• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1334-1341
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• 2006

정보가시화는 정보를 기하학적으로 표현하는 연구 분야로 정량적 정보를 테이블, 도표 등의 형태로 표현하는 정량 정보 가시화와 그래프나 네트워크와 같은 구조적 자료를 기하학적으로 표현하는 graph visualization이 있다. 본 연구에서는 그래프 기반의 정보 가시화를 이용한 회의 정보 가시화 프로그램을 소개하고자 한다. 이는 연구개발이나, 프로젝트 관리, 브레인스토밍 등의 회의에 있어서 태스크, 자원, 일정, 문서 등으로 구성되는 회의정보를 대상으로 한다. 최초 정보 생성, 정보 수정, 정보 가시화 기능을 갖고 있으며 그래프 자동 배치 모듈, 가시화 모듈, 사용자 인터페이스 모듈 등으로 구성되어 있다. 그래프 자동 배치는 오픈 소스로 제공되는 GraphViz를 사용하였고, 가시화는 OpenGL을 이용하였다. 회의 정보들 사이의 복잡한 관계를 그래프 구조로 표현하여 업무와 자원의 분배, 관련된 문서 검색을 쉽게 하여 회의 정보를 직관적이고, 빠르고 쉽게 조작하고, 이해하는데 유용하다.

• Journal of Korea Multimedia Society
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• v.5 no.1
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• pp.114-119
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• 2002

Realtime assignment of railways is an important component in the railway control systems. To solve this problem, we must exactly represent the track topology. Graph is a proper data structure for representing general network topologies, but not Proper for track topologies. In this paper, we define a new data structure, railway graph, which can exactly represent topologies of railway networks. And we describe a path search algorithm in the defined railway graph, and a top-down approach for designing railway network by the Proposed graph.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.471-476
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• 2024

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

• 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.

• School Mathematics
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• v.10 no.1
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• pp.43-61
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• 2008

This study is based on the pedagogical aspect that both connections of mathematical concepts and a geometric approach enhance the understanding of structures in school mathematics. This study is to investigate the graphical properties of quadratic functions such as symmetry, coordinates of vertex, intercepts and congruency through the geometric properties of graphs of linear functions. From this investigation this study would give suggestions on a new pedagogical perspective about current teaching and learning methods of quadratic function graphs which is focused on routine algebraic transformation of the completing squares. In addition, this study would provide the topic of quadratic function graphs with the understanding of geometric perspective.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.1289-1292
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• 2000

그래프는 실세계의 많은 문제를 푸는데 아주 강력한 방법을 제공한다. 이와 같은 그래프를 효율적으로 표현하기 위한 자료구조와 그래프 연산에 대한 알고리즘이 개발되어 왔다. 본 논문에서는 그래프를 관계형 테이블로 표현하고, 그래프에 대한 연산과 알고리즘을 라이브러리화 하는 방법을 제안한다. 제안한 방법은 관계형 데이터베이스를 이용하여 개발할 수 있으며, 개발된 라이브러리는 그래프로 모델링되는 실세계의 많은 문제를 푸는데 손쉽게 활용할 수 있을 것이다. 또한, 방대한 양의 그래프를 효율적으로 관리할 수 있으며 다수의 사용자가 공유할 수도 있을 것이다.

• Journal of The Korean Association For Science Education
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• v.27 no.4
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• pp.285-296
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• 2007

Graph is a major aspect of science textbooks. In this study, we investigated graph types and characteristics used in high school earth science subject by comparative analysis of science textbooks. The results of the analysis revealed that line graph and contour map was the most widely used graph types in earth science. Among line graphs, multiple line graph and YX graph was dominant. Comparing earth science graphs with other science graphs, earth science graphs exhibited superior in the number and variety. In earth science graphs, the portion of line graph was small, but the portion of contour map and scatter graph was larger than that of other science graphs. YX graph was the most specific graph type in earth science textbooks. The results of our study have implications for reform in function and structure of graph. We suggest that future studies be focused on students' ability of earth science graph interpretation.

• Proceedings of the Korean Society for Information Management Conference
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• 1994.12a
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• pp.29-32
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• 1994

과거 실세계 정보를 처리하기 위한 방법으로는 관계형데이타베이스, 객체지향데이타베이스. 지식베이스시스템 등이 연구되었다. 이들 방법은 제한된 정보표현 및 정보의 운영 및 접근방법 등의 문제점을 갖는다. 정보의 구조화는 정보의 의미를 분석하고 정보의 특성에 적합한 융통성 있는 정보모델을 필요로 한다. 본 논문에서는 방대한 양의 정보처리 및 다양한 형태의 표현, 동적 변환 등의 정보특성을 효율적으로 처리하기 위한 정보모델로 의미구조그래프를 사용하여 기존 시스템의 문제점을 해결하기 위한 방법을 제안한다. 의미구조그래프를 사용한 정보구조화는 정보의미를 분석할 수 있으며, 정보의 표현의 융통성을 제공한다. 의미구조그래프는 노드와 링크를 갖는 확장된 하이퍼그래프를 사용하였으며, 정보구조화를 위한 대상데이타로 문화예술 분야의 관련 정보를 실험하였다.

• Journal of Korea Game Society
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• v.4 no.3
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• pp.55-64
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• 2004

A dynamic graph is suitable for representing and managing dynamic changable obstacles or terrain information in 2D/3D games such as RPG and Strategy Simulation Games. We propose a dynamic hierarchical graph model with fixed level to perform a quick A* path finding. We divide a graph into subgraphs by using space classification and space model, and construct a hierarchical graph. And then we perform a quick path fading on the graph by using our dynamic graph operators. With our experiments we show that this graph model has efficient properties for finding path in a dynamic game environment.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.