• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.161-169
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• 2006

The study proposes component models in developing an efficient platform for internet-based structural analysis. Since a structural analysis requires an operation of complicated algorithms, a client-side computation using X-Internet is preferred to a server-side computation to provide a flexible service for multi-users. To compete with the user-friendly interfaces of available commercial analysis programs, a window-based interface using Smart Client was applied. Also, component-based programming was performed with the considerations on reusability and expandability so that active Preparation for future change or modification could be feasible. The components describe the whole system by subdivision and simplification. In the relationship between upper-and lower-level components and also in the relationship between components and objects, a unified interface was used to clearly classify the connection between the libraries. By performing data communication between different types of platforms using XML WebService, a conner-stone of data transfer is proposed for the future integrated CAE. The efficiency of the developed platform has been examined through a sample structural analysis and design on planar truss structures.

• Journal of The Korean Association of Information Education
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• v.16 no.2
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• pp.245-253
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• 2012

The purpose of this study is to find out the structural relations among the changing of internet use for learning, online game use, and perceived achievement. To complete this study, we set three research models and verified our hypotheses from the research models. We used Korean Youth Panel Study (KYPS) data, which surveyed beginning with fourth grade 2,844 elementary school students. We discovered that (a) there was a statically significant individual variability in initial levels and rates of change in internet use for learning. The change of trajectory was declined. (b) We also found out both initial state and changing rate of internet use for learning positively affect perceived academic achievement. (c) Lastly our study found both the concurrent and lag effects support the developmental relation between internet use for learning and game use in young adolescents.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1037-1044
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• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.211-214
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• 2003

Since the number of mobile Internet users has been increasing rapidly around the world, the mobile business which is a variety of applications of mobile Internet has gained attention among the related industry and academics. However, most researchers mainly focus on the issues concerning the trend, forecast, technoloies, and demographic characteristics of mobile Internet services. Further, only mobile Internet users have participated in surveys, excluding network operators and contents providers. The purpose of this research is to project the evolution of mobile business and identify its critical success factors. The results of this research are from the analysis of data collected not only from mobile Internet users but also from network operators and contents providers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.3
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• pp.165-172
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• 2006

There are various threats as side effects against the growth of information technology, and malicious codes such as Internet worms may bring about confusions to upset a national backbone network. In this paper, we examine the existed spreading models and propose a new worm spreading model on Internet environment. We also predict and analyze the spreading effects of high-speed Internet worms. The proposed model leads to a better prediction of the worm spreading since various factors are considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Asian Journal of Business Environment
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• v.4 no.4
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• pp.27-38
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• 2014

Purpose - The study aims to investigate empirically the effects of the flow of an Internet shopping mall upon consumers' revisit intention and purchase intention. Research design, data, and methodology - The subjects comprised customers of Internet shopping malls. SPSS 19.0 for Windows was used to verify the models and hypotheses. Frequency, factors, reliability, and regression analysis were used. Results - This study classified flow behavior factors of Internet shopping malls into four categories-skills, convenience, design, and mutual reaction-to investigate their influence on flow. Skills and convenience had a greater influence than mutual reaction and design. The flow was most influenced by convenience, followed by skills. Conclusions - First, the subjects comprised those who had made purchases at least once at an Internet shopping mall. Second, the study applied the common flow attributes of past researchers to the Internet shopping mall environment, to gauge customers' e-commerce involvement. Third, skill, convenience, and shopping mall display design affirmatively influenced the computer-mediated environment from the Internet marketing control implications perspective regarding the contents of the marketer's website.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2365-2373
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• 2012

To obtain Korea AS-level internet topology, we used three data sources, which include BGP data of UCLA IRL, IRR and IXP data. Using Internet topology generator models(Waxman, BA and GLP), we developed three graphs that have same number of nodes as Korea AS-level Internet. Then we compared each graph with the Korea AS-level Internet topology. Through this study we could find that the existing Internet topology generators can't simulate Korea AS-level internet.

• The Research Journal of the Costume Culture
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• v.14 no.5
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• pp.850-863
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• 2006

The purpose of this study was to investigate if star marketing of on-line shopping malls affects consumers' WOM effect, and to compare the differences of consumption behavior between female teenagers and college students. Two hundred five female teenagers and college students who had purchased fashion goods through internet shopping mall participated in this study. For data analysis, descriptive statistics, factor analysis, t-test, and multiple regression were used. As the results, first, recognition of celebrity advertising models was classified into three factors such as 'trust of product', 'attractiveness of product' and 'leading interest of product' factors. Second, the greater exposure to celebrity models, the greater the good feelings about them, showing respondents' positive consumption behavior. Third, results of multiple regression revealed that behavior of pursuing celebrities' style accounted for 37% of the explained variance WOM behavior. Finally, t-test revealed that female college students were affected more by celebrity style and bought fashion items than female teenagers. However, female teenagers conducted more WOM behavior than college students. Based on these results, on-line fashion marketers would use these data for more their efficient fashion marketing strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.558-575
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• 2013

The purpose of a data leakage prevention system is to protect corporate information assets. The system monitors the packet exchanges between internal systems and the Internet, filters packets according to the data security policy defined by each company, or discretionarily deletes important data included in packets in order to prevent leakage of corporate information. However, the problem arises that the system may monitor employees' personal information, thus allowing their privacy to be violated. Therefore, it is necessary to find not only a solution for detecting leakage of significant information, but also a way to minimize the leakage of internal users' personal information. In this paper, we propose two models for representing the level of personal information disclosure during data leakage detection. One model measures only the disclosure frequencies of keywords that are defined as personal data. These frequencies are used to indicate the privacy violation level. The other model represents the context of privacy violation using a private data matrix. Each row of the matrix represents the disclosure counts for personal data keywords in a given time period, and each column represents the disclosure count of a certain keyword during the entire observation interval. Using the suggested matrix model, we can represent an abstracted context of the privacy violation situation. Experiments on the privacy violation situation to demonstrate the usability of the suggested models are also presented.