• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.126.1-126.1
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• 2010

In direct methanol fuel cells(DMFCs), it is well known that methanol crossover severely reduces the cell performance and the cell efficiency. There are a number of design and operating parameters that influence the methanol crossover. This indicates that a DMFC demands a high degree of optimization. For the successful design and operation of a DMFC system, a better understanding of methanol crossover phenomena is essential. The main objective of this study is to examine methanol-crossover phenomena in DMFCs. In this study, 1D DMFC model previously developed by Ko et al. is used. The simulation results were compared with methanol-crossover data that were measured by Eccarius et al. The numerical predictions agree well with the methanol crossover data and the model successfully captures key experimental trends.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.104-119
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• 2022

Life safety awareness level diagnosis is necessary for customized safety education and continuous safety awareness. As the starting stage of safety education for each life cycle, a scale that has verified the reliability and validity of high school students' life safety awareness has not yet been developed. In this context, the purpose of this study is to develop and validate the life safety awareness scale of high school students and to analyze interindividual differences. Questionnaire data was collected from April to June 2022 from 834 students in the first, second, and third grades of high schools in △△ city in Jeollabuk-do. A final 25-item scale was developed using the preliminary survey, preliminary test, the main test, descriptive statistical analysis, and exploratory and confirmatory factor analysis. This scale consists of four sub-factors: 'safety prevention', 'safety knowledge', 'safety preparation', and 'safety protection'. Good reliability and validity were verified by analysis of content validity and construct validity. The generalizability of the scale was verified by crossover validation between the search group and the crossover group. Based on the interindividual differences analysis, although there was a difference between genders in life safety awareness, there was no difference by grade level and academic achievement. This study is significant in developing the first valid scale that can measure high school students' life safety awareness and providing the necessity and rationale for life safety education by life cycle considering individual gender differences.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.125.2-125.2
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• 2010

In this paper, both theoretical and experimental investigations have been performed to examine the effects of key operating parameters on the cell performance of a DMFCs (i.e., methanol feed concentration and operating temperature). For experiment, the membrane electrode assemblies (MEAs) were prepared using a conventional MEA fabrication method based on a catalyst coated electrode (CCE) and tested under various cell temperatures and methanol feed concentrations. The polarization curve measurements were conducted using in-house-made $25cm^2$ MEAs. The voltage-current density data were collected under three different cell temperatures ($50^{\circ}C$, $60^{\circ}C$, and $70^{\circ}C$) and four different methanol feed concentrations (1 M, 2 M, 3 M, and 4 M). The experimental data indicate that the measured I-V curves are significantly altered, depending on these conditions. On the other hand, previously developed one-dimensional, two-phase DMFC model is simulated under the same operating conditions used in the experiments. The model predictions compare well with the experimental data over a wide range of these operating conditions, which demonstrates the validity and accuracy of the present DMFC model. Furthermore, both simulation and experimental results exhibit the strong influences of methanol and water crossover rates through the membrane on DMFC performance and I-V curve characteristics.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.271-277
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• 2018

The aim of this study was to select useful items for assessing fall risk in community-dwelling elderly. This study assumed five fall risk factors: Symptoms of falling, physical function, disease and physical symptoms, environment, and behavior and character based on previous studies. The questionnaire consisted of 44 items according to the contents validation, crossover analysis and factor analysis. The Korean version of the Fall Risk Assessment Scale (FRA-K) will be used as a useful tool to improve the fall problems perceived to be serious social problems and to provide important information for prevention of falls.

• Journal of Pharmaceutical Investigation
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• v.38 no.2
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• pp.135-142
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• 2008

The present study describes the evaluation of the bioequivalence of two atorvastatin tablets, Lipitor $Tablet^{(R)}$ (Pfizer, reference drug) and Atorva $Tablet^{(R)}$ (Yuhan, test drug), according to the guidelines of Korea Food and Drug Administration (KFDA). Forty-nine healthy male Korean volunteers received each medicine at the atorvastatin dose of 40 mg in a $2{\times}2$ crossover study with a two weeks washout interval. After drug administration, serial blood samples were collected at a specific time interval from 0-48 hours. The plasma atorvastatin concentrations were monitored by an high performance liquid chromatography -tandem mass spectrometer (LC-MS/MS) employing electrospray ionization technique and operating in multiple reaction monitoring (MRM) and positive ion mode. The total chromatographic run time was 4.5 min and calibration curves were linear over the concentration range of 0.1-100 ng/mL for atorvastatin. The method was validated for selectivity, sensitivity, linearity, accuracy and precision. $AUC_t$ (the area under the plasma concentration-time curve from time zero to 48hr) was calculated by the linear log trapezoidal rule method. $C_{max}$ (maximum plasma drug concentration) and $T_{max}$ (time to reach $C_{max}$) were complied trom the plasma concentration-time data. Analysis of variance was carried out using logarithmically transformed $AUC_t$ and $C_{max}$. No significant sequence effect was found for all of the bioavailability parameters indicating that the crossover design was properly performed. The 90% confidence intervals of the $AUC_t$ ratio and the $C_{max}$ ratio for Atorva $Tablet^{(R)}$ / Lipitor $Tablet^{(R)}$ were ${\log}\;0.9413{\sim}{\log}\;1.0179$ and ${\log}\;0.831{\sim}{\log}\;1.0569$, respectively. These values were within the acceptable bioequivalence intervals of ${\log}\;0.8{\sim}{\log}\;1.25$. Based on these statistical considerations, it was concluded that the test drug, Atorva $Tablet^{(R)}$ was bioequivalent to the reference drug, Lipitor $Tablet^{(R)}$.

• Biomolecules & Therapeutics
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• v.9 no.4
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• pp.291-297
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• 2001

The bioequivalence of two triflusal products was evaluated with 20 healthy volunteers following single oral dose according to the guidelines of Korea Food and Drug Administration (KFDA). Trisa $l^{R}$ capsule (Whanin Pharm. Corp., Korea) and Disgre $n^{R}$ capsule (Myung-In Pharm. Corp., Korea) were used as test product and reference product, respectively. Both products contain 300 mg of trifusal. One capsule of test product or reference product was orally administered to the volunteers, respectively, by randomized two period crossover study (2$\times$2 Latin square method). Blood samples were taken at predetermined time intervals for 4 hours and the determination of trifusal was accomplished using semi-microbore HPLC equipped with automated column switching system. The analytical method with HPLC was validated according to the Bioanalytic Method Validation guideline by F7A prior to determining the plasma samples. The pharmacokinetic parameters (AU $C_{0-4h}$ $C_{max}$ and $T_{max}$) were calculated and ANOVA test was utilized for statistical analysis of parameters. As a result of the assay validation, the limit of quantification of trifusal in human plasma by current assay procedure was 50 ng/ml using 500 $\mu$l of plasma. The accuracy of the assay was from 97.76% to 116.51% while the intra-day and inter-day coefficient of variation of the same concentration range was less than 15%. Average drug concentration at the designated time intervals and pharmacokinetic parameters calculated were not significantly different between two products (p>0.05). The difference of mean AU $C_{olongrightarrow4hr}$, $C_{max}$, and $T_{max}$ between the two products (2.92, 4.39, and -2.44%, respectively) were less than 20%. The power (1-$\beta$) and treatment difference ($\Delta$) for AU $C_{olongrightarrow4hr}$ and $C_{max}$ were more than 0.8 and less than 0.2, respectively. Although the power for $T_{max}$ was under 0.8, $T_{max}$ of the two products was not significantly different from each other (p>0.05). These results satisfied the criteria of KFDA guideline for bioequivalence, indicating the two products of triflusal were bioequivalent.quivalent.ent.ent.

• 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.

• Journal of Pharmaceutical Investigation
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• v.38 no.4
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• pp.269-275
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• 2008

The purpose of the present study was to evaluate the bioequivalence of two levofloxacin tablets, Jeil $Cravit^{TM}$ tablet (Jeil Pharm. Co., Ltd., Korea, reference drug) and $Lesacin^{TM}$ tablet (Ilhwa. Co., Ltd., Korea, test drug), according to the guidelines of Korea Food and Drug Administration (KFDA). Twenty-four healthy male Korean volunteers received two tablets containing levofloxacin 200 mg in a $2{\times}2$ crossover study. There was a one-week washout period between the doses. Plasma concentrations of levofloxacin were monitored for over a period of 24 hr after administration by using a high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The area under the plasma concentration-time curve from time zero to 24 hr ($AUC_t$), maximum plasma drug concentration ($C_{max}$) and time to reach $C_{max}\;(T_{max})$ were complied from the plasma concentration-time data. Analysis of variance (ANOVA) test was utilized for the statistical analysis of the parameters using logarithmically transformed $AUC_t$ and $C_{max}$. The 90% confidence intervals of the $AUC_t$ ratio and the $C_{max}$ ratio for $Lesacin^{TM}$/Jeil $Cravit^{TM}$ were $\log\;0.9527{\sim}\log\;0.9981$ and $\log\;0.8712{\sim}\log\;1.0556$, respectively. These values were within the acceptable bioequivalence intervals of $\log\;0.80{\sim}\log\;1.25$, recommended by KFDA. In all of these results, we concluded that $Lesacin^{TM}$ tablet was bioequivalent to Jeil $Cravit^{TM}$ tablet, in terms of rate and extent of absorption.

• Analytical Science and Technology
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• v.22 no.1
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• pp.101-108
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• 2009

The bioequivalence of two pioglitazone tablets, Actos$^{(R)}$ tablet (Takeda Chemical Industries, reference drug) and Pioglitazone tablet (Boryung Company, test drug) was evaluated according to the guidelines of Korea Food and Drug Administration. Twenty-eight healthy male Korean volunteers received each medicine (pioglitazone dose of 30 mg) in a $2{\times}2$ crossover study with one week washout interval. After drug administration, blood samples were collected at specific time intervals from 0-36 hours. The plasma concentrations of pioglitazone were determined by high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The total chromatographic run time was 5 min and calibration curves were linear over the concentration range of 5-2000 ng/mL for pioglitazone. The method was validated for selectivity, sensitivity, linearity, accuracy and precision. The pharmacokinetic parameters were determined from the plasma concentration-time profiles of both formulations. The primary calculated pharmacokinetic parameters were compared statistically to evaluate bioequivalence between the two preparations. The 90% confidence intervals of the $AUC_t$ ratio and the $C_{max}$ ratio for Pioglitazone tablet and Actos$^{(R)}$ tablet were log0.9422~log1.1040 and log0.9200~log1.1556, respectively. Based on the statistical considerations, we can conclude that the test drug, Pioglitazone tablet was bioequivalent to the reference drug, Actos$^{(R)}$ tablet.

• 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.