• Journal of Engineering Education Research
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• v.24 no.1
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• pp.15-23
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• 2021

The first job of university graduates is the beginning of career development, and it has a great influence on a personal life in the transition process of a labor market later. The study has compared and analyzed the effects of major variables (whether or not one participates in the career and employment programs, the satisfaction in the education infrastructure and curriculum) related to university education that determine the satisfaction in the first job of graduates from the entire university of 4-year general courses and the engineering college with experiences of having the first job. Through this, it is meaningful to make it possible for the design of university education related to career and employment tailored to the engineering college. The results of 2017 Graduate Occupational Mobility Survey were used as the data for analysis, which was analyzed by the decision tree analysis. As it was found that the most important factor determining the satisfaction in the first job was student welfare facilities for the entire graduates among education infrastructure and was major curriculum and its content among education curriculum for the graduates from the engineering college, it was analyzed that factors related to majors were more important compared to other majors in the engineering college. The customized major curriculum and content should be considered as a priority, taking into account of the demand of the industry for the successful settlement of graduates from the engineering college in a labor market.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.649-656
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• 2015

The objective of this study was to analyze the optimal thinning regimes for timber or carbon managements in Cryptomeria japonica stands of Hannam Experimental Forest, Korea Forest Research Institute. In solving the problem, PATH algorithm, developed by Paderes and Brodie, was used as the decision-making tool and the individual-tree/distance-free stand growth simulator for the species, developed by Kwon et al., was used to predict the stand growth associated with density control by thinning regimes and mortality. The results of this study indicate that the timber management for maximum net present value (NPV) needs less number of but higher intensity thinnings than the carbon management for maximum carbon absorption does. In case of carbon management, the amount of carbon absorption is bigger than that of timber management by about 6% but NPV is reduced by about 3.2%. On the other hand, intensive forest managements with thinning regimes promotes net income and carbon absorption by about 60% compared with those of the do-nothing option.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.771-780
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• 2009

Cataract is the main cause of blindness and visual impairment, especially, age-related cataract accounts for about half of the 32 million cases of blindness worldwide. As the life expectancy and the expansion of the elderly population are increasing, the cases of cataract increase as well, which causes a serious economic and social problem throughout the country. However, the incidence of cataract can be reduced dramatically through early diagnosis and prevention. In this study, we developed a prediction model of cataracts for early diagnosis using hospital data of 3,237 subjects who received the screening test first and then later visited medical center for cataract check-ups cataract between 1994 and 2005. To develop the prediction model, we used random forests and compared the predictive performance of this model with other common discriminant models such as logistic regression, discriminant model, decision tree, naive Bayes, and two popular ensemble model, bagging and arcing. The accuracy of random forests was 67.16%, sensitivity was 72.28%, and main factors included in this model were age, diabetes, WBC, platelet, triglyceride, BMI and so on. The results showed that it could predict about 70% of cataract existence by screening test without any information from direct eye examination by ophthalmologist. We expect that our model may contribute to diagnose cataract and help preventing cataract in early stages.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Technology Innovation
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• v.23 no.3
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• pp.225-253
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• 2015

The standard is a nation's one of the most important industrial issues that improve the social and economic efficiency and also the basis of the industrial development and trade liberalization. This research analyzes the enactment and the utilization of Korean industrial standards(KS) of various industries. This paper examines Korean industries' KS utilization status based on the KS possession, enactments and inquiry records. First, we implement multidimensional scaling method to visualize and group the KS possession records and the nation's institutional issues. We develop several hypothesis to find the decision factors of how each group's KS possession status impacts on the standard enactment activities of similar industry sectors, and analyzes the data by implementing regression analysis. The results show that the capital intensity, R&D activities and sales revenues affect standardization activities. It suggests that the government should encourage companies with high capital intensity, sales revenues to lead the industry's standard activities, and link the policies with the industry's standard and patent related activities from R&D. Second, we analyze the impacts of each KS data's inquiry records, the year of enactments, the form and the industrial segment on the utilization status by implementing statistical analysis and decision tree method. The results show that the enactment year has significant impact on the KS utilization status and some KSs of specific form and industrial segment have high utilization records despite of short enactment history. Our study suggests that government should make policies to utilize the low-utilized KSs and also consider the utilization of standards during the enactment processes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.139-148
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• 2020

The most important factor affecting the berthing energy generated when a ship berths is the berthing velocity. Thus, an accident may occur if the berthing velocity is extremely high. Several ship features influence the determination of the berthing velocity. However, previous studies have mostly focused on the size of the vessel. Therefore, the aim of this study is to analyze various features that influence berthing velocity and determine their respective importance. The data used in the analysis was based on the berthing velocity of a ship on a jetty in Korea. Using the collected data, machine learning classification algorithms were compared and analyzed, such as decision tree, random forest, logistic regression, and perceptron. As an algorithm evaluation method, indexes according to the confusion matrix were used. Consequently, perceptron demonstrated the best performance, and the feature importance was in the following order: DWT, jetty number, and state. Hence, when berthing a ship, the berthing velocity should be determined in consideration of various features, such as the size of the ship, position of the jetty, and loading condition of the cargo.

• Korean journal of applied entomology
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• v.42 no.1
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• pp.29-34
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• 2003

Dispersion pattern for adult citrus red mite (CRM), Panonychus citri (McGregor) using by Taylor's power law (TPL) and Iwao's patchiness regression (IPR) was determined to develop a monitoring method on citrus orchards, on Jeju, in Autumn season, during 2001 and 2002.CRM population was sampled by collecting leaves and fruits. The relationships of CRM adults between leaf and fruit were analyzed by different season. The regression equation for CRM adults between leaf (X) and fruit (Y) was ln(Y+1) : 1.029 ln(X+1) ( $r^2$ : 0.80). The density of CRM was higher on fruit than on leaf according to fruit maturing level. TPL provided better description of mean-variance relation-ship for the dispersion indices compared to IPR. Slopes and intercepts of TPL from leaf and fruit samples did not differ between sample units and surveyed years. Fixed-precision levels (D) of a sequential sampling plan were developed using Taylor's power law parameters generated from adults of CRM in leaf sample. Sequential sampling plans for adults of CRM were developed for decision making CRM population level based on the different action threshold levels (2.0,2.5 and 3.0 mites per leaf) with 0.25 precision. The maximum number of trees and required number of trees sampled on fixed sample size plan on 2.0,2.5 and 3.0 thresholds with 0.25 precision level were 19, 16 and 15 and their critical values T$_{critical}$ at were 554,609 and 659, respectively. were 554,609 and 659, respectively.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.165-177
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• 2022

The purpose of this study is to present the direction of employment guidance for long-term service through the analysis of the cause of employment of employed students who graduated from specialized high school. In particular, the purpose is to present student guidance plans for long-term service by analyzing personal reasons for students graduating from commercial high schools and policy factors for individual, school, company, and government service after employment. To this end, a survey was conducted for graduates of commercial high schools nationwide, and the validity, reliability, and causality of the survey data were analyzed by applying Exploratory Factor Analysis, Cronbach's Alpha, and decision tree analysis techniques. We found that personal goal setting for employment is an important factor for working for more than 1 year, personal relationships at work and personal characteristics are important factors for working for more than 3 years. In addition, we found that the reason for getting a job is that personal reasons and school recommendations are great, special lectures on employment, camps, and 'advice from seniors and teachers' programs are helpful in finding a job, and accounting and computer related subjects are helpful for long-term employment. Accordingly, in specialized high schools, it is required to prepare specific instructional measures for education such as setting personal goals and the formation of human relationships that are the basis of social life, and to actively operate the above subjects and programs to help with employment and longevity.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.191-204
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• 2015

According to the '2013 Performance Assessment Report on the Financial Program' from the National Assembly Budget Office, the unfilled recruitment ratio of Software(SW) Developers in South Korea was 25% in the 2012 fiscal year. Moreover, the unfilled recruitment ratio of highly-qualified SW developers reaches almost 80%. This phenomenon is intensified in small and medium enterprises consisting of less than 300 employees. Young job-seekers in South Korea are increasingly avoiding becoming a SW developer and even the current SW developers want to change careers, which hinders the national development of IT industries. The Korean government has recently realized the problem and implemented policies to foster young SW developers. Due to this effort, it has become easier to find young SW developers at the beginning-level. However, it is still hard to recruit highly-qualified SW developers for many IT companies. This is because in order to become a SW developing expert, having a long term experiences are important. Thus, improving job continuity intentions of current SW developers is more important than fostering new SW developers. Therefore, this study surveyed the job continuity intentions of SW developers and analyzed the factors associated with them. As a method, we carried out a survey from September 2014 to October 2014, which was targeted on 130 SW developers who were working in IT industries in South Korea. We gathered the demographic information and characteristics of the respondents, work environments of a SW industry, and social positions for SW developers. Afterward, a regression analysis and a decision tree method were performed to analyze the data. These two methods are widely used data mining techniques, which have explanation ability and are mutually complementary. We first performed a linear regression method to find the important factors assaociated with a job continuity intension of SW developers. The result showed that an 'expected age' to work as a SW developer were the most significant factor associated with the job continuity intention. We supposed that the major cause of this phenomenon is the structural problem of IT industries in South Korea, which requires SW developers to change the work field from developing area to management as they are promoted. Also, a 'motivation' to become a SW developer and a 'personality (introverted tendency)' of a SW developer are highly importantly factors associated with the job continuity intention. Next, the decision tree method was performed to extract the characteristics of highly motivated developers and the low motivated ones. We used well-known C4.5 algorithm for decision tree analysis. The results showed that 'motivation', 'personality', and 'expected age' were also important factors influencing the job continuity intentions, which was similar to the results of the regression analysis. In addition to that, the 'ability to learn' new technology was a crucial factor for the decision rules of job continuity. In other words, a person with high ability to learn new technology tends to work as a SW developer for a longer period of time. The decision rule also showed that a 'social position' of SW developers and a 'prospect' of SW industry were minor factors influencing job continuity intensions. On the other hand, 'type of an employment (regular position/ non-regular position)' and 'type of company (ordering company/ service providing company)' did not affect the job continuity intension in both methods. In this research, we demonstrated the job continuity intentions of SW developers, who were actually working at IT companies in South Korea, and we analyzed the factors associated with them. These results can be used for human resource management in many IT companies when recruiting or fostering highly-qualified SW experts. It can also help to build SW developer fostering policy and to solve the problem of unfilled recruitment of SW Developers in South Korea.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.65-75
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• 2015

This paper analyzes the determinants of wage for the college and university graduates utilizing both individual-level and industry-level variables. We note that wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and industry-level (level-2) variables. Then, the assumption that individual wage is independent in the classical regression is violated. Therefore, this paper utilizes the hierarchical linear model (HLM). The major results are the followings. First, the multiple correspondence analysis including level-1 and 2 variables reveals that both level 1 and level 2 variables affects individual wages judging from the fact that the values of level 1 and level 2 variables differ across the different level of individual wage groups. Second, the decision tree analysis including level-1 and 2 variables shows that the most influential variable in wage determination is industry-level wage and the next is industry-level working hour, ages and sex in the decling order in. This suggests that the utilization of the HLM is appropriate since the characteristics of industry is important in determining the individual wage. Third, it is shown that the HLM model is the best compared to the other models which do not take level-1 and level-2 variables simultaneously into account.