• Spatial Information Research
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• v.16 no.1
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• pp.145-155
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• 2008

Case study has been criticized far its lack of generalization to the larger population and lack of sampling controls. Some of shortcomings of case study methods may be overcome, by using logically consistent, rigorous, and systematic approaches. Especially, Case study method has been suggested as appropriate for researching a range of GIS implementation, utilization, and diffusion issues. This paper provides examples of how the substance of its requirements may be met in the context of GIS case study. For the successful implementation of GIS, this study suggest that the case study method in GIS appropriate to the study of casual relations in and explication of complex GIS acquisition and adoption process.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.513-524
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• 2016

New education methodologies exploiting new media technologies are emerging with the development of digital technology. There also have been researches on efficient methods to apply new technologies on special education. In this paper, we analyze 32 researches associated with application of new media technology on special education for research theme, media, and teaching types. The analysis results show that there is few research on intervention contents which consider special education environment and the roll of teacher. This motivates us to develop a social storytelling contents to improve sociality of the children with developmental disability which can be easily useable in special education environment. The basic structure of the contents is designed for a child to make a choice in the meantime of storytelling and experience the different results depending on the choice. The pilot test with a child with mild autism verifies that it draws attention from the child and it is easy to use in a special education environment, which lays down the possibility as an intervention content.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.674-679
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• 2006

The public research and development (R&D) funds for construction engineering is one of the key elements for the advancement of construction industry. Korean Ministry of Construction and Transportation is currently planning to produce approximately US$ 500 millions of research funds every year in Korea from year 2007. Along with the prospect for increasing R&D investment, there is of increasing pressures on the development of quantitative methodologies for assessing the performance of construction R&D results. Although there have been some general R&D project evaluation methods, they are not readily used for construction R&D research assessment due to the distinct characteristics of construction R&D. This paper presents a simple methodology that can make quantitative assessments of construction R&D performances. The proposed methodology is expected to assist government officials in objectively evaluating construction R&D performances and identifying the areas that require more R&D investment.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1007-1023
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• 2016

This paper explores the conditions and potential of newly designed and tried methodology of big data analysis that apply to Korean history subject matter. In order to advance them, we need to pay more attention to quantitative analysis methodologies over pre-existing qualitative analysis. To obtain our new challenge, I propose 'digital history' methods along with associated disciplines such as linguistics and computer science, data science and statistics, and visualization techniques. As one example, I apply interdisciplinary convergence approaches to the principle and mechanism of elite reproduction during the Korean medieval age. I propose how to compensate for a lack of historical material by applying a semi-supervised learning method, how to create a database that utilizes text-mining techniques, how to analyze quantitative data with statistical methods, and how to indicate analytical outcomes with intuitive visualization.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.103-115
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• 2014

Mobile-Office is the IT office that enables people handle their business anywhere and anytime without going to head office. It has propagated rapidly in domestic and foreign companies as the users who use mobile terminal such as smartphone have increased sharply. Mobile-Office is emerging as a new way of conducting business. It requires business environment to be changed to improve business efficiency, as fast-growing mobile-based economies emerges. Small and medium-sized companies's utilization ability for advanced IT technology is insufficient, and limitations exist on capacity of building and investment. They need different development methodologies and utilization methods. The purpose of this study is not only to consider the previous business environment problem on accessibility, mobility, effectiveness, complexity and consolidation, but to search more efficient methods for introducing applications to utilize various smart devices and websites with minimum investment in R&D.

• Cartoon and Animation Studies
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• s.41
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• pp.223-252
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• 2015

Previous literature reviews have provided important insights into social game addictions, but the issue still needs to be examined from other directions such as the distribution of research purposes. For this purpose, 61 papers from selected journals were analyzed by a meta-analysis method. Specifically, this study poses the following three research questions: (1) What are the major of research purposes and methodologies? (2) What are the highly cited articles in studies of social game addictions? Results showed five new findings: (1) the research purpose of most social game addictions studies focuses on investigating the effect, followed by evaluating the influence of social game addictions. (2) Surveys and experimental methods were the preferred research methods, regardless of whether the research purpose focused on investigating or evaluation. (3) Social game addictions studies are most prevalent at the game users, higher education institutions, followed by schools. (4) Social game addictions studies most frequently supports researcher in the professions and applied sciences, followed by humanities, formal sciences and social sciences. (5) The most highly cited articles fall into the categories of investigating the effects and followed by evaluating the influence, designing a model and evaluating the effects of social game addictions. In this regard, this study of issues in social game addictions presents findings that can help supplement linkages with previous studies and forms an important reference base to pursue deeper academic discussions in the current research fields. These results and findings not only to supplement understanding of social game addictions based on different and important viewpoints, but also to provide useful insights for researchers and educators into issues related to social game addictions studies in future.

• Clinical and Experimental Pediatrics
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• v.48 no.10
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• pp.1068-1075
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• 2005

Purpose : The role of ghrelin, which promotes the secretion of growth hormone, was not well known until now. Recently it was found that the mutation of ghrelin gene is related to obesity and diabetes. This study is to find the screening method that can easily and effectively detect the polymorphism of Leu72Met in ghrelin gene of obesity patients and apply it to clinical usage. Methods : We compared PCR-RFLP, PCR-SSCP and ARMS methodologies for analyzing of the polymorphism of Leu72Met in ghrelin gene of obesity children, and also studied the merits and demerits of these methodologies. Results : In this study, we were able to find out the band of peculiar allele of Leu72Met in ghrelin gene using PCR-RFLP, PCR-SSCP and ARMS analyses. The polymorphism of Leu72Met in ghrelin gene determined by all above methodologies was in complete agreement. Compared to the PCR-RFLP and PCR-SSCP, ARMS analysis is simple, inexpensive and also consume less time. It is very sensitive to analyze the polymorphism and easy to understand the results of test. Conclusion : Though PCR-RFLP, PCR-SSCP and ARMS analyses were sensitive to analyze the polymorphism of Leu72Met in ghrelin gene, ARMS analysis appears to be more efficient than PCR-RFLP and PCR-SSCP. Therefore, we conclude that ARMS analysis is suitable to analyze the polymorphism of Leu72Met in ghrelin gene for large quantity of specimens.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.83-97
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• 2019

As society develops, the dissemination of microdata has increased to respond to diverse analytical needs of users. Analysis of microdata for policy making, academic purposes, etc. is highly desirable in terms of value creation. However, the provision of microdata, whose usefulness is guaranteed, has a risk of exposure of personal information. Several methods have been considered to ensure the protection of personal information while ensuring the usefulness of the data. One of these methods has been studied to generate and utilize synthetic data. This paper aims to understand the synthetic data by exploring methodologies and precautions related to synthetic data. To this end, we first explain muptiple imputation, Bayesian predictive model, and Bayesian bootstrap, which are basic foundations for synthetic data. And then, we link these concepts to the construction of fully/partially synthetic data. To understand the creation of synthetic data, we review a real longitudinal synthetic data example which is based on sequential regression multivariate imputation.

• Journal of Korea Technology Innovation Society
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• v.17 no.3
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• pp.519-539
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• 2014

In order to accomplish the successful performance management of the national research and development programs, it is necessary to establish the legal system that provide a institutional foundation and to set up and practice the relative substantial operating system. This paper aims to study the problems and improvement measures of the Performance Management System in National R&D Programs. For this purpose, the development of legal system on the performance management in national R&D programs was surveyed, the problems in existing laws and ordinances on the performance management were analyzed, and lastly the improvement measures on the legal system of the performance management were suggested. The legal system of performance management is being composed of and managed with The Framework Act On Science And Technology that prescribed the national science and technology policy direction, and Act On The Performance Evaluation And Management Of National Research And Development Projects, Etc. that prescribed the basic policies of performance management process and methods, and Regulations On The Management, Etc. Of The National Research And Development Projects that prescribed the concrete methodologies on performance management. Results of the study, it is necessary that the performance management laws and ordinances need improvement of Compatibility, the object and range of performance management system extend more widely to the whole field of national R&D programs in principle, and the performance management process and methods improve reasonably considering the goal and direction of national R&D policy.