• Journal of the Korean Orthopaedic Association
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• v.56 no.3
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• pp.234-244
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• 2021

Purpose: To date, studies of firearm and explosive injuries in the Korean military have been limited compared to its importance. To overcome this, this study examined the characteristics of musculoskeletal damages in soldiers who have suffered firearm and explosive injuries over the past four years. Materials and Methods: From January 2015 to July 2019, military forces who had suffered musculoskeletal injuries from firearms or explosive substances were included. The medical records and radiographs were reviewed retrospectively, and telephone surveys about Short Musculoskeletal Functional Assessment (SMFA) for this group were conducted. To compare the functional outcomes, statistical analysis was performed using a t-test for the types of weapons, and ANOVA for others. Results: Of the 61 patients treated for firearms and explosives injuries, 30 patients (49.2%) were included after undergoing orthopedic treatment due to musculoskeletal injury. The average age at injury was 26.4 years old (21-52 years old). The number of officers and soldiers was similar. Eleven were injured by gunshot and 19 by an explosive device. Sixteen were treated in the Armed Forces Capital Hospital and 10 at private hospitals. More than half of the 16 patients (53.3%) with a fracture had multiple fractures. The most common injury site was the hand (33.3%), followed by the lower leg (30.0%). There were 14 patients (46.7%) with Gustilo-Anderson classification 3B or higher who required a soft tissue reconstruction. Fifteen patients agreed to join the SMFA survey for the functional outcomes. Between officers and soldiers, officers had better scores in the Bother Index compared to soldiers (p=0.0045). Patients treated in the Armed Forces Capital Hospital had better scores in both the Dysfunction and Bother Index compared to private hospitals (p=0.0008, p=0.0149). Conclusion: This is the first study to analyze of weapons injuries in the Korean military. As a result of the study, the orthopedic burden was high in the treating patients with military weapon injuries. In addition, it is necessary to build a military trauma registry, including firearm and explosive injuries, for trauma treatment evaluation and development of military trauma system.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Research in Community and Public Health Nursing
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• v.14 no.2
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• pp.190-199
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• 2003

This study was conducted to determine the factors affecting job satisfaction in workers by using the Job Stress Model proposed by the National Institute for Occupational Safety and Health (NIOSH). Data were collected from December 1 to December 30, 1999. The subjects were 2,133 workers employed at 155 work sites, who were examined using NIOSH Job Stress questionnaire translated by the Korea Occupational Safety ${\pounds}|$ Health Academy and Occupational Safety ${\pounds}|$ Health Research Institute. SAS/PC program was used for statistical analysis using descriptive analysis. Pearson's correlation coefficient, ANOVA, and Stepwise multiple regression analysis. The results of this study were as follows. 1. According to general characteristics of the subjects, job satisfaction was high in those with less number of children. 2. By work condition, job satisfaction was higher in those who were working in a permanent job position, were working with regular time basis than with shift basis, were working in regular shift hours than in changing shift hours, were working for a short period, and were working less hours and overtime works per week. 3. In terms of physical work environment, job satisfaction was significantly related to 10 physical environmental factors. In other words, job satisfaction was high in workers who were working in an environment with no noise, bright light, temperature adjusted to an appropriate level during summer and winter, humidity adjusted to an appropriate level. well ventilation, clean air, no exposure to hazardous substance during work hour, overall pleasant work environment and not crowded work space. 4. By work-related factors, job satisfaction was high in those with less ambiguity about future job and role, high job control/autonomy, and less workload. On the other hand, job satisfaction was low in those with little utilization of competencies, and much role conflict at work and workload. 5. As for the relationships between job satisfaction and the non-work related factors, job satisfaction was high in workers who were volunteering at different organizations or active in religious activities for 5-10 hours per week. 6. In the relationships between job satisfaction and buffering factors, significantly positive correlations were found between job satisfaction and factors such as support by direct superior, support by peers, and support by spouse, friend and family. 7. There were nine factors that affected job satisfaction in the workers: age, number of children, work hours per week, noise, temperature at the work site during summer, uncomfortable physical environment, role ambiguity, role conflict, ambiguity in job future, work load, no utilization of competencies and social support from direct supervisor. These nine factors accounted for 26% of the total variance in the multiple regression analysis. In conclusion. the following are proposed based on the results of this study. 1. The most important physical environmental factors affecting job satisfaction in workers were noise, role ambiguity, and work load, suggesting a need to develop strategies or programs to manage these factors at work sites. 2. A support system that could promote job satisfaction is needed by emphasizing the roles of occupational health nurses who may be stationed at work sites and manage the factors that could generate job stress. 3. Job satisfaction is one of the three acute responses to stress proposed in NIOSH job stress model (job satisfaction. physical discomfort and industrial accidents). Therefore, further studies need to be conducted on the other two issues.

• Journal of Biosystems Engineering
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• v.4 no.2
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• pp.64-64
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• 1979

Low-temperature drying systems have been extensively used for drying cereal grain such as shelled corn and wheat. Since the 1973 energy crisis, many researches have been conducted to apply solar energy as supplemental heat to natural air drying systems. However, little research on rough rice drying has been done in this area, especially very little in Korea. In designing a solar drying system, quality loss, airflow requirements, temperature rise of drying air, fan power and energy requirements should be throughly studied. The factors affecting solar drying systems are airflow rate, initial moisture content, the amount of heat added to drying air, fan operation method and the weather conditions. The major objectives of this study were to analyze the effects of the performance factors and determine design parameters such as airflow requirements, optimum bed depth, optimum temperature rise of drying air, fan operation method and collector size. Three hourly observations based on the 4-year weather data in Chuncheon area were used to simulate rough rice drying. The results can be summarized as follows: 1. The results of the statistical analysis indicated that the experimental and predicted values of the temperature rise of the air passing through the collector agreed well.2. Equilibrium moisture content was affected a little by airflow rate, but affected mainly by the amount of heat added, to drying air. Equilibrium moisture content ranged from 12.2 to 13.2 percent wet basis for the continuous fan operation, from 10.4 to 11.7 percent wet basis for the intermittent fan operation respectively, in range of 1. 6 to 5. 9 degrees Centigrade average temperature rise of drying air.3. Average moisture content when top layer was dried to 15 percent wet basis ranged from 13.1 to 13.9 percent wet basis for the continuous fan operation, from 11.9 to 13.4 percent wet basis for the intermittent fan operation respectively, in the range of 1.6 to 5.9 degrees Centigrade average temperature rise of drying air and 18 to 24 percent wet basis initial moisture content. The results indicated that grain was overdried with the intermittent fan operation in any range of temperature rise of drying air. Therefore, the continuous fan operation is usually more effective than the intermittent fan operation considering the overdrying.4. For the continuous fan operation, the average temperature rise of drying air may be limited to 2.2 to 3. 3 degrees Centigrade considering safe storage moisture level of 13.5 to 14 perceut wet basis.5. Required drying time decrease ranged from 40 to 50 percent each time the airflow rate was doubled and from 3.9 to 4.3 percent approximately for each one degrees Centigrade in average temperature rise of drying air regardless of the fan operation methods. Therefore, the average temperature rise of drying air had a little effect on required drying time.6. Required drying time increase ranged from 18 to 30 percent approximately for each 2 percent increase in initial moisture content regardless of the fan operation methods, in the range of 18 to 24 percent moisture.7. The intermittent fan operation showed about 36 to 42 percent decrease in required drying time as compared with the continuous fan operation.8. Drymatter loss decrease ranged from 34 to 46 percent each time the airflow rate was doubled and from 2 to 3 percent approximately for each one degrees Centigrade in average temperature rise of drying air, regardless of the fan operation methods. Therefore, the average temperature rise of drying air had a little effect on drymatter loss. 9. Drymatter loss increase ranged from 50 to 78 percent approximately for each 2 percent increase in initial moisture content, in the range of 18 to 24 percent moisture. 10. The intermittent fan operation: showed about 40 to 50 percent increase in drymatter loss as compared with the continuous fan operation and the increasing rate was higher at high level of initial moisture and average temperature rise.11. Year-to-year weather conditions had a little effect on required drying time and drymatter loss.12. The equations for estimating time required to dry top layer to 16 and 1536 wet basis and drymatter loss were derived as functions of the performance factors. by the least square method.13. Minimum airflow rates based on 0.5 percent drymatter loss were estimated.Minimum airflow rates for the intermittent fan operation were approximately 1.5 to 1.8 times as much as compared with the continuous fan operation, but a few differences among year-to-year.14. Required fan horsepower and energy for the intermittent fan operation were3. 7 and 1. 5 times respectively as much as compared with the continuous fan operation.15. The continuous fan operation may be more effective than the intermittent fan operation considering overdrying, fan horsepower requirements, and energy use.16. A method for estimating the required collection area of flat-plate solar collector using average temperature rise and airflow rate was presented.

• 대한공업교육학회지
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• v.42 no.2
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• pp.67-88
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• 2017

The purpose of this research was educational needs of experts for revitalizing youth entrepreneurship education and creating effective conditions for such education. The subjects of the survey were chosen 100 teachers who had participated in entrepreneurship-related professional training for teachers were selected and surveyed. A total of 100 questionnaires were collected, of which 92 (92.00%) were used for the analysis. Eight were excluded as they were not properly answered. As for the used survey tool, a total of 8 areas and 30 items were derived from the review of the literature, and the validity of the contents was examined through expert meetings. The data were analyzed using the SPSS (ver. 20.0) statistical program. The analysis was conducted in terms of the required competency level, perceived competency level and educational needs. As for the used analytical methods, first, the averages of the required competency level and perceived competency level were calculated and the education needs were calculated using Borich's formula, and then the averages were compared through paired t-test. The results turned out to be statistically significant (p<.000). The details are as follows: As a result of the calculation of the educational needs the educational needs in all areas turned out to be very high with the average being 4.94 points, which indicates that the teachers strongly feel the need for educational strengthening in relation to entrepreneurship. These results show that all the educational conditions such as entrepreneurship-related curriculum, teacher professionalism, educational environment, educational support and the perception among school community members are insufficient in the current school settings. For the improvement of the current status, the education conditions in the following areas should be improved: the cooperation from school community members including principals, teacher support such as an exclusive responsibility teacher system, the development of an entrepreneurship curriculum, the securing of teacher professionalism through the implementation of the curriculum, teacher training support for the enhancement of their professionalism and the provision of educational environment and facilities. For enhancing the perception of parents and society regarding entrepreneurship, it is necessary to establish the precise concept of entrepreneurship and promote it based on such work.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.1
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• pp.78-86
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• 2009

Recently, there seems to besome problems in the urban visual landscape as a result of continuous economic growth and industrial development. At the same time, the public has begun to be aware of the importance of visual resources, and the necessity for visual landscape conservation and improvement. Therefore, the development of evaluative indicators for systematic visual landscape planning and design is urgent. The purpose ofthis study is to discover evaluative models and indicators for the diagnosis of urban visual landscapes. This study included the selection of 18 physical indicators(statistical data) by literature reviews, adoption of field and questionnaire surveys at 12 autonomous districts in Seoul and surrounding major mountain valleys and river streams(i.e. Mt. Nam and Han-River). The content of the questionnaire is scenic beauty. Moreover, the linear regression analysis between the scenic beauty mean scores and the physical indicator scores figure out the scenic beauty prediction model. As this study suggests, the most important indicators in urban visual landscapes are 'Greens', 'Park' and 'the number of apartment buildings(higher than 20 stories).' Based on the results, greens and parks should be priority elements to considerin urban landscape planning and design. Moreover, since the number of apartment buildings that are higher than 20 stories has a negative correlation with the scenic beauty score, it can be used as basic data for landscape planning. For the scenic beauty prediction models and evaluative indicators suggest a direction of urban management, each indicator becomes basic data for visual landscape planning and design. In following studies, if physical indicators and case studies are added, the scenic beauty prediction models and evaluative indicators could be more synthetic and systematic. Moreover, the development of physical indicators in three dimensions(3D)(i.e. results from visual district analysis, view surface analysis) could be expected to obtain more general and varied results.

• Journal of Korean Society of Forest Science
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• v.107 no.1
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• pp.96-107
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• 2018

This study analyzed the linkage between the Forestry sector Official Development Assistance (ODA) Project in South Korea and the Sustainable Development Goals (SDGs) of United Nations (UN), Suggested direction of ODA project focusing on the implementation of the SDGs. Forestry sector ODA project data in South Korea have collected from Economic Development Cooperation Fund (EDCF) statistical inquiry system developed by The Export-Import Bank of Korea. According to the analysis result, Forestry sector ODA project in South Korea have been actively implemented in the fields of forestry development, forestry policy and administration. In both fields, Korea Forest Service and Korea International Cooperation Agency (KOICA) carried out the most projects. The Forestry sector ODA project data in South Korea are classified technical development, capacity building, construction of infrastructure and afforestation based on their objectives and contents. SDGs emphasizes the importance of national implementation assessment and this study analyze linkage between ODA activity content in each classification item and 2016 Korea Forest Service Performance Management Plan indicator. Analyzed the 2016 Korea Forest Service Performance Management Plan indicator and SDGs target and SDGs indicator were identified. finally, SDGs goals were recognized. In conclusion, Forestry sector ODA project in South Korea are associated with the SDGs Goal 1 (No Poverty), Goal 2 (Zero Hunger), Goal 6 (Clean Water and Sanitation), Goal 13 (Climate Action), Goal 15 (Life on Land) and Goal 17 (Partnership for The Goals). Therefore, With the launch of the SDGs, This study analyzed the linkage among the Forestry sector ODA Project in South Korea, the 2016 Korea Forest Service Performance Management Plan and the SDGs. it presented the limitations of Forestry sector ODA Project in South Korea and made proposals for the implementation of the SDGs.

• Korean Journal of Social Welfare
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• v.57 no.3
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• pp.51-72
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• 2005

The purpose of this article is to empirically testify three hypotheses on the relation between married women's employment and the allocation of unpaid domestic work within households - i.e., adaptive partnership (AP), dual burden (DB) and lagged adaptation (LA) models. The AP hypothesis assumes that, when wives are employed, husbands spend more time doing housework in order to compensate for their wives' increased responsibility. The DB model, by contrast, indicates that, even if married women are employed, their burden on domestic work does not decrease. In this case, therefore, the dual burden of married women can be expected. Between these two opposite views, the third, alternative hypothesis has been suggested recently. The LA model argues that the behaviours of households are adaptive to the changing environments but over a period of many years and even across generations. The article has analysed the total work time as well as unpaid domestic work time to testify these three hypotheses, utilising 1999 Time Use Survey data of the National Statistical Office. The research results can be summarised as follows. First, married working women worked 100 minutes more than their male spouses. Second, the average domestic work time of married men, 23-25 minutes per day, was no more than 5-10% of that of women. Third, the effects of age and women's employment were not statistically significant in multiple regression models, which means that the DB hypothesis explains the situation of married working women in Korea. Based on these findings, the article suggested the expansion of the public social service system to mitigate the dual burden of married working women, the introduction of compensatory credit for caring work, and the directions of further empirical research using the time use survey data.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• The Journal of Korean Academy of Sensory Integration
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• v.16 no.2
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• pp.1-14
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• 2018

Objective : The present study investigated the effects of group play activities based on Ayres Sensory $Integration^{(R)}$ (ASI) on sensory processing ability, social skill ability and self-esteem of low-income children with attention deficit hyperactivity disorder (ADHD). Methods : A total of 20 low-income participants with children with ADHD were recruited and divided into an experimental group (n=10) and a control group (n=10). Sensory processing ability was measured via the Short Sensory Profile (SSP). Social skill ability was measured via the Social Skills Rating System (SSRS). To measure self-esteem, the Rosenberg's self- esteem scale was used. The experimental group received the $ASI^{(R)}$ group play activities for 50 minutes, twice per week for six weeks, while the control group did not receive an intervention. Level of significance of all statistical analyses was .05. Results : Social skill ability (F=4.443, p=.05), cooperation (F=5.328, p=.035) and self-esteem (F=5.358, p=.033) differed significantly between groups after the intervention. Conclusion : Our findings indicate that the group play activities based on $ASI^{(R)}$ are effective in improving social skill ability and self-esteem. This study provided a theoretical basis for the claim that sensory integration therapy should be applied in general elementary schools.