• 한국경영과학회지
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• 제40권4호
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• pp.1-18
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• 2015

This paper proposes relational capability as a main driver of constructing inter-organizational collaboration networks. Based on social network theory and relational view literature, three components of relational capability are constructed and implemented by an agent-based model. The components include organizational capability, structural capability, and trust between a partner and a focal firm. These three components are updated by two micro mechanisms: structural mechanism and relational mechanism. Structural mechanism is a feedback loop in which the relational capability increases structural capability and vice versa. Relational mechanism is a learning-by-doing process in which a focal firm experiences success or failure of collaboration and the experience increases or decreases cumulative trust in a partner firm. Result of agent-based simulation shows that a collaboration network emerges through interactions of firm's relational capabilities and the characteristics of emerged networks vary with the contribution of structural capability and trust to relational capability. Specifically, in case structural capability contributes more to relational capability, the average degree centrality and collaboration proportion increases as time passes and enters into an equilibrium state. In that case, almost every firms participated in the network collaborates each other so that the emerged network becomes highly cohesive. In case trust contributes more to relational capability, the results are reversed. In an equilibrium state, the balance of contribution between structural capability and trust makes an emerged network larger and maximizes average degree centrality of the network.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.392-395
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• 2017

본 연구에서는 일 증발접시 증발량 산정을 위한 딥러닝 (deep learning) 모형의 적용성을 평가하였다. 본 연구에서 적용된 딥러닝 모형은 deep belief network (DBN) 기반 deep neural network (DNN) (DBN-DNN) 모형이다. 모형 적용성 평가를 위하여 부산 관측소에서 측정된 기상자료를 활용하였으며, 증발량과의 상관성이 높은 기상변수들 (일사량, 일조시간, 평균지상온도, 최대기온)의 조합을 고려하여 입력변수집합 (Set 1, Set 2, Set 3)별 모형을 구축하였다. DBN-DNN 모형의 성능은 통계학적 모형성능 평가지표 (coefficient of efficiency, CE; coefficient of determination, $r^2$; root mean square error, RMSE; mean absolute error, MAE)를 이용하여 평가되었으며, 기존의 두가지 형태의 ANN (artificial neural network), 즉 모형학습 시 SGD (stochastic gradient descent) 및 GD (gradient descent)를 각각 적용한 ANN-SGD 및 ANN-GD 모형과 비교하였다. 효과적인 모형학습을 위하여 각 모형의 초매개변수들은 GA (genetic algorithm)를 이용하여 최적화하였다. 그 결과, Set 1에 대하여 ANN-GD1 모형, Set 2에 대하여 DBN-DNN2 모형, Set 3에 대하여 DBN-DNN3 모형이 가장 우수한 모형 성능을 나타내는 것으로 분석되었다. 비록 비교 모형들 사이의 모형성능이 큰 차이를 보이지는 않았으나, 모든 입력집합에 대하여 DBN-DNN3, DBN-DNN2, ANN-SGD3 순으로 모형 효율성이 우수한 것으로 나타났다.

• Journal of Information Science Theory and Practice
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• 제11권3호
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• pp.29-42
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• 2023

This study explored issues related to the library in the COVID-19 era in YouTube videos in Korea. This study performed social network analysis and topic modeling analysis by collecting 479 YouTube videos, 20,545 words, and 8,379 channels related to COVID-19 and the library from 2019 to 2020. The study results confirmed that YouTube, a social media platform, was used as an important medium to connect users and physical libraries and provide/promote online library services. In the study, major topics and keywords such as quarantine, vlog, and library identity during the COVID-19 pandemic, library services and functions, and introductions and user guides of libraries were derived. Additionally, it was identified that videos about COVID-19 and the library are being produced by various actors (news and media channels, libraries, government agencies, librarians, and individual users). However, the study also identified that the actor network is fragmented through the channel network, showing a low density or weak linkage, and that the centrality of the library in the actor network is weak.

• Journal of Information Science Theory and Practice
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• 제10권spc호
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• pp.12-24
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• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.2427-2445
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• 2016

In contrast to traditional "store-and-forward" routing mechanisms, network coding offers an elegant solution for achieving maximum network throughput. The core idea is that intermediate network nodes linearly combine received data packets so that the destination nodes can decode original files from some authenticated packets. Although network coding has many advantages, especially in wireless sensor network and peer-to-peer network, the encoding mechanism of intermediate nodes also results in some additional security issues. For a powerful adversary who can control arbitrary number of malicious network nodes and can eavesdrop on the entire network, cryptographic signature schemes provide undeniable authentication mechanisms for network nodes. However, with the development of quantum technologies, some existing network coding signature schemes based on some traditional number-theoretic primitives vulnerable to quantum cryptanalysis. In this paper we first present an efficient network coding signature scheme in the standard model using lattice theory, which can be viewed as the most promising tool for designing post-quantum cryptographic protocols. In the security proof, we propose a new method for generating a random lattice and the corresponding trapdoor, which may be used in other cryptographic protocols. Our scheme has many advantages, such as supporting multi-source networks, low computational complexity and low communication overhead.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 춘계학술대회 논문집
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• pp.651-652
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• 2009

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 춘계학술대회 논문집
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• pp.17-18
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• 2009

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2007년도 추계 국제학술대회
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• pp.120-120
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• 2007

• 한국의류학회지
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• 제41권5호
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• pp.858-871
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• 2017

The present study purported to understand the relationships between SNS appearance-related photo activity, body image and self-esteem among young women. Objectification Theory and Social Comparison Theory explain the process how young women's SNS appearance-related photo activity influence their body satisfaction through a perceived media pressure, objectification of their own bodies and appearance comparison. These process ultimately affect the self-esteem of young female users. A research model was proposed and related hypothesis were examined. We collected an online questionnaire from 400 female participants in their 20's. Data were analyzed using SPSS 23.0 and AMOS 18.0. Structural equation modeling analyses suggested that the proposed research model provided a good fit to the data and supported most hypothesis. The results indicated that the level of SNS appearance-related photo activity significantly influences young female user's body satisfaction and self-esteem. The findings of this study were consistent with previous literature on media and body image. Limitations and future research suggestions were also described.

• 상하수도학회지
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• 제26권1호
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• pp.1-12
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• 2012

The purpose of water distribution system is supplying water to users by maintaining appropriate pressure and water quality. For efficient monitoring of the water distribution system, determination of optimal locations for pressure monitoring is essential. In this study, entropy theory was applied to determine the optimal locations for pressure monitoring. The entropy which is defined as the amount of information was calculated from the pressure change due to the variation of demand reflected the abnormal conditions at nodes, and the emitter function (fire hydrant) was used to reproduce actual pressure change pattern in EPANET. The optimal combination of monitoring points for pressure detection was determined by selecting the nodes receiving maximum information from other nodes using genetic algorithm. The Ozger's and a real network were evaluated using the proposed model. From the results, it was found that the entropy theory can provide general guideline to select the locations of pressure sensors installation for optimal design and monitoring of the water distribution systems. During decision-making phase, optimal combination of monitoring points can be selected by comparing total amount of information at each point especially when there are some constraints of installation such as limitation of available budget.