• Journal of the Korean Regional Science Association
• /
• v.34 no.2
• /
• pp.65-77
• /
• 2018

The purpose of this study is to compare and analyze factors in country risk between Cambodia and Vietnam. OECD and the Export-Import Ban of Korea assess country risk of Cambodia more highly than Vietnam. As results of the parametric tests for evaluation factors on the basis of country risk classification, the economic growth rate, the foreign trade index, and the foreign exchange reserves among the economic risks with the corruption index as the political and social risk have statistically significant effect on the difference between country risks of two countries. However, discriminant factor analysis indicates that the economic growth rate, the foreign exchange reserves, and the corruption index are key variables, which represent the difference between country risks of Cambodia and Vietnam. Consequently, the government of Cambodia needs to try to root out the corruption and to expand trade through increasing export for lowering the country risk to the level of Vietnam. Vietnam would also need to focus on attaining the sustainable high economic growth rate and increasing the foreign exchange reserves.

• Communications for Statistical Applications and Methods
• /
• v.16 no.1
• /
• pp.103-113
• /
• 2009

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. In this paper, we consider the efficiency and robustness of nonresponse weight adjustment bated on the response propensity and predictive mean. In the simulation study based on 2000 Fishry Census in Korea, the root mean squared errors for assessing the various ways of forming nonresponse adjustment cell s are investigated. The simulation result suggest that the most important feature of variables for inclusion in weighting adjustment is that they are predictive of survey outcomes. Though useful, prediction of the propensity to response is a secondary. Also the result suggest that adjustment cells based on joint classification by the response propensity and predictor of the outcomes is productive.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.7
• /
• pp.81-91
• /
• 2006

Recently, G.A studies have studied and demonstrated that artificial intelligence like G.A networks, G.A PI controller. The design techniques of PI controller using G.A with the newly proposed teaming algorithm was presented, and the designed controller with AC servo motor system. The goal of this paper is to design the AC servo motor using genetic algorithm and to control drive robot. And in this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for genetic algorithm PI controller. Our experimental results show that this approach increases overall classification accuracy rate significantly. Finally, we executed for the implementation of high performance speed control system. It is used a 16-bit DSP, IMS320LF2407, which is capable of the high speed and floating point calculation.

• Journal of Korea Game Society
• /
• v.17 no.6
• /
• pp.51-62
• /
• 2017

The augmented reality toy-game is a kind of new game genre that can be seen within the concept of augmented reality games, and it is a term to refer to the content or hardware that plays the game using the toy of the real world. This study aims to analyze the elements for the model of toy-game development based on the augmented reality. This study analyzed three characteristics of toy game which are different from other games based on existing related research. and have selected important factors to consider when developing augmented reality toy-game. A questionnaire was conducted to determine the suitability of the development elements derived, and the analysis and verification of the factors derived using an exploratory analysis method. As a result, it showed a reasonable outcome of the selection of variables, with the exception of some of the questions, and the classification results of the multi-dimensional scaling methods were also classified as reasonable in the clustering analyses.

• Physical Therapy Korea
• /
• v.27 no.3
• /
• pp.199-205
• /
• 2020

Background: The classification of foot type can be commonly determined by the height of the media longitudinal arch. The normalized arch height (NAH) is defined as the ratio of navicular or instep heights to the foot length or instep length. Objects: This study investigated the relationships among foot characteristics, such as foot length (FL), instep length (IL), navicular height (NH), and instep height (IH), in Korean young adults. Also, the distribution of foot type based on calculated NAH was assessed. Methods: Three-dimensional foot scanning data of young adults aged 20 to 39 years (total: 1,978; 974 male, 1,004 female) were obtained from the Korea Technology Standards Institute, and used for analyses. NAH was calculated as the following: NH/FL, IH/FL, IH/IL, NH/IL. Spearman's rank order correlation was used to identify correlations among variables. The Mann-Whitney U-test and chi-square test were used to compare the sex differences in foot characteristics and distribution of foot type. Results: FL and IL showed a very high correlation (r = 0.94). The correlations between FL or IL and IH (r = 0.50-0.57) were greater than those between FL or IL and NH (r = 0.23-0.72). Males had significantly larger values than females (p < 0.001), and the frequency of pes planus was significantly higher in females than in males (χ² = 50.09, p < 0.001). Based on the IH/IL index, the neutral foot, pes planus and pes cavus distributed by 16%, 78%, and 6% respectively. Conclusion: Our results on foot arch distribution could be used as basic data in clinical or footwear fields, and our data on differences in arch structure according to sex may facilitate understanding of why injury to the lower limbs differs between males and females.

• Journal of Digital Convergence
• /
• v.13 no.3
• /
• pp.155-165
• /
• 2015

Bankruptcy prediction has been one of the important research topics in finance since 1960s. In Korea, it has gotten attention from researchers since IMF crisis in 1998. This study aims at proposing a novel model for better bankruptcy prediction by converging three techniques - support vector machine(SVM), fuzzy theory, and genetic algorithm(GA). Our convergence model is basically based on SVM, a classification algorithm enables to predict accurately and to avoid overfitting. It also incorporates fuzzy theory to extend the dimensions of the input variables, and GA to optimize the controlling parameters and feature subset selection. To validate the usefulness of the proposed model, we applied it to H Bank's non-external auditing companies' data. We also experimented six comparative models to validate the superiority of the proposed model. As a result, our model was found to show the best prediction accuracy among the models. Our study is expected to contribute to the relevant literature and practitioners on bankruptcy prediction.

• Nuclear Engineering and Technology
• /
• v.47 no.1
• /
• pp.74-84
• /
• 2015

If a transient occurs in a nuclear power plant (NPP), operators will try to protect the NPP by estimating the kind of abnormality and mitigating it based on recommended procedures. Similarly, operators take actions based on severe accident management guidelines when there is the possibility of a severe accident occurrence in an NPP. In any such situation, information about the occurrence time of severe accident-related events can be very important to operators to set up severe accident management strategies. Therefore, support systems that can quickly provide this kind of information will be very useful when operators try to manage severe accidents. In this research, the occurrence times of several events that could happen during a severe accident were predicted using support vector machines with short time variations of plant status variables inputs. For the preliminary step, the break location and size of a loss of coolant accident (LOCA) were identified. Training and testing data sets were obtained using the MAAP5 code. The results show that the proposed algorithm can correctly classify the break location of the LOCA and can estimate the break size of the LOCA very accurately. In addition, the occurrence times of severe accident major events were predicted under various severe accident paths, with reasonable error. With these results, it is expected that it will be possible to apply the proposed algorithm to real NPPs because the algorithm uses only the early phase data after the reactor SCRAM, which can be obtained accurately for accident simulations.

• The Journal of Industrial Distribution & Business
• /
• v.10 no.7
• /
• pp.83-89
• /
• 2019

Purpose - It is a very important issue for the Korean tourism industry to increase tourism revenue by attracting foreign tourists. Although Japanese tourists have been an important part of the Korean tourism industry for a long time, the level of tourist satisfaction including accommodation has been at the worst compared to other foreign visitors, which strongly requires concrete solutions. Therefore, this study focuses on improving the satisfaction level of Japanese visitors in the use of accommodation, and find out the influence of the managerial response. Research design, data, and methodology - In this study, customer review and managerial response of hotels in Seoul were collected from "Rakuten Travel" which is the most representative online travel agency in Japan. As a result of collecting data from 2016 to 2018, 6,190 customer reviews and 1,241 managerial responses from 120 hotels were used for analysis. In addition, information on the properties of 120 hotels, such as the number of rooms, classification, types of hotel facilities, types of room facilities, accessibility and prices, were collected. To test the hypotheses, moderated multiple regression analysis was conducted with SPSS 22.0. Results - It was found that only 25 sites, 20.8% of the total 120 sites, were implementing managerial response and average response rate was 66.42% among them. As a result of examining the main effects of the hotel attributes on the ratings, accessibility and price are confirmed as effective variables. We also found that the response rate has a significant moderate effect in both the accessibility and price. In other words, there was a significant difference in the influence of accessibility and price on the ratings depending on the response rate. Also, it was confirmed that the response rate is not a pure moderator variable but a quasi moderator variable. Overall, the evidences partially supported the hypothesis. Conclusion - It was possible to provide important suggestions to the hotel managers who were concerned about managing tourist satisfaction with accessibility problems. It was found that the accessibility problem could be overcome by increasing the response rate. It was also confirmed that high ratings can be more effectively achieved for high priced hotels by increasing the response rate.

• The Journal of Information Systems
• /
• v.29 no.1
• /
• pp.241-265
• /
• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• Journal of the Korean Society for Railway
• /
• v.20 no.4
• /
• pp.482-490
• /
• 2017

This study is a railway accident investigation statistic study with the purpose of prediction and classification of accident severity. Linear regression models have some difficulties in classifying accident severity, but a logistic regression model can be used to overcome the weaknesses of linear regression models. The logistic regression model is applied to escalator (E/S) accidents in all stations on 5~8 lines of the Seoul Metro, using data mining techniques such as logistic regression analysis. The forecasting variables of E/S accidents in urban railway stations are considered, such as passenger age, drinking, overall situation, behavior, and handrail grip. In the overall accuracy analysis, the logistic regression accuracy is explained 76.7%. According to the results of this analysis, it has been confirmed that the accuracy and the level of significance of the logistic regression analysis make it a useful data mining technique to establish an accident severity prediction model for urban railway casualty accidents.