• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.573-580
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• 2018

Various studies have been conducted to analyze the location appropriateness and capacity of shelters. However, research on how to select new shelters is relatively insufficient. Since the shelter is designated in case of emergency, it is also necessary to efficiently select the location of the shelter. Therefore, this study presented a method for selecting the location of the shelter using network analysis that has been used to analyze the location appropriateness of shelters and genetic algorithm which is a representative heuristic algorithm. First, the network analysis using the existing civil defense evacuation facility data was performed and the result showed that the vulnerability of evacuation has a high deviation by region in the study area. In order to minimize the evacuation vulnerable area, the genetic algorithm was designed then the location of new shelters was determined. The initial solution consisting of candidate locations of new shelters was randomly generated and the optimal solution was found through the process of selection, crossover, and mutation. As a result of the experiment, the area with a high percentage of the evacuation vulnerable areas was prioritized and the effectiveness of the proposed method could be confirmed. The results of this study is expected to contribute to the positioning of new shelters and the establishment of an efficient evacuation plan in the future.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.47-56
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• 2021

This study a secondary data analysis study attempted to identify the factors influencing the successful aging of the elderly in Korea. Using the data of the 7th Aging Research Panel in 2018, 4,106 people over 65 years of age who had at least one non-living child and no missing values in the study variables were enrolled. Data were analyzed by frequency analysis, crossover analysis, independent sample t-test, and binary logistic regression analysis. The results of this study revealed that the factors affecting successful aging among elderly included age, the presence or absence of a spouse, education level, housing type, subjective health, exercise, alcohol drinking, and non-face-to-face contact frequency with non-cohabiting children, and the explanatory power of the variables was 24.1%. In order for the elderly to achieve successful aging, centering on child ties, the frequency of non-face-to-face contact, which can comfort the elderly's life and increase the satisfaction of life in a continuous relationship, is more important than having children live close and meet frequently. Based on this study, various strategies are needed for the successful aging of elderly people who are socially isolated due to concerns about COVID-19 infection.

• Journal of Family Resource Management and Policy Review
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• v.26 no.1
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• pp.27-42
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• 2022

This study was conducted to examine the resources that influence the choice of drop-out students' reentry to school. A total of five years of panel analysis of 2,553 drop-out teenagers from 2013 to 2017 were utilized. In order to verify the resources that affect the choice of school reentry of teenagers with experiences of suspension of studies to formal middle and high school after July 2012, this study analyzed drop-out teenagers' family resources as well as their psychological, mental, and social-relationship resources. A crossover analysis, t-test, and hierarchical logistic regression analysis were conducted. The major outcomes of this study are as follows: First, the socio-demographic variables among the resources that affected the choice of reentry for school of teenager children were the type of family and number of moves to a new house. Second, the psychological and sentimental variable that affected the choice of school reentry was a decreasing level of positive recognition of the situation of suspension of studies combined with depression, impulsiveness, and perceiving society as one that discriminates based on the level of education. Third, significant family resource variables were the type of family form and parents providing economic support, which is a subfactor of parental attachment. Fourth, the presence of a mentor as a helpful social-relationship resource had a significant effect on relational resources. This study is significant in the sense that the positive family resources that affect the choice of school reentry of drop-out teenage students were determined, and the positive directivity of supportive family resources is presented for parents with teenage children experiencing a suspension of studies.

• The Journal of Korean Medicine
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• v.43 no.2
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• pp.61-74
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• 2022

Objectives: This study aimed to explore the short-term effects of bilateral masticatory training using an intraoral device on memory and concentration, which is an advanced form of Gochi, compared to the unilateral form with gum. Methods: Thirty young healthy participants (age, 16-30 years) were screened and randomly assigned to one of two sequences in a crossover design. The participants assigned to sequence A (n=15) performed bilateral mastication using an intraoral device with a total of 300 taps, followed by unilateral mastication using gum with the same number of repetitions and frequency, separated by a 7-day washout period. A reverse order was used for sequence B. The primary and secondary outcomes were the digit span test result and the symbol digit modality test and the word list recall results, respectively, which were conducted before and after each intervention. Results: Symbol digit modality test scores increased by 12.03±8.33 with bilateral mastication, which was significantly higher than that obtained with chewing gum (5.17 points;95% confidence interval: 0.99, 9.34; p<0.05). Changes in the digit span test and word list recall scores were not significantly different between the two groups. In the digit span test forward, symbol digit modality test, and word list recall test, bilateral mastication was not inferior to unilateral mastication in improving memory and concentration. Conclusions: Bilateral masticatory exercises using an intraoral device are not inferior to unilateral mastication with gum for improving memory in healthy young individuals. Further research is needed to determine the efficacy of bilateral masticatory training on cognitive function.

• The Korean Journal of Pharmacology
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• v.26 no.2
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• pp.219-226
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• 1990

Enalapril maleate tablets of two different producers were tested for bioequivalence. Enalapril is rapidly metabolized to an active metabolite, enalaprilat which inhibits angiotensin-converting enzyme (ACE). The pharmacokinetics of enalapril maleate and the time course of inhibition of plasma ACE activity after administration of the drugs were studied. Two single doses of 10mg each of enalapril maleate were administered orally to twelve male volunteers in a balanced, randomized, two-way crossover investigation. Plasma enalaprilat concentrations were determined over a 23-hour after the dose by enzyme inhibition assay and enalapril by the same method following in vitro hydrolysis. Urinary recoveries of enalapril and enalaprilat were determined for the calculation of renal clearance. Plasma ACE activity was determined by an enzyme assay. Peak plasma levels of enalapril were observed about 1 hour after the doses, and practically all enalapril had disappeared from plasma within 6 hour. Peak enalapril concentrations of both formulations were almost identical ($Vasotec^{\circledR}$, 61.38 ng/ml; $Beartec^{\circledR}$, 64.27 ng/ml). The values of the pharmacokinetic parameters of enalaprilat computed for $Vasotec^{\circledR}$ and $Beartec^{\circledR}$ tablets are presented in that order; area under the curve=330.63:320.96 $ng{\cdot}hr/ml$; peak concentration=38.63:39.43 ng/ml; time to peak=3.83:4.08 hour; elimination half-life=3.95:3.92 hours. No statistically significant difference was detected when area under the curve and all other parameters were compared. Using criteria of 95% confidence interval for the comparison of these parameters, only the upper limits of area under the curve and time to peak of enalapril were over 120%. All the parameters of enalaprilat were acceptable. Percent inhibition of plasma ACE to plasma enalaprilat concentration showed the sigmoid concentration-inhibition relationship. Time courses of plasma ACE inhibition after the administration of both formulations were quite similar. The formulations were found to be equivalent when compared on the premise that no significant difference was detected when pharmacokientic parameters and inhibition of ACE activity were compared, based on the confidence limits analysis.

• Archives of design research
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• v.18 no.1 s.59
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• pp.167-174
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• 2005

This study is to objectively support the emotional and intuitional decision making of the designer by means of developing the supporting models and tools of color coordination. Based on the color grouping system and representative vocabularies suggested in the precedent 'Study on the Grouping System of Fabric Color,' this study suggested the manufacture of the supporting model of color coordination that could be used practically through the design of coloring group. The results of this study can be summarized as below. Firstly, 687 colors in total have been collected from the four world famous collections, the street fashion of 2002 F/W 2003 S/S Season and the representative brands in each group for five years from 1999 to 2003 in order to single out the basic colors for the purpose of composing the color groups. Secondly, 687 collected colors have been grouped into 144 colors in total through the three-step process for the extraction of coloring groups. Thirdly, the final extracted colors have been divided into , , , group by the grouping system specified in the precedent study and the said four large groups have been again subdivided into 12 small groups. Fourthly, the suggested colors in each group have established a color coordination system by introducing the concept of the crossover coordination that could be matched with other groups as well as the coordination within the group. Fifthly, we have dyed 144 colors in total that have consisted of the coloring system of four representative groups (twelve subgroups) in each methodical tone as in the above in cotton yarn, one of the representative materials in fabric fashion design industry. Besides, we have specified the symbol of the Pantone Color Book and CMYK values in each color that has consisted of the system considering the industrial characteristics of fashion as a global business and the compatibility with the related design industry. Sixthly, we have packed the completed yam made of fabrics in the designed container for the easy use of cross-coordination and have completed a color coordination system that could be easily utilized for the fashion-related working-level staffs.

• Journal of Nutrition and Health
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• v.53 no.5
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• pp.464-475
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• 2020

Purpose: This study compared the effects of a high glycemic load (high GL) diet and low glycemic load (low GL) diet on the body weight, body fat, blood pressure, and blood lipid indicators. Methods: Twenty-one young adults aged between 21 and 28 years who were overweighted or obese (body mass index [BMI] between 23 and 33.5 kg/㎡) before the study and after calorie reduction diets with either low GL or high GL for 2 weeks each were examined. The study was a randomized crossover design with a 2-week washout period between the 2 types of diet. The order of the low GL and high GL diet periods was randomized. The body weight, body fat, blood pressure, levels of blood lipids, fasting glucose, insulin, homeostatic model assessment (HOMA) insulin, C-peptide, and HOMA C-peptide were measured at the baseline, as well as 2, 4, and 6 weeks after starting the experiment. Results: When subjects were on the low GL diet, they lost more weight than those eating the high GL diet (mean ± SD, -2.77 ± 1.09 vs. -1.56 ± 0.78 kg; p < 0.001); there were greater decreases in body fat mass (-1.62 ± 1.19 vs. -0.88 ± 0.91 kg; p = 0.024) and BMI (-0.95 ± 0.32 vs. -0.56 ± 1.08 kg/㎡; p < 0.001). On the other hand, there were no significant differences in changes in biochemical parameters, such as blood lipids and fasting glucose levels, and blood pressure. The body weight, body fat mass, BMI, percent body fat, blood pressure, cholesterol (total, low-density lipoprotein, and high-density lipoprotein), fasting glucose, C-peptide, HOMA-insulin resistance-C-peptide levels were decreased significantly at 6 weeks. Conclusion: The low GL diet may be more effective in losing body weight, body fat mass, and BMI than the high GL diet for 2 weeks in healthy young overweight or obese adults.

• 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.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.