• Communications of the Korean Mathematical Society
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• v.16 no.1
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• pp.1-24
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• 2001

어떤 유향그래프 D의 경쟁그래프란 D와 같은 꼭지점들의 집합을 갖고 두 개의 꼭지점 x와 y가 변으로 연결되어있는 것과 동치인 조건이 D에서 어떤 꼭지점 z에 대하여 유향변(x,z)와 (y,z)가 존재하는 것인 그래프로 정의된다. 어떤 그래프 G의 경쟁수란 G에 첨가하여 생기는 그래프가 유향 회로가 없는 유향그래프의 경쟁그래프가 되도록 하는 고립된 꼭지점의 최소수로 정의된다. 갱쟁그래프의 개념은 생태학적 위상공간의 최소차원을 결정하는 수단으로 1968에 Cohen에 의하여 도입되었다. 경쟁그래프와 그것의 변형들은 잡음이 많은 통신로 상에서의 교신, 라디오 송신기에 주파수 부어하기, 복잡한 경제 체계와 에너지 체계에 응용된다. 이 논문에서는 경쟁 그래프와 그 변형들, 경쟁수와 그 변형들에 대하여 소개하며 그것들에 대한 중요한 결과들을 개관하고 미해결 문제들을 제시하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.347-348
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• 2016

스토리에 의해서 콘텐츠 산업의 경쟁력이 결정되고 콘텐츠에 대한 흥행과 보급에 핵심 요소가 된다. 현재까지의 스토리에 대한 연구는 대부분 인문학의 분야에서 이루어지고 있다. 인문학에서는 스토리를 창작하는 방법과 스토리에 대한 해석에 주로 초점이 맞추어져서 연구가 이루어지고 있다. 콘텐츠 산업의 핵심요소인 스토리를 창작하거나 해석하기 위해서는 스토리를 한눈에 보고 이해하거나 설명하기 위한 다양한 해석 방법과 시각화 방법이 필요하다. 따라서 본 논문에서는 스토리의 무대가 되는 공간 정보와 인물 정보가 함게 고려된 장면 그래프에 대한 연구를 제시하고 설명한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1682-1687
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• 2020

We study the problem of sampling a subset of nodes of graphs for bandlimited graph signals such that the signal values on the sampled nodes provide the most information in order to reconstruct the original graph signal. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound of the reconstruction error to reduce the complexity of the selection process. We further simplify the upper bound by applying useful approximations to propose a low-weight greedy selection process that is iteratively conducted to find a suboptimal sampling set. Through the extensive experiments for various graphs, we inspect the performance of the proposed algorithm by comparing with different sampling set selection methods and show that the proposed technique runs fast while preserving a competitive reconstruction performance, yielding a practical solution to real-time applications.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.120-128
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• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.166-172
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• 2014

Image classification is an important problem in computer vision. However, it is a very challenging problem due to the variability, ambiguity and scale change that exists in images. In this paper, we propose a method of a hypergraph based modeling can consider the higher-order relationships of semantic attributes of a scene image and apply it to a scene image classification. In order to generate the hypergraph optimized for specific scene category, we propose a novel search method based on a probabilistic subspace method and also propose a method to aggregate the expression values of the member semantic attributes that belongs to the searched subsets based on a linear transformation method via likelihood based estimation. To verify the superiority of the proposed method, we showed that the discrimination power of the feature vector generated by the proposed method is better than existing methods through experiments. And also, in a scene classification experiment, the proposed method shows a competitive classification performance compared with the conventional methods.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.301-306
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• 2000

최근 데이터 웨어하우징의 활발한 구축과 우수고객 확보를 위한 치열한 경쟁으로 데이터 마이닝은 많은 업체의 큰 관심을 끌고있다. 본 연구는 풍부한 알고리즘과 과학적 그래프를 제공하여 사용자로 하여금 최상의 데이터 마이닝 효과를 거둘 수 있도록 Statserver를 핵심 엔진으로 사용한 인터넷 기반의 데이터 마이닝 솔루션 개발에 관한 편이다

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.288-289
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• 2021

4차산업혁명과 함께 기존 산업구조가 급속하게 변화하고 기술패권주의가 심화되면서, 기술 패권의 승패에 따라 국가의 글로벌 경쟁력이 크게 좌우된다. 세계 주요국들은 기술경쟁력 확보를 위해 기술혁신과 기술연대의 경쟁을 벌이고 있고, 우리나라도 이러한 동향 속에서 적극적인 R&D 연구 투자와 정책적 지원을 통해 미래 산업 분야의 기술경쟁력 확보를 위해 노력하고 있다. 현재 중국에 의한 기술 탈취나 인력 유출이 발생하고 있고, 이는 산업경쟁력 상실로 이어져 막대한 경제적 피해를 야기할 수 있다. 기술경쟁력을 잃지 않기 위해, 반드시 우리의 산업기술 보호 수단도 마련되어야 한다. 선제적으로 중요한 산업기술을 적절히 식별하여 중요도에 따라 보호수단을 이행하는 것이 산업기술 보호의 시작일 것이다. 이에 따라, 본 논문에서는 지식그래프와 임베딩 모델을 활용하여 우리나라의 핵심산업분야 중 하나인 반도체 분야의 기술문서를 중요도에 따라 수직적으로 분류할 수 있는 방안에 대해 연구하고자 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.2
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• pp.12-17
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• 2010

An automatic facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.705-711
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• 2003

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in !be image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.934-940
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• 2003

In this paper, hair beauty fashion design feature points detection system is proposed. A hair models and hair face is represented as a graph where the nodes are placed at facial feature points labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between hair models and the input image. This matching hair model works like random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background. pose variations and distorted by accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.