• 인터넷정보학회논문지
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• 제17권5호
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• pp.131-139
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• 2016

This paper presents a workflow validation method for data-intensive graphical workflow models using real-time workflow tracing mode on data-intensive workflow designer. In order to model and validate workflows, we try to divide as modes have editable mode and tracing mode on data-intensive workflow designer. We could design data-intensive workflow using drag and drop in editable-mode, otherwise we could not design but view and trace workflow model in tracing mode. We would like to focus on tracing-mode for workflow validation, and describe how to use workflow tracing on data-intensive workflow model designer. Especially, it is support data centered operation about control logics and exchange variables on workflow runtime for workflow tracing.

• 콘크리트학회논문집
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• 제13권1호
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• pp.77-85
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• 2001

CFT 기둥은 뛰어난 구조적 성능으로 사용이 증가하고 있는 추세이다. 하지만 CFT 기둥을 사용하여 건설된 구조물도 내부콘크리트의 시간의존적 변형으로 사용상태에 이상 응력집중이나 과다 처짐 등의 구조적 문제를 초래할 수 있다. 따라서 이에 대한 연구가 필요하다. 본 논문에서는 CFT 기둥에 하중이 가해지는 시점에서의 해석법과 이를 기초로 한 장기거동 해석법을 제안하였다. 또한 이에 대한 실험을 실시하여 해석결과와 비교.분석하였다. 이를 통해 제안된 해석법을 검증하고 아울러 해석에 적용된 부착강도 모델식의 정확성 또한 평가하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.453-466
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• 2006

The increasing popularity of the Internet has led to the emergence of online service delivered by websites. Given large investments in these websites, it is important to retain existing customers in online service contexts. In order to understand how website information and system attributes may influence perceived usefulness of using the websites, customer satisfaction, and ultimate continuous usage behaviors, we develop a model to integrate user satisfaction with technology acceptance. Furthermore, this integration is sharpened through the synthesis with research on continuous usage of online service based on customer satisfaction. We then test the model using a sample of 236 respondents who have used one of the most popular Internet blog and community service in South Korea within the last three months. The analysis results suggest that website information and system attributes play key roles in forming continuous intention of the service and perceived playfulness serves as an important moderator toward customer satisfaction. Our model is more likely to help link website design and invesment decisions to the strategy for retaining existing customers in the context of online service.

• 한국게임학회 논문지
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• 제17권6호
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• pp.29-38
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• 2017

게임을 개발하는 단계에서 게임에 대한 이용자의 반응을 예측하는 것은 게임성 평가에 있어 중요하다. 본 논문에서는 게임 이용자의 반응을 알아보고자 게임 이용자의 생체 정보를 비침투적인 방법으로 수집한 뒤, 분석할 수 있는 시스템을 제안한다. 이를 위해 아두이노를 활용하여 피부 전도도, 압력, 자이로스코프, 가속도계 센서를 내장한 마우스와 분석 시스템을 새로이 개발하였다. 이 시스템의 유용성을 검증하기 위해 피험자가 이 마우스를 사용해 1인칭 슈팅 게임 '오버 워치'를 플레이하는 실험을 진행하였다. 실험 결과 본 시스템이 게임 플레이 영상과 마우스 내 여러 센서들로부터 수집한 생체 정보들을 활용하여 게임 이용자의 특징을 분석하는데 유용하게 활용될 수 있음을 확인하였다.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• 한국해양바이오학회지
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• 제14권2호
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• pp.69-75
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• 2022

The micro-Kjeldahl method, a common technique for analyzing crude proteins, is time-consuming and dangerous due to the employment of reagents such as sulfuric acid and sodium hydroxide. However, a Fourier transform near-infrared (FT-NIR) spectrophotometer analysis can be completed in under a minute after simple pre-processing if data has been gathered using sufficient reference material in advance. Furthermore, the use of safe reagents in this technique ensures the safety of the experimenter and the environment. In addition, a portable FT-NIR spectrophotometer enables real-time measurement at processing or distribution sites and has recently gained popularity. The standard errors of calibration and regression (r²) for the calibration result for estimating the crude protein content of dried laver were 0.9775 and 1.2526, respectively. The standard error of prediction was 1.1814, and the r² was 0.9303 in the validation results, which was a good level. In the present study, a method for predicting the crude protein content of dried laver using an FT-NIR spectrophotometer in the range of 29%-40% crude protein content has been reported.

• Asia pacific journal of information systems
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• 제25권2호
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• pp.211-238
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• 2015

Despite longstanding concern with the dimensionality of the service quality construct as measured by ServQual and IS-ServQual instruments, variations on the IS-ServQual instrument have been enduringly prominent in both academic research and practice in the field of IS. We explain the continuing popularity of the instrument based on the salience of the item set for predicting overall customer satisfaction, suggesting that the preoccupation with the dimensions has been a distraction. The implicit mutual exclusivity of the items suggests a more appropriate conceptualization of IS-ServQual as a formative index. This conceptualization resolves the paradox in IS-ServQual research, that of how an instrument with such well-known and well-documented weaknesses continue to be very influential and widely used by academics and practitioners. A formative conceptualization acknowledges and addresses the criticisms of IS-ServQual, while simultaneously explaining its enduring salience by focusing on the items rather than the "dimensions." By employing an opportunistic sample and adopting the most recent IS-ServQual instrument published in a leading IS journal (virtually, any valid IS-ServQual sample in combination with a previously tested instrument variant would suffice for study purposes), we demonstrate that when re-specified as both first-order and second-order formatives, IS-ServQual has good model quality metrics and high predictive power on customer satisfaction. We conclude that this formative specification has higher practical use and is more defensible theoretically.

• 지식경영연구
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• 제20권3호
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.

• 한국의류산업학회지
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• 제24권5호
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• pp.493-504
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• 2022

This study aims to gain a deeper understanding of the fashion styles of underground rappers in China and South Korea. Due to rappers' fashion influence on modern fashion trends, research on rapper fashion has been conducted steadily in the field of apparel. Qualitative research methodologies including literature research and in-depth interviews were the primary techniques used to solve the research questions. In-depth interviews were conducted with 10 Chinese underground rappers and 10 Korean underground rappers to reveal and explore their fashion style and view of fashion. As a result, the participants' fashion styles were categorized into four styles: authentic hip-hop fashion style, popularized hip-hop fashion style, easy sports casual style, and trendy street style. Rappers from both countries consider that their identity as rappers can be demonstrated through their hip-hop fashion style. The influence of hip-hop culture and the popularity of rap music differs between China and Korea. It affects not only the rapper's musical characteristics, but also their fashion style. While Korean underground rappers' fashions style is trendier, Chinese underground rappers' fashions style displays stronger characteristics of hip-hop fashion. Due to the public's negative view of rappers, some Korean underground rappers intentionally try to hide their identity by wearing a fashion style that differs from the authentic rapper image. Understanding the fashion styles of underground rappers in Korea and China is expected to assist in predicting future hip-hop culture and rapper fashion trends.