• 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.

• Journal of Korea Game Society
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• v.17 no.6
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• pp.51-62
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• 2017

The augmented reality toy-game is a kind of new game genre that can be seen within the concept of augmented reality games, and it is a term to refer to the content or hardware that plays the game using the toy of the real world. This study aims to analyze the elements for the model of toy-game development based on the augmented reality. This study analyzed three characteristics of toy game which are different from other games based on existing related research. and have selected important factors to consider when developing augmented reality toy-game. A questionnaire was conducted to determine the suitability of the development elements derived, and the analysis and verification of the factors derived using an exploratory analysis method. As a result, it showed a reasonable outcome of the selection of variables, with the exception of some of the questions, and the classification results of the multi-dimensional scaling methods were also classified as reasonable in the clustering analyses.

• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.109-133
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• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.

• Journal of Korea Technology Innovation Society
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• v.15 no.3
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• pp.500-532
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• 2012

This study analyzed the technology innovation strategies of knowledge-based companies in the semiconductor IP industry. The theoretical approaches of this study are to i) the creation, protection and utilization of knowledge and innovation, ii) value creation from innovation, iii) modularity, timing of market entry, and the emergence and competition of standard (dominant design). Based on the theoretical analysis, I presented exploratory research hypotheses. Ultimately, this study examined the proposed hypotheses by conducting case studies on the technology innovation strategy of two leading knowledge-based companies in the semiconductor IP industry: ARM and INTEL. First, knowledge-based companies entering in the early stage of the technology cycle select the vertically-integrated technology strategy because of lower access to complementary knowledge assets, and maintain the vertically-integrated technology strategy despite the environmental change-driven differentiation of industry's value chain. Second, knowledge-based companies entering in the later stage of the technology cycle prefer the contract-based technology strategy because of its increased accessibility to complementary knowledge assets, and choose a different path of innovation strategies depending on whether their asset has the feature of discontinuity or not.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.199-209
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• 2019

The importance of business strategy for successful social media engagement has quickly increased as more businesses engage in social media. The importance is even greater for startup companies because startup companies are genuinely new to business, and they need to increase their presence in the market, and quickly access future customers. The objective of this paper lies in exploring key indicators of social media engagements by selected startup companies. The key indicators include two aspects of social media usages by the companies: i) overall social media activities, and ii) properties of network structure of the information flow platform provided by social media service. To better assess and evaluate the key indicators of social media usages by startup companies, the indicators will be compared with those of selected large established companies. Twitter is selected as a social media service for the analysis of this paper, and using Twitter REST API, data regarding the key indicators of overall Twitter activities and the Twitter follower-network of each company in the sample are collected. Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of the follower-network structures and to compare the characteristics between startup companies and established companies. The results show that most indicators are significantly different across startup companies and established companies. One key interesting finding is that the startup companies have proportionally more influencers in their follower-networks than the established companies have. Another interesting finding is that the follower-networks of startup companies in the sample have higher modularity and higher transitivity, suggesting that the startup companies tend to have a proportionally larger number of communities of users in their follower-networks, and the users in the networks are more tightly connected and cohesive internally. The key business implication for the future social media engagement efforts by startup companies in general is that startup companies may need to focus on getting more attention from influencers and promoting more cohesive communities in their follower-networks to appreciate the potential benefits of social media in the early stage of business of startup companies.

• 한국정보교육학회:학술대회논문집
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• 2005.08a
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• pp.401-408
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• 2005

본 연구는 학습자 중심의 구성주의 학습 모형인 문제중심학습( Problem-Based Learning: PBL) 모형 개발을 통한 효과적인 HTML 학습 방안의 탐색을 위해 수행되었다. 초등학생이 HTML( Hyper Text Markup Language )학습을 통해 프로그래밍을 학습할 때 단순문법을 익히는 것을 넘어 프로그래밍 언어를 자율적이고 창의적으로 활용하기 위해서는 고차원적인 자기 주도적 학습 능력과 문제 해결 능력이 요구된다. 이를 위해 본 논문은 문제중심학습의 기존모형들이 갖고 있는 특징을 기반으로 하여 개발되었다. 본 연구의 문제중심학습의 절차는 문제와의 만남- 문제의 해결 전략 세우기- 문제 해결을 위한 정보수집- 문제의 해결 -평가 단계와 같다. 학습과정 에세이 기록을 통해 학습절차를 설계하고 과정을 돌이킬 수 있으며 피드백 과정을 통하여 학습의 결손을 방지하도록 하였다. 구성주의 학습 모형인 문제중심학습(PBL)을 HTML 언어교육에 적용 할 경우 학습자의 자기 주도적 학습 능력과 의사소통능력, 창의력 논리력을 키울 수 있을 것으로 기대된다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.67-69
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• 2001

최근 이미지나 멀티미디어 데이터와 같은 비정형 데이터의 검색을 보다 효과적으로 수행하기 위한 연구가 활발하게 진행되어 왔다. 비정형 데이터를 검색하기 위해서는 비정형 데이터를 다차원의 특징 벡터로 변환하고, 그것을 다차원 색인구조를 이용해 색인한다. 따라서 이러한 비정형 데이터를 효율적으로 색인 할 수 있는 다차원 색인구조가 요구되고 있다. 이 논문에서는 데이터를 벡터 근사치로 표현한 후 이를 트리 형태로 구성하여 검색이 효율을 높이는 다차원 데이터를 위한 색인구조 VA(Vector Approximate)-트리를 제안한다. 이 논문에서 제안하는 VA-트리는 VA-파일과 K-D-B-트리 구조를 기반으로 하고 있다. VA-트리는 적은 비트를 이용하여 다차원 공간을 표현하기 위해 노드내의 모든 정보를 비트로 표현한다. 중간노드의 비트 형태 엔트리는 하위노드에 포함된 정보를 의미하고 있어 탐색을 효율적으로 수행할 수 있도록 한다. 실험을 통한 성능평가를 수행하여 제안된 색인구조의 우수함을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.370-372
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• 2001

본 논문에서는 컬러 정지 영상을 대상으로 상반신 인물 영상이 입력되었을 때, 얼굴 영역을 추출하고 검증하는 방법을 제안한다. 본 논문의 얼굴 추출과정은 1단계로 영상 내 피부색 영역을 추출한 다음, 후보 영역들에 대한 공간적 제한조건을 이용하여 1차 얼굴 후보 영역을 결정한다. 2단계에서는 얼굴 구성 요소 중 가장 두드러진 특징으로서 눈 영역을 탐색하고, 눈 영역을 기준으로 한국인의 얼굴에 대한 구조적 통계값을 적용한다. 이로서 얼굴 포함 최소 사각형 후보 영역을 결정한다. 마지막 3단계에서는 영상 내 색상 정보와 공간 정보 그리고 구조적 통계치로부터 결정된 얼굴 후보 영역에 대하여 얼굴 영역의 텍스춰(texture)를 Wavelet Packet Analysis를 이용해 조사함으로써 얼굴 영역을 확정하게 된다. 일반적으로 2단계에서 대부분의 얼굴 영역이 결정되지만 3단계에서 얼굴 내 텍스춰 정보를 활용하면 보다 적절한 얼굴 포함 사각형의 범위를 결정할 수 있었다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.325-332
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• 2000

데이터마이닝 분야에서 시계얼 데이터(time-series data)내에서 숨어 있는 순차패턴의 발견은 상품(Items)이나 어떤 사건(Event)과 같이 데이터의 특징이 명확한 대상에 대한 연구는 많이 되어왔으나 수치 값을 가지는 시계열 데이터에서 이들 내부에 숨어 있는 패턴을 발견하는 것은 최근에 관심을 가지게 되었다. 우리는 시계열 데이터를 시간적 변화에 따라 값의 변화 경향(Trend)이 같은 데이터 그룹을 패턴 요소인 벡터 (Vestor)로 표현하여 이들을 이용해서 흥미로운 패턴들을 발견한다. 이와 같은 벡터적인 표현으로 우리는 벡터들 간의 포함관계를 적용해 모든 가능한 형태의 패턴 발견을 목적으로 한다. 또한 경향성을 가진 패턴 요소를 사건(Event)과 같이 취급함으로써 다양한 종류의 시계열 데이터가 동시에 발생될 때 이들 상호간에 연관된 시간적 패턴을 찾을 수 있다. 따라서 이 연구에서 제안하는 경향성을 기초로 한 순차패턴의 탐식은 기업내부의 판매실적의 변화 패턴이나, 고객의 구매 행동분석에 적용이 가능하리라 여겨진다

• Korean Journal of Social Welfare
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• v.63 no.1
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• pp.137-158
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• 2011

This study explore nonlinear relationship between volunteer time spending and psychosocial well-being among the old adults, on the ground of Socioemotional Selectivity Theory. This is secondary analysis using the 2009 National Old Aging Volunteer Survey data including 645 cases. The simple and multiple line graph method are implemented. As a result, this paper shows a nolinerar relationship which the low level of psychosocial well-being at non volunteer status is increasing to the paticular time spending, and then decreases after high level participation like U-shape. Male, over the 75 and not good health status has showed lower level of volunteer time spending. It can be founded that volunteering activity contributes to improve a psychosocial well-being in old life, but the desirable volunteer time spending is medial about 100-200 yearly not over the 700.