• Journal of the Korean Vacuum Society
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• v.10 no.4
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• pp.403-410
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• 2001

Acceleration in integration density and speed performance of ULSI circuits require miniaturization of CMOS and interconnections as well as higher current density capabilities for transistors. A leading candidate to substitute Al-alloy is Cu, which has lower resistivity and higher melting point. So we can expect much higher electromigration resistance. In this paper, we are going to explain the major features of EM for MOCVD Cu according to variant conditions. We compared the life time and activation energy of MOCVD Cu with those of I-beam Cu and AA in the same conditions. The electromigration experiments were performed with Cu/Al/TiN multilayer. Experimental results shows that the deposition rate and electromigration characteristics of Cu thin film were improved by the Al interlayer.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.275-285
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• 2021

The purpose of this study is to empirically identify the process of technology acceptance of the metaverse, a virtual world-based platform that has attracted attention due to the 4th industrial revolution and the COVID-19 pandemic. The technology acceptance model (TAM) was used to identify factors affecting the use of the metaverse platforms and to analyze the causal relationship among these factors. For research, a survey was conducted on ordinary adult men and women and was analyzed using a structural equation model. The study found that perceived pleasure, interactivity, self-efficacy, and social influence had a positive effect on perceived ease-of-use. Interactivity and social influence had a statistically significant effect on perceived usefulness. The relationship between perceived ease-of-use and perceived usefulness was not statistically significant, but both perceived ease-of-use and perceived usefulness had a significant effect on positively forming attitudes toward metaverse. Lastly, favorable attitudes toward the metaverse platform had a positive effect on the intention to continue using it. Through this study, it was possible to identify the factors affecting the intention to use the metaverse and to confirm the causal relationship between the factors. A deeper understanding of users may be obtained in future if the research subject can be expanded and investigated with various factors.

• Journal of Global Scholars of Marketing Science
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• v.17 no.1
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• pp.101-122
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• 2007

Recently, as internet shopping mall users rapidly, a form of shopping changed from off line to on line. The rapid growth of customers and transaction volume through evolution of new media, internet, brings new problems to internet marketers. It is the most important task that how internet shopping mall operators obtain their customers trust and repeat buying. This empirical research investigates online shoppers for their trust dimensions for online retailers. The study aimed to determine whether e-trust antecedents(perceived reputation, perceived quality, perceived value) influence trust dimension and whether the multidimensional trust contributed differently to perceived risk and willingness to depend on e-retailers. Consequences of the research are as follows: First, it reveals that of reputation, web site quality of the internet shopping mall have influence upon trust dimension. Second, the higher level of trust consumers have, the higher level of willingness to depend and intent to revisit on the retailers they have. But level of perceive risk consumers have not influences on willingness to depend on the retailers. It is necessary for internet shopping mall to development its reputation and familiarity to obtain customer's trust. Accordingly, this research will be helping internet shopping mall insight for marketing strategies, constantly should study about action and mind of consumer.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.587-598
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• 2002

In multicast environment, the main objective of group key management is to provide security services to group communications by sharing a single group key among all the members of the group and subsequently encrypting and decrypting all the communication messages exchanged among the members of the group. Up to now, there has been no effort to develop group key management mechanism that considers the rate of users' join/leave operations. Hence, in this research, we propose group key management mechanisms that consider the rate of user's join/leave operations. We also define a new tree structure called variable tree which is much more flexible than full regular trees and show that variable trees are more efficient than full regular trees for group key management. Especially, we propose an algorithm that minimizes the necessary number of rekey messages according to the rate of join and leave operations. We also shows that if the rate of leave operation is greater than 50%, then the tree structure with degrees 2 or 3 are the optimal structures.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.19-36
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• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• The Journal of Korean Association of Computer Education
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• v.18 no.4
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• pp.19-30
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• 2015

This study investigated the structural relationships among performance expectancy, effort expectancy, social influence, facilitating condition, attitude, usage intention and actual use. In order to examine structural relationships among variables, we surveyed 285 high school students in spring semester of 2014. After collecting data, we examined causal relationships among variables using structural equation modeling. The results of this study were as follows: First, performance expectancy and effort expectancy significantly affected attitude; however, social influence did not. Second, attitude affected usage intention and usage intention significantly affected actual use. Third, the indirect effect of attitude between performance expectancy, effort expectancy and usage intention were significant and the indirect effect of usage intention between attitude and actual was also significant. Finally, the moderating effect of gender and experience were not significant. Based on this study, the practical strategies to facilitate usage intention and actual use of mobile learning of high school students were suggested.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.127-129
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• 2006

최근 대부분 기업들 환경에서의 정보 시스템들은 지역적으로 분산되어 있으며 다양한 형태로 구성되어 있으므로, 사용자 의사 결정을 지원하는데 필요한 통합된 정보를 얻는 것은 어려운 일이다. 따라서 이러한 문제를 효율적으로 정보 검색에 적용하기위해 사용자에게 단일 인터페이스를 제공하고, 이기종 시스템들 간에 구축된 데이터베이스 시스템들은 각각 독립성을 유지하면서 하나의 인터페이스처럼 투명성을 제공할 필요성이 있다. 이를 위해 ISO/IEC 11179에서 연구 중인 XMDR의 개념을 이용하여 정보검색에서 발생하는 "의미적 상호운용성(semantic interoperability)"이라는 문제점을 해결하고 이 XMDR에 지식 인스턴스 계층을 통한 지식공유를 가능하게 함으로써 단순 검색의 한계점을 극복할 수 있도록 한다. 본 논문에서는 데이터 표현에 사용되는 명칭, 속성, 관계성에 대한 이질적인 문제를 해결하기 위한 표준 온톨로지, 각 레거시 시스템을 연결하는 중간자(mediation)역할을 수행하는 로케이션 온톨로지, 지식공유가 가능하도록 하는 지식 인스턴스 계층으로 구성하는 방법을 제안한다. 또한 지식 인스턴스 계층은 협업적인 검색 환경하에서 각각의 정보시스템에서 다양한 형태의 지식을 공유 및 통합에 있어 구조화 되지 않은 지식들을 어떻게 공유할 것인가에 대한 개념적인 모델을 제시한다.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.125-132
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• 2005

Domestic construction firms make every effort to save cost and, contrarily, enhance quality for competitive advantage in the market. Structural work of building construction takes chaise of the total cost and schedule, thus elaborate planning and management of the work help to lead the project into a successful way. Therefore, the idea to save time and cost and enhance constructability securing quality and safety of the work should be developed after analyzing the designed documents and site conditions comprehensively in the initial construction planning phase. Value Engineering (VE) technique is introduced in the substructural work in this paper to save cost creatively and systematically in the design and construction phase. A practical VE model that is applied to the underground building work systematically is proposed to save cost and it applies to the actual project to confirm the effectiveness of the model.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.591-596
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• 2019

In this paper, we implemented a deep learning operation structure with less influence of local PC performance. In general, the deep learning model has a large amount of computation and is heavily influenced by the performance of the processing PC. In this paper, we implemented deep learning operation using AWS and streaming server to reduce this limitation. First, deep learning operations were performed on AWS so that deep learning operation would work even if the performance of the local PC decreased. However, with AWS, the output is less real-time relative to the input when computed. Second, we use streaming server to increase the real-time of deep learning model. If the streaming server is not used, the real-time performance is poor because the images must be processed one by one or by stacking the images. We used the YOLO v3 model as a deep learning model for performance comparison experiments, and compared the performance of local PCs with instances of AWS and GTX1080, a high-performance GPU. The simulation results show that the test time per image is 0.023444 seconds when using the p3 instance of AWS, which is similar to the test time per image of 0.027099 seconds on a local PC with the high-performance GPU GTX1080.