• 대한공업교육학회지
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• v.39 no.2
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• pp.101-121
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• 2014

The study aims to develop the NCS based vocational curriculum model for the practical and creative human resources. For effectiveness of the study, the study consists of literature studies of both domestic and international, contents analysis, case study, expert(9samples) consultation and review, and in-depth-interview of the three advisory members. The validity of the developed model is analyzed through mean, standard deviation and contents validity ratio(CVR). The main results of the model development in our study are as follow. First, our NCS based vocational curriculum model for the practical and creative human resources is developed with the analyses of NCS development manuals, training standard utilization and training curriculum organization manuals, NCS learning module development manual and case studies, NCS research report, NCS based curriculum pilot development resources directed toward the high schools and vocational school as well as the domestic and international literature study on career training model like NCS. Second, based on the findings of our analysis in combination with the findings from the consultations with the expert and advisory committee, total 19 sub-factors of each step and domain are extracted. The sub-factors of domain in step 1 are the competency unit, definition of competency unit, competency unit element, performance criteria, range of variable, guide of assessment, key competency; in step 2, they are subject title, subject objectives, chapter title, chapter objectives, pedagogical methods, assessment methods and basic job competence; and in step 2, they are NCS based subject matrix table, NCS based subject profile, NCS based job training curriculum table, NCS based subjects organization flowchart, NCS based job training operation plan. Third, the final model including step 3 NCS based subject profile are developed in association with the linked organizational sub-factors of step 1 and step 2. Forth, the validity tests for the final model by the step and domain yield the mean 4.67, CVR value 1.00, indicating the superior validity. Also, the means of each sub-factors are all over 4.33 with the CVR value 1.00, indicating the high validity as well. The means of the associated organizations within the model are also over 4.33 with the CVR value of 1.00. Standard deviations are all .50 or lower which are small. Fifth, based on the validity test results and the in-depth-interview of the expert and advisory committee, the model is adjusted complemented to establish final model of the NCS based vocational curriculum for the practical and creative human resources.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Life Science
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• v.17 no.3 s.83
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• pp.368-374
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• 2007

The purpose of this study was to investigate the effects of different type of triglycerides (MCT & LCT) on weight, survival time, energy substrate (FFA, TG, pyruvate, lactate), insulin and lipase in the trained rats. Fifty-four Sprague-Dawley rats were divided into 3 groups: control group (CG, n=18), MCT supplement group (MG, n=18), and LCT supplement group (LG, n=18). They also were divided into 3 periods: trained resting (R, n=6) and trained & exercise load (E, n=6), and survival time test was performed to know the supplemented effects. Body weight of all animals was checked every week, MCT group and LCT group received supplementary MCT and LCT orally and preliminary swimming training for 6 days before the start of main experiment. All animals received 15-minute swimming training 5 times during first week of experiment, and swimming training time was increased 15 minutes every week until it reached 90 minutes at last 9th week. After last swimming training, animals were fasted for 12 hours and blood samples were taken from abdominal aorta in the Department of Animal Medicine at the D university Animal Center. Among the CGE, MGE, and LGE groups, the MGE had the greatest increase in physical performance time. In the FFA levels, there was significant differences(p<.05) in CG, MG and LG groups, and also there was major difference of FFA levels in the MG and LG. In the lipase levels, there was signifi.ant differences (p<.05) in CG, MG and LG groups. MG was the greatest than the other groups. In the insulin hormone levels, there was the great differences (p<.05) in LG compare to CG groups, whereas there was no significant difference in the CG and MG. In conclusion, these results suggest that regular prolonged physical training with MCT supplementation, improves exercise performance time through the increase of energy substrate utilization, lipase activity and FFA levels, irrespective of insulin hormone responses.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.942-953
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• 2019

The objective of this study was to evaluate the effects of L-arginine supplementation on muscle damage and fatigue indices and athletic performance improvement of canoe athletes after conducting a high-intensity training program. To achieve the objective, this study applied a high-intensity training program to seven high school canoe athletes. The high-intensity training program is composed of aerobic exercise sessions (twice per week; Tuesday and Thursday), anaerobic exercise sessions (three times per week; Monday, Wednesday, and Friday), and flexibility exercise sessions (five times per week). During the 6 week high-intensity training program, drug ingestion (L-arginine or placebo) was conducted in the first two weeks, wash out (two weeks) followed it, and drug ingestion (L-arginine or placebo) was carried out again in the last two weeks. The crossover design was used for the experiment so all study subjects were assigned to either the L-arginine intake group (the treatment group) or the placebo group (the control group). Each subject ingested 3g per day. This study confirmed the significant effects of L-arginine supplementation on muscle damage indices, fatigue indices, and antioxidants using blood samples. Additionally, FMD was analyzed to evaluate vascular endothelial cell functions and canoe performance was examined using the canoe ergometer. The results of this study showed that L-arginine intake did not have direct effects on the levels of ammonia, IP, and CK. The level of LDH decreased significantly more in the ARG group than in the PLA group due to L-arginine supplementation. Moreover, L-arginine supplementation did not change total NO, d-ROMs, BAP, and FMD significantly. Lastly, the results of the 500m canoe ergometer, which was conducted to evaluate the canoe performance, revealed that L-arginine did not have direct effects on total time, stroke distance, and mean velocity. However, L-arginine supplementation significantly improved muscle damage indices, fatigue indices, antioxidants, FMD, and canoe performance. Therefore, it is believed that additional studies are needed for examining the potential effects of L-arginine supplementation athletic performance enhancement.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• The Korean Journal of Emergency Medical Services
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• v.11 no.3
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• pp.175-189
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• 2007

Background and Purpose : Higher grade students in elementary schools are most adequate subjects for first aid training. The purpose of this study was to assess first aid education and needs of higher grade students in elementary schools. Method : The subjects of this study were 183 higher grade students from 8 elementary schools. Data were collected by the questionnaire during the period from March 19 to April 13, 2007. The data were analyzed through frequency, Cronbach's ${\alpha}$, Independent Two samples t-test, One Way ANOVA by SPSS win 12.0. Result : 1. It showed that 78.1%(143 persons) of sujects answered that they learned first aid. 65% of sujects learned in the school(65%). 61.2% of sujects were taught by health teachers. 36.7%(67 person) of subjects was educated using practice with demonstration including lecture. Learned contents were action at emergency(50.8%), CPR(36.6%), splint (33.9%). 2. It showed that 90.2%(165 persons) of subjects answered that first aid and CPR education are necessary. Also 74.9%(137 persons) of subjects answered that will be educated first aid and CPR if opportunities is given. The 53.3%(73 persons) of subjects wanted teaching method using practice with demonstration including lecture. 3. The total mean showed $2.29{\pm}.48$ in needs of first aid education by 3 points Likert scale. Needs of first aid education was ranked Heimlich maneuver($2.41{\pm}.65$), splint and bandaging($2.38{\pm}.59$). Priority of intensive training showed patient assessment(38.0%) and CPR(19.7%) in first, splint and bandging(22.6%), CPR(21.9%) and Heimlich maneuver(21.9%) in second. 4. The needs assessment of first aid education showed statistically significant differences according to teaching method(F = 2.563, p = .025), education necessity yes or no(F = 2.474, p = .015), attending future education yes or no(F = 2.253, p = .026). Conclusion : These results suggest that First aid education for higher grade students in elementary schools must be consisted of most adequate content and method based on current education condition and needs assessment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.161-166
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• 2018

In this paper, we propose a method for the random selection of pooling operations for the regularization and reduction of cross validation in convolutional neural networks. The pooling operation in convolutional neural networks is used to reduce the size of the feature map and for its shift invariant properties. In the existing pooling method, one pooling operation is applied in each pooling layer. Because this method fixes the convolution network, the network suffers from overfitting, which means that it excessively fits the models to the training samples. In addition, to find the best combination of pooling operations to maximize the performance, cross validation must be performed. To solve these problems, we introduce the probability concept into the pooling layers. The proposed method does not select one pooling operation in each pooling layer. Instead, we randomly select one pooling operation among multiple pooling operations in each pooling region during training, and for testing purposes, we use probabilistic weighting to produce the expected output. The proposed method can be seen as a technique in which many networks are approximately averaged using a different pooling operation in each pooling region. Therefore, this method avoids the overfitting problem, as well as reducing the amount of cross validation. The experimental results show that the proposed method can achieve better generalization performance and reduce the need for cross validation.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.4
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• pp.131-142
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• 2017

This study aims to create a Local Climate Zone(LCZ) map of Seoul by using World Urban Database and Access Portal Tools(WUDAPT) protocol, and to analyze the characteristics of the Seoul LCZs. For this purpose, training samples of 17 LCZ types were collected by using Landsat images and Google Earth. LCZ Classification and Filtering were performed by SAGA GIS. An ArcGIS was used to analyze the characteristics of each LCZ type. The characteristics of the LCZ types were analyzed by focusing on building surface fraction ratio, impervious surface fraction ratio, pervious surface fraction ratio, building stories and air temperature. The results show that one filtering was found to be most appropriate. While Yangcheongu and Yeongdeungpogu with the higher annual and maximum mean air temperature than other areas have the higher rate of LCZ 3(compact low-rise) and LCZ 4(open high-rise), Jongnogu, Eunpyeonggu, Nowongu and Gwanakgu with the lower value have the higher rate of LCZ A(Dence trees). The values of building surface fraction ratio, impervious surface fraction ratio and building stories of each LCZ were included in the range of WUDAPT for most LCZs. However, the values of pervious surface fraction ratio were out of the range, in particular, in the LCZs 4~6 and 9~10. This study shows the usability and applicability of the WUDAPT methodology and its climate zone classification used in many countries as a basic data for the landscape planning and policy to improve the thermal environment in urban areas.

• Korean Journal of Social Welfare
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• v.54
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• pp.35-64
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• 2003

This study is to explore experience on the inter-organizational linkages for vocational rehabilitation of people with psychiatric disabilities and to find out predictors affecting the linkages. This study used triangulation a way as to combine the advantages of both the qualitative and the quantitative approach. The qualitative approach is based on grounded theory by Strauss & Cobin. The quantitative research used the samples of 122 organizations, and was analyzed by multiple regression & logistic regression. The results are as follows. First, as results of in-depth interview, interviewees perceived linkage experience as 'turning the eyes on the other organization in community', namely 'the pursue of collaboration'. This concept is classified four types: initiative, cooperative, authoritative and passive type. Second, according to Tobin(1986)'s five phase of the closeness of inter-organizational linkages, our linkages were found to be in third phase, "coordination". Finally, predictors of the attempt of having linkages & linkage strengthening were analyzed by logistic regression & multiple regression. 'The experience of professional training on people with psychiatric disabilities' and 'the resource dependency' have statistically significant relation with the attempt to have linkages. 'The length of current service', 'the experience of professional training on people with psychiatric disabilities', and 'recognition about other organizational activities' are significantly related to strengthening linkage. Based on the results of this study, alternatives for promoting inter-organization linkages for vocational rehabilitation of people with psychiatric disabilities were proposed.

• Journal of The Korean Association of Information Education
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• v.19 no.4
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• pp.431-440
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• 2015

The purpose of this study is to analyze the influence of digital textbook use for potential risk group of internet addiction and average group. For this, I selected two samples. One was a group to use digital textbook at school, the other was a group not to use that. Then each potential risk group of internet addiction were sort out in two groups by a test of internet addiction. I firstly compared a average user group and a potential risk group of internet addiction in group to use digital textbook. Also, I too did that in group not to use digital textbook. Then I analyzed the relevance of using digital textbook and internet addiction. As a result, I found that using digital textbook have not the relevance with internet addiction. But in this paper, I proposed a way to teach ICT ethical training before students use the digital textbook, because most of potential risk students of internet addiction tend to think they was influenced by digital textbook.