• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.101-118
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• 2014

"Career maturity" is very crucial for high school students since it has an impact on their career path and decision-making. Not only that, it is also important in self-realization and happiness as well as maximizing human resources. When it comes to understanding high school students' career path, it is necessary to know how they perceive school life since they spend most of their time in school. It's also vital to observe in the perspective of students' personal growth. This study seeks to understand the relationship between "adaptation to school life" "self-esteem" and "career maturity". To accomplish this, the 7th additional surveys conducted by Welfare Panel Study were used. The survey was conducted among 496 high school students in order to come up with descriptive statistics and correlation between "adaptation to school life" and "self-esteem" as well as the level of "career maturity". Hierarchical multiple regression analysis was used to understand the effects of "adaptation to school life" and "self-esteem" on "career maturity." The Baron and Kennny mediation analysis were used to understand the effects when the mediating role of "self-esteem" comes into the relationship between "adaptation to school life" and "career maturity". The results of the analysis are as follows: First, the average age for high school students' "career maturity" is 2.07, while it is 1.91 for "self-esteem". For "adaptation to school life," the relationship between "obedience to school regulations" and "relationship with friends" was relatively higher than the relationship between "attitude toward school life" and "interest in school life" Second, high school students' "career maturity" "adaptation to school life" and "self-esteem" were thought to be statistically meaningful since it showed that they had a positive relationship with each other. Third, "interest in school life" "attitude toward school life" and "obedience to school life" and "relationship with friends" in which all of these are the sub factors for "adaptation to school life" together with "self-esteem" had an influence on high school students' "career maturity". Lastly, the relationship between "adaptation to school life" and "career maturity" was proved to be influenced by the partial mediating role of "self-esteem". As the study seeks to find relationships and the factors that affect high school students' "career maturity" meaningful information is given out for the development and progress of educational programs for "career maturity". This was done by understanding the fundamental and systematic approach towards "career maturity" in the students' perspective.

• The Journal of the Korean Society for Microbiology
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• v.22 no.1
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• pp.79-93
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• 1987

The result of various researches mainly in search of 194 Shigella strains, isolated by the Health Research Centers(situated in Seoul city, Inchon city, Pusan city, Kyonggi-Do, Kangwon-Do, Chungchongnam and Buk-Do, Kyongsangnam and Buk-Do, Jollanam and Buk-Do, and Jaeju-Do) in addition to those clinical laboratories of all the general hospitals situated down twon Seoul, conducted during the month of Jan. through Dec. 85, through the reisolating-activity program following its transportation into the laboratory, particularly for a complete check on its correctiveness, are as follows: 1. Isolation processes were performed with the 194 strains obtained from each placeduring the period of investigation: 164 Strains(84.5%) of Sh. flexneri, B group; 6 Strains(3.1%) of Sh. boydii, C group; 24 Strains(12.3%) of Sh. sonnei, D group, which means there's quite a lot in B group while Sh. dysenteriae, A group was not isolated at all. 2. The isolation rate of the 164, B group for subserotype was 1b, 84(51.2%) the highest one, 2(1.2%) on 3a the lowest one, 4, on C group; In D group subserotype II showed 14(58.4%) more than subserotype I. 3. The biological data on sexuality regarding the isolation-strain showed traditional particularity. But the subserotype 1b in B group 2(2.4%) showed gas-growth from glucose. In subserotype 1a, the indole-growth was 88.9% on masculine which was considerably a good one. In the test of arginine dihydrolase subserotype I among D group showed 100% masculine rate. The subserotype 6 among B group showed 92.5% masculine. In the dissolution test of manitol, all subserotypes showed 100% maculine except subserotype 1b. In the dissolutioning test of rhamnose, the subserotype I among D group showed 100% masculine which is the unusual one. 4. Interms of the area among 13 districts examined, Kangwon-Do had 41(21.1%) which is the highest one on its ratio. 5. In terms of season on the strain isolation category, 44(22.7%) is the number isolated in April which is the highest one. 6. In terms of ages, the strain isolation ratio was notably high above the ages of 60 which was 34(17.5%). Next one was 29(14.9%) which was under the ages of 4. 7. In terms of sex, female was 113 or 58.2% while male was 74 or 38.2%, which means the female had more than the male. 8. The result of the resisting capability on the usage of 12 antibiotic medication was; 100% on chloramphenicol; 94.3% on tetracycline, 82.0% on streptomycin, 76.3% on carbenicillin, 74.7% on ampicillin, in regular order. The strain source bearing multimedication resisivity against the 5 antibiotic medication is as many as 117 or 60.3%. Of which 43.3% of 1b sub serotype, B group was the best one, and thus the resistivity against the antistrain medication seems the tendency is being changed. The summing up of the above result shows the total specific strains isolated in each branch in Korea is 194, of which the main type is Sh. flexneri 84.5%. The isolating rate is almost evenly spreading, although the Kangwon-Do showed the highest rate on the above data. It also shows female is higher than male on its statistics. The tendency on age category showed both on old and infancy generations high. However, the resistant capability against antibacteria medication or vaccine was still remaining on habitual one, particularly tending towards multimedication or vaccine trend.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.6
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• pp.62-72
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• 2015

The objective of this research is to investigate the different aspects in which users positively identify with indoor green walls and the influences that it has on usage behaviors. Under the hypothesis that public space users prefer locations with green walls, the effect on their behavior was observed. After installing indoor green walls, behavioral observations and questionnaires were carried out to analyze green wall preferences. The observation experiment was carried out for a total of 8 days in order to see what influences the preferences for of indoor green walls had on usage behaviors and compare a control group with an experimental group that experienced a green wall. The usage time data were put into an SPSS statistics program and used to run an independent sample t-test. The questionnaire was carried out for two days from March 1st to 2nd 2014 after the observation was completed, and was done by 224 users of the two areas. The results from the experiment are as follows. First, comparisons between the total usage time of seats adjoined to partitions in both the green walled area and the partitioned area showed that there was no preference for indoor green walls. Second, the results appeared to show a higher percentage of women users in the green walled area, compared to the original partitioned area. Third, it showed that partitions and plants did not have any influence on seat choices. Fourth, the questionnaire showed preferences for indoor green walls. Out of the 94 people who sat in the partitioned area, 11.7% answered that they wanted to sit in the green walled area, they couldn't due to the lack of available seats. Furthermore, out of the 130 people who sat in the green walled area, 24.6% said they chose the seat because of their preference for the green wall. Although 64.3% of users of the two areas said that they would choose the green walled area if under the same circumstances, the behavior observation did not reflect this.

• The Journal of Korean society of community based occupational therapy
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• v.6 no.2
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• pp.51-59
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• 2016

Objective : The purpose of study is to provide basic information about the effects of leisure time use and leisure activity performance for intellectual disabilities residing in a residential care facility by participating a regular rehabilitation sports program. Methods : Participants were recruited 8 individual with intellectual disability in a residential care facility in Yong-in city and the study period lasted 12 weeks, from september 1 to November 30 in 2015. As a program, participants participated a muscle strengthening exercise using a Gym-ball and a elastic band. In order to analyze leisure time-use, time questionnaire was used every month to analyze total time and exercise frequency. Also, analyze the effects of leisure activity performance, Canadian Occupational Performance Measure(COPM) was used to performance and satisfaction of dynamic leisure activity. Collected data was encoded by item and analyzed with SPSS ver18.0. Descriptive statistics were used for the participants' general information. A non-parametric test (the Friedman test) was used to analyze leisure time-use. A non-parametric test (the Wilcoxon's signed ranked test) was used to analyze to the effects of leisure activity performance. Statistical significance was accepted outside the 95% confidence interval. Results : The results of the total time and the exercise frequency showed significant increase. Also, the results of the performance and the satisfaction showed significant increase. Conclusion : Thus, the participation of the rehabilitation sports program is a vital element to lead to change leisure time use and leisure activity performance for intellectual disabilities residing in a residential care facility. Also, through the providing and the developing a regular rehabilitation sports program systematically, intellectual disabilities residing in a residential care facility have a higher quality of life and satisfaction of the daily routine and life in a residential care facility.

• Journal of radiological science and technology
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• v.40 no.1
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• pp.41-47
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• 2017

Considering that the X-ray apron used in the department of radiology is also used in the department of nuclear medicine, the study aimed to analyze the shielding rate of the apron according to types of radioisotopes, thus ${\gamma}$ ray energy, to investigate the protective effects. The radioisotopes used in the experiment were the top 5 nuclides in usage statistics $^{99m}Tc$, $^{18}F$, $^{131}I$, $^{123}I$, and $^{201}Tl$, and the aprons were lead equivalent 0.35 mmPb aprons currently under use in the department of nuclear medicine. As a result of experiments, average shielding rates of aprons were $^{99m}Tc$ 31.59%, $^{201}Tl$ 68.42%, and $^{123}I$ 76.63%. When using an apron, the shielding rate of $^{131}I$ actually resulted in average dose rate increase of 33.72%, and $^{18}F$ showed an average shielding rate of -0.315%, showing there was almost no shielding effect. As a result, the radioisotopes with higher shielding rate of apron was in the descending order of $^{123}I$, $^{201}Tl$, $^{99m}Tc$, $^{18}F$, $^{131}I$. Currently, aprons used in the nuclear medicine laboratory are general X-ray aprons, and it is thought that it is not appropriate for nuclear medicine environment that utilizes ${\gamma}$ rays. Therefore, development of nuclear medicine exclusive aprons suitable for the characteristics of radioisotopes is required in consideration of effective radiation protection and work efficiency of radiation workers.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.203-211
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• 2021

With the advent of the age of"untact" modern people are pursuing a healthy body and mind. In order to achieve well-being, LOHAS and Wellness,people prefer to use natural affinity alternative therapies, Aromatherapy. This study focuses on women in their 20s~50s in Zhejiang Province, with the aim of investigating their knowledge and use of essential oils.The questionnaire was divided into four parts: 3 questions for general question, 11 questions for knowledge, 13 questions for use and 9 questions for satisfaction. In addition, the study was conducted using the WeChat and the Wenjuanxing Program from July 5 to August 30, 2019. Finally, a total of 617 questionnaires were analyzed. In this study, SPSS WIN 21.0 program is used for frequency analysis. The level of knowledge and satisfaction is verified by Cronbach's α. And the following analysis results were obtained by frequency analysis, descriptive statistics, Chi-squared test(χ2), one-way ANOVA on the understanding level and usege of essential oils according to age. The results were as follows. The most common characteristics of subjects were the 20s, university students, essential oil recognition was high in having experience. There is no great difference in knowledge or satisfaction depending on age. knowledge and satisfaction was moderate. The results of experience in the use of essential oils were higher among all age groups, those who in their 30s did not think that the use of essential oils would be effective. However, people in their 20s and 40s and older have unclear answers, indicating that results showed a difference. The results of the survey on usage showed that there were significant differences in period of use, place of purchase, method of purchase, purpose of use, place of use, number of use, frequency of use, body parts of use. According to the study, awareness and knowledge of essential oils vary according to age, and those in their 20s use essential oils for facial skin, and those in their 30s and older use essential oils for stress relief and body management. This study provides basic information on marketing related to diversified essential oil products according to age.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• 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.

• Korea Science and Art Forum
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• v.32
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• pp.319-334
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• 2018

This research is a case study to make sure the enhancement of students' imagination and creativity through developing and applying the Literature-Art-Science Integrated Education Program. Its research object was totally 25 persons of 29 students of the 1st to the 4 th Grades from Gunsan Sulsan Elementary School. Its research period lasted for 4 months from September to December, 2017, and I, as the research place, used the art room at Gunsan Sulsan Elementary School. The programs were totally 10 sessions with a unit of 1 session per each grade for 2 hours from 1:00 to 3:00 in the afternoon from Monday through Friday. I fixed ten themes of this program-eight plane modeling, and two solid modeling, and finished the work of storytelling during summer vacation. And I arranged their levels as low:middle:high(3:5:2) ones. The former was 'A Film of Monster Gorilla'(L), 'Learning the Spirit of Gyeongju Choi's Family'(M), 'A Tale of My Friend Made of Natural Materials'(L), 'The Reading of My Dream'(M), 'Gathering the Objects in My Mobile'(M), 'A Mock Trial of Marrying Off'(M), 'Painting My Favorite Children's Poem'(H), and 'Painting My Favorite Children's Song'(H), and the latter was 'Seeking for a Bluebird in My Mind'(L), and 'Making My Cherished Object' (M). Then I used the unique art expression technique per each theme, which were in sequence marbling, Korean paper art, combine painting, collage, imaginary painting, imaginary painting, play dough art, imaginary painting techniques. And I delivered to the students the scientific knowledge in terms of growing or manufacturing processes of materials used for making artworks. Prior to and after the processing this program, I surveyed about the students' ability of integrated thinking and emotional experience by 'Figure B Type' and 'Figure A Type' of The Torrance Tests of Creative Thinking, and took statistics with the resultant data. And I executed a paired t-test in order to verify the significance of mean difference in the result of investigation with those data. From the analyzed result according to the elements of creativity and the mean quotients of creativity, there showed a significant difference (t=3.47, p<.01) in 'fluency', and also a significant difference(t=3.59, p<.01) in 'creativity.' Judging from the statistic values of two fields such as the student's ability of integrated thinking and emotional experience, I estimate that over the majority of the students showed the enhancement in self-confident creative expression as well as higher interest and concern through this program. The result that I arranged and analyzed the making process of artworks, the photos of the resultant, etc. as such is as follows : Firstly, from this program being proceeded as art-centered STEAM class, the student's systematic problem-solving ability was improved in his ability of integrated thinking to transform the literary contents into artistic one. Secondly, the student obtained the emotional experience such as interest in the class, self-confidence, intellectual satisfaction, self-fulfillment, etc. through art-centered STEAM class using ten art expression techniques. Thirdly, the student's mind willing to cooperate, communicate with his friends, and care for them was ripened in the process of problem-solving. Fourth, the student's self-confidence was further instilled when presenting famous artists and their artworks in the introduction and finale of ten art expression techniques. Likewise, the statistic values on the fields of student's ability of integrated thinking and emotional experience illustrate that over the majority of the students showed improvement in the ability of creative expression with confidence as well as higher interest and concern upon this program.