• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.1
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• pp.212-217
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• 2004

To make the germinated buckwheat tea, soluble solid contents, total flavonoid contents and organoleptic properties were investigated under various steaming time and drying temperature. The optimum condition of soluble solid contents were 6.93 min of steaming time and 73.59$^{\circ}C$ of drying temperature. Total flavonoid contents were maximum under the condition of 5.22 min of steaming time and 79.05$^{\circ}C$ of drying temperature. The optimum condition of overall palatability was 6.00 min of steaming time and 77.33$^{\circ}C$ of drying temperature. The optimum ranges of soluble solid contents, total flavonoid contents and overall palatability of the tea were 5.4∼7.0 min of steaming time and 75∼8$0^{\circ}C$ of drying temperature. The values expected in the optimum ranges were also similar to the experimental values.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1137-1144
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• 2012

Many universities stress gradually the importance of english-medium class in order to improve the international competitiveness and the internationalization of the university. In this paper, we compare english-medium class with korean class using course evaluation score. Also we analyze the factors that affect the effectiveness of the course evaluation score of english-medium class. First, logistic regression analysis is used to examine the main effects of subjects and individual characteristics. Also, decision tree analysis is used to examine the interaction effects for subjects and individual characteristics. The results of this paper are as follows. Grade, department category, class size, GPA and screening method affect the effectiveness of english-medium class. The highest effectiveness group of english-medium class is that grade is freshmen and department category is humanity. Also the group of the second highest effectiveness group is that grade is freshmen and department category is nature and art and GPA is high.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1149-1157
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• 2013

In this study we compare academic achievements of college students admitted by admissions officer selection with those admitted by general selection. Two measurements of the academic achievements considered are GPA (grade point average) and relative ascending rank of GPA. By the comparison of the academic achievements, we would like to assess the effectiveness of the admissions office selection and then provide a basis for screening good students by that selection. The results of data analysis indicate that the academic achievements of admissions officer selection students tend to be lower than those of early general admission students and also those of regular general admission students tend to be higher than those of early general admission students.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1285-1296
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• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.292-297
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• 2014

As a Social Network Service (SNS) has become an integral part of our everyday lives, millions of users can express their opinion and share information regardless of time and place. Hence sentiment analysis using micro-blogs has been studied in various field to know people's opinion on particular topics. Most of previous researches on movie reviews consider only positive and negative sentiment and use it to predict movie rating. As people feel not only positive and negative but also various emotion, the sentiment that people feel while watching a movie need to be classified in more detail to extract more information than personal preference. We measure sentiment distributions of each movie from tweets according to the Thayer's model. Then, we find similar movies by calculating similarity between each sentiment distributions. Through the experiments, we verify that our method using micro-blogs performs better than using only genre information of movies.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.441-452
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• 2013

This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.407-413
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• 2013

For Ubiquitous service, there are some method researched. To IT convergence study tourism the convergence of IT and tourism in recent years has emerged as a discipline in the future. Tourist information is information about tourism products as tourists tourism decision-making needed to say. Information presented information anytime, anywhere, using a contact-type media, mobile and efficient tourist information content and generate content using Smart App store to the database is needed. This paper, by taking advantage of the Smart App Places to generate content and Smart Things to query, modify, search, tourism information, tourism policy and tourists can be analyzed, and the average inclination and these efficient tourism information content and that can be utilizedmodels are proposed. This u-Smart is a tourist information system. Build the biggest advantages of the meta-meta-model in real time by utilizing Smart App disposition of existing tourism information and tourist and tourism rating database. Helps to generate patterned by digital tourism policy tourism information content.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.199-208
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• 2017

This study investigates the components and rating system of reliable technology credit information for a technology finance donor who is a consumer of the information and aims to create an effective and optimal technology credit appraisal system to enlarge technology finance supply. Firstly, we calculate the optimal TCAR which becomes the maximum AUROC through the combination of ratio change, verify the substitution possibility between TAR and CR through the existing CR and system gap simulation, and propose a rating system by which financial institutes can utilize the TCAR as a credit rating. As a result, 70% : 30% is the most suitable as the weighted combination ratio of credit rating : technology rating. As a result of this study, we confirmed the possibility that the technical credit rating information could be substituted by the credit rating or the technology appraisal rating. Furthermore, it also suggests that sophisticated risk management is possible through using technology credit rating that are combined with credit and technology appraisal rating.

• Journal of Korean Society of Forest Science
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• v.88 no.3
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• pp.309-319
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• 1999

This study was conducted to get forest scenic beauty management information toward forest working systems; characteristics in Larix forest stands. 35 different alternatives were simulated to visualize on basis of actual forest working methods. The options were illustrated as photos produced by computer software. Each alternatives were evaluated by 3 groups with total 593 respondents after reliability test. Visual preference evaluation was used 1 - 10 point rating scale. The ratings were scaled using SBE analysis program of RMRATE. To compare the relationship of visual preference and image scale, Spatial images of 13 thinning alternatives were measured by Semantic differential scale. In general, the respondents preferred refreshing and ordering forest stand after forest working to natural forest stand before forest working. Visual preference decreased with the increasing intensity of bare ground area and slash area in forest stand. And also, visual preference was high related to tree density, clear length of stem, and ground vegetation. Farm line of small clear cutting area was preferred straight line to curve line. Visual preferences were significant differences in certain socioeconomic variables of the respondents. Results indicate how to conduct forest working system for forest scenic beauty management.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1491-1498
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• 2017

As the use of smartphones and the launch of various apps have increased rapidly, the number of malicious apps has also increased, and the damage is continuing. The Google Market where Android apps are registered is inevitably present at the same time as normal apps and malicious apps even though there are regulations for app registration. Especially, as social networks are activated, users are connected with social networks, and the ratings, downloads and awareness information are reflected in the number of downloaded apps. As a result, when users choose their apps by simply reflecting ratings, popularity, popular comments, and highly-categorized apps, malicious app downloads can sometimes cause significant harm. Therefore, this study first analyzed the tendency of malicious apps by directly crawling and analyzing long-term social information in the currently active Android market.