• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.104-110
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• 2020

In the 4th Industrial Revolution, the competitiveness of the software industry is important, and as a solution to fundamentally secure the competitiveness of the software industry, education classes should be provided to educate high quality software personnel in educational institutions. Despite this social situation, software-related classes in universities are largely composed of competitive or individual learning structures. Cooperative learning is a learning model that can complement the problems of competitive and individual learning. Cooperative learning is more effective in improving academic achievement than individual or competitive learning. In addition, most learners have the advantage of having a more desirable self-image by having a successful experience. In this paper, we apply a pair check model, which is a type of cooperative learning, in operating system classes. In addition, the class procedure and instruction plan are designed to apply the pair check model. We analyze the test results to analyze the performance of the cooperative learning model.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.191-202
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• 2012

A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.629-643
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• 2014

It is common to check the validity of an assumed model with the heavy use of diagnostics tools when conducting data analysis with regression techniques; however, outliers and influential data points often distort the regression output in undesired manner. Jang and Anderson-Cook (2013) proposed a graphical method called a firework plot for exploratory analysis that could visualize the trace of the impact of possible outlying and/or influential data points on individual regression coefficients and the overall residual sum of squares(SSE) measure. They developed 3-D plot as well as pair-wise plot for the appropriate measures of interest. In this paper, the approach was extended further to tell the strength of their approach; in addition, a more meaningful interpretation was possible by adding a measure not mentioned in their paper. This approach was applied to the mixture experiment because we felt that a detailed analysis of statistical measure sensitivity is required in a small experiment.

• Journal of the Ergonomics Society of Korea
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• v.28 no.1
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• pp.1-7
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• 2009

As products are getting more diverse and new products are entering the market faster, customers have trouble learning how to use them. User-oriented menu structures may solve this problem. In order to design user-oriented menu structures, spreading activation theory has been studied. The spreading activation test shows that the strong associative relationship between words has shorter response times. Based on the spreading activation test, asymmetric spreading activation was introduced and a hypothesis that in a well-designed menu structure, association between upper-low menu pairs is not affected by an activation direction was tested for this study. In this study the menu of a cellular phone (Model: SPH-w2900) was extracted, and underwent 1st spreading activation tests. Then, on each menu pair, response time differences (asymmetric transition) by accuracy and directions were analyzed to find out problems in labels and improve menu structures and vocabulary. Second spreading activation tests were conducted to check whether asymmetric transitions decreased. The results showed that response time differences (asymmetric transition) for activation directions were found to be dropped significantly. Asymmetric transitions in spreading activation presented in this study will be helpful to define user-oriented menu structures.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.69-77
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• 2017

S The need for an automated geo-positioning approach for near real-time results and to boost cost-effectiveness has become increasingly urgent. Following this trend, a new approach to automatically compensate for the bias of the rational function model (RFM) was proposed. The core idea of this approach is to remove the bias of RFM only using tie points, which are corrected by matching with the digital elevation model (DEM) without any additional ground control points (GCPs). However, there has to be a additional evaluation according to the quality of DEM because DEM is used as a core element in this approach. To address this issue, this paper compared the quality effects of DEM in the conduct of the this approach using the Shuttle Radar Topographic Mission (SRTM) DEM with the spatial resolution of 90m. and the National Geographic Information Institute (NGII) DEM with the spatial resolution of 5m. One KOMPSAT-2 stereo-pair image acquired at Busan, Korea was used as experimental data. The accuracy was compared to 29 check points acquired by GPS surveying. After bias-compensation using the two DEMs, the Root Mean Square (RMS) errors were less than 6 m in all coordinate components. When SRTM DEM was used, the RMSE vector was about 11.2m. On the other hand, when NGII DEM was used, the RMSE vector was about 7.8 m. The experimental results showed that automated geo-positioning approach can be accomplished more effectively by using NGII DEM with higher resolution than SRTM DEM.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.598-604
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• 2014

In this paper, we develop stepwise regression data envelopment model to select important variables. We formulate null hypothesis to understand the importance of each variable and use Kruskal-Wallis test for this purpose. If the Kruskal-Wallis test does reject the null hypothesis this will imply there is significant fluctuation in the efficiency score relative to base model. And therefore we have to further check the pair of variables that causes the fluctuation in order to determine its importance using Conover-Inman test. The proposed models helps understand the extent of misclassification decision making units as efficient/inefficient when variables are retained or discarded alongside provides useful managerial prescription to make improvement strategies.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.391-398
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• 2001

To design and develop secure operating systems, the BLP (Bell-La Padula) model that represents the MLP (Multi-Level Policy) has been widely adopted. However, user\`s security level in the most developed systems based on the BLP model is inherited to a process that is actual subject on behalf of the user, regardless whatever the process behavior is. So, there could be information disclosure threat or modification threat by malicious or unreliable processes even though the user is authorized in the system. These problems can be solved by defining the subject as (user, process) ordered pair and by defining the process reliability. Moreover, when the leveled programs which exist as objects in a disk are executed by a process and have different level from the process level, the security level decision problem occurs. This paper presents an extended BLP (E-BLP) model in which process reliability is considered and solves the security level decision problem. And this model is implemented into the Linux kernel 2.4.7.

• Fashion & Textile Research Journal
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• v.19 no.2
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• pp.230-239
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• 2017

In the clothes industry, there are lack of experts including technical designers who can analyze the fit of clothes. This study is to provide practical data available for fit analysis education by distinguishing the differences in standards and aspects of garment fit between experts and novices, through the eye-tracking technology to quantify the sense of fit. For this study, two groups were organized; one composed of 7 experts with over 15 year-experience including technical designers and patternmakers, and the other composed of 7 novices who are students majoring in clothing. Wearing the goggle type eye-tracker Tobii Pro Glasses 2, the participants in the experiments were required to conduct fit analyses for a pair of pants on a live model. After those experiments, they were required to check the items for fit analysis and assess the importance level of them on a questionnaire. The differences between the two groups in the ratios of total visit count and total visit duration by each AIO(Area of Interest) of clothes were analyzed through non-parametric statistical test. The results of eye tracking experiments showed that experts focused on center front and back line, crotch area, and side seam, while novice's fixation points were dispersed around the pants. The survey results showed that the experts put importance on the center line position and its verticality, front-back proportion of side seam line, and front-back proportion of waist line, 71.4~100% of whom checked them, while 14.3% of the novices checked them.

• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.143-153
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• 2005

The purpose of this research is to study face images according to the difference of facial shape and make-up. A variety of face images can be formulated by computer graphic simulation, combining numerously different facial shapes and make-up styles. In order to check out the diverse images by make-up styles, we applied five forms of eye brows, two types of eye shadows, and three lip shapes to the round-shaped face of a model. The question sheet, used with a operational stimulant in the experiment, contained 28 articles, composed of a pair of bi-ended adjective in 7 point scale. Data were analyzed using Varimax perpendicular rotation method, Duncan's Multiple Range Test, and Three-way ANOVA. After comparing various results of make-up application to various face types, we could find that facial shape, eye-brows, eye-shadow, and lip shapes influence interactively on total facial images. As a result of make-up image perception analyses, a factor structure was divided into mildness, modernness, elegance, and sociableness. Speaking of make-up image in terms of those factors, round form make-up style showed the highest level of mildness. Upward and straight style of make-up had the highest of modernness. Elegance level went highest when eye shadow style was round form and lip style was straight. Lastly, an incurve lip make-up style showed the highest of sociableness.