outdoor scenes and provide the exact boundaries and intersections of planes.

• Communications of Mathematical Education
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• v.32 no.4
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• pp.477-493
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• 2018

The six core competencies included in the mathematics curriculum Revised in 2015 are problem solving, reasoning, communication, attitude and practice, creativity and convergence, information processing. In particular, the creativity and convergence competency is very important for students' enhancing much higher mathematical thinking. Based on the creativity and convergence competency, this study selected the five elements of the creativity and convergence competency such as productive thinking element, creative thinking element, the element of solving problems in diverse ways, and mathematical connection element, non-mathematical connection element. And also this study selected the content(chapter) of the graph in the seventh grade mathematics textbook. By the subject of the ten kinds of textbook, this study examined how the five elements of the creativity and convergence competency were shown in each textbook.