• 한국전산구조공학회논문집
• /
• 제19권2호
• /
• pp.161-169
• /
• 2006

본 연구에서는 효과적인 인터넷 기반 구조해석 플랫폼을 개발하기 위하여 컴포넌트 모델을 제시하였다. 구조해석의 특성상 복잡한 알고리즘을 수행해야 하므로 다수 사용자에 대한 원활한 서비스를 위해 서버 연산 보다는 X-Internet을 이용한 클라이언트 연산을 실시하였다. 기존 상용 해석프로그램들의 사용자 편의적인 인터페이스에 부합되도록 Smart Client를 이용하여 윈도우 기반 인터페이스를 구축하였으며, 개발된 플랫폼의 재사용 및 확장성을 고려하여 컴포넌트 기반 프로그래밍을 함으로써 수정 및 변화에 능동적인 대처가 가능하게 하였다. 컴포넌트는 분할-단순화의 기법을 적용하여 전체 시스템을 표현하였고, 상위 컴포넌트와 하위 컴포넌트, 컴포넌트와 객체간의 관계에는 공통 인터페이스를 사용함으로써 라이브러리간의 연결을 명확히 구분하였다. 설계검토를 XML WebService를 사용하여 이기종 플렛폼과의 데이터 통신을 실시함으로써 차후의 통합 CAE에서의 데이터 교환의 기틀을 제시하였다. 2차원 트러스 구조물의 정적해석 및 설계검토를 수행하여 개발한 플랫폼의 효율성을 검증하였다.

• 정보교육학회논문지
• /
• 제16권2호
• /
• pp.245-253
• /
• 2012

본 연구의 목적은 초기청소년의 학습을 위한 인터넷의 사용, 게임사용, 그리고 지각된 학업성취도와의 관계를 종단적 자료로 살펴보고자 하였다. 이를 위하여 연구문제를 해결할 수 있는 잠재성장모형을 확장한 3개의 연구모형을 설정하였고, 연구모형별로 가설들을 검정하였다. 한국청소년패널(KYPS)에서 4년간 반복추적 조사한 초등학교 4학년 2,844명을 연구대상으로 하였다. 연구결과 (a) 학년이 증가함으로써 학습목적 인터넷 사용의 변화율은 유의하게 감소하는 경향을 나타내었지만, 개인차는 있는 것으로 나타났다. (b) 학습목적 인터넷 사용은 최종시점(중1)의 지각된 학업성취도에 영향을 주는 것으로 나타났다. 그리고 (c) 게임사용은 학습목적 인터넷 사용에 동시효과와 지연효과가 모두 유의한 것으로 나타났다.

• 응용통계연구
• /
• 제21권6호
• /
• pp.1037-1044
• /
• 2008

본 연구에서는 재무시계얼 자료의 변동성을 분석하는데 유용하게 쓰이는 멱변환 시계열 모형을 인터넷 트래픽 자료 특성 분석에 적용하여 효용성을 보이고자 한다. 트래픽의 특성인 장기기억(long memory)특성을 설명하기 위하여 멱변환 GARCH(PGARCH) 모형을 소개하고 기존의 GARCH 모형보다 더 유용함을 시뮬레이션과 실제 인터넷 트래픽 자료에 적합시켜 입증하였다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2003년도 춘계학술발표논문집 (상)
• /
• pp.211-214
• /
• 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.

• 정보보호학회논문지
• /
• 제16권3호
• /
• pp.165-172
• /
• 2006

누구나 인터넷에 접속할 수 있는 환경이 구축됨에 따라 해킹, 악성코드 등의 다양한 위협도 함께 등장하고 있다. 그 중 인터넷 웜은 1.25 대란과 같이 국가 기간망을 뒤흔들 수 있는 위협으로 인식되고 있다. 본 논문은 인터넷 환경에서 웜 확산의 모델링을 그 목표로 한다. 이를 위해 인터넷 원에 적용 가능한 확산 모델을 제안하고, 인터넷 환경에서 웜에 적용하여 동작을 분석한다. 제안 모델은 고속의 인터넷 웡 확산에 따른 영향을 분석함으로써 인터넷 웜의 확산을 보다 정확하게 예측할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권11호
• /
• pp.3099-3120
• /
• 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
• /
• 제4권4호
• /
• pp.27-38
• /
• 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.

• 한국정보통신학회논문지
• /
• 제16권11호
• /
• pp.2365-2373
• /
• 2012

본 논문에서는 UCLA IRL의 BGP 데이터, IRR, IXP 데이터를 이용하여 한국 AS 수준 인터넷 위상 데이터를 구축하였다. 그리고 지금까지 소개된 인터넷 위상 생성 모형(Waxman, BA and GLP)를 이용하여 한국 AS 수준 인터넷과 동일한 노드수를 가지는 위상 데이터를 생성한 뒤, 각각의 모형이 얼마나 한국 AS 수준 인터넷 위상을 잘 묘사하는지 분석하였다. 본 연구를 통해 기존의 인터넷 위상 생성기 모형들은 한국 AS 수준 인터넷을 잘 묘사하지 못하는 것을 확인하였다.

• 복식문화연구
• /
• 제14권5호
• /
• pp.850-863
• /
• 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)
• /
• 제7권3호
• /
• pp.558-575
• /
• 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.