• Korean Journal of Animal Reproduction
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• v.22 no.3
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• pp.287-292
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• 1998

The objective of this study was to test whether ZP drilling using a 1.48$\mu$m diode laser beam on mouse IVF embryos becomes effective the hatching and normal in vivo development, as a preliminary test for obtaining the additional proof that the 1.48$\mu$m diode laser could be used safely for human applications. The results obtained in this experiment were as follows: when the hatched rates of mouse embryos by laser ZP drilling according to the embryonic stage were examined until 72 hr (in case of blast tocyst: day 4 after IVF) or 120 hr (in case of 4-cell: day 2 after IVF) after treatment, the d data of laser drilled blastocysts (81.8%) was significantly higher than those of control (hatching blastocyst: day 4 after IVF) (54.2%) and laser drilled 4-cell embryos (45.5%) (p<0.05). When the effect of laser drilling on implantation rates following embryo transfer in day 3 synchronized pseudopregnant recipients was examined, the l laser drilled group (48.7%) was slightly higher than that of control group (43.6%). In addition, when the several pregnant mice delivered in two groups were analysed their chromosomal normality and tested reproductive ability, all p pups were presented normal chromosomal number (n=40) and showed normal growth and reproductive ability. Therefore, these results dem-onstrated that ZP drilling using a 1.48$\mu$m diode l laser can increase the embryo hatching and ind duce the normal pregnancy of mouse embryos.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.973-979
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• 2020

This study aims to objectively and quantitatively determine the social emotion of empathy by collecting pupillary response. 52 subjects (26 men and 26 women) voluntarily participated in the experiment. After the measurement of the reference of 30 seconds, the experiment was divided into the task of imitation and spontaneously self-expression. The two subjects were interacted through facial expressions, and the pupil images were recorded. The pupil data was processed through binarization and circular edge detection algorithm, and outlier detection and removal technique was used to reject eye-blinking. The pupil size according to the empathy was confirmed for statistical significance with test of normality and independent sample t-test. Statistical analysis results, the pupil size was significantly different between empathy (M ± SD = 0.050 ± 1.817)) and non-empathy (M ± SD = 1.659 ± 1.514) condition (t(92) = -4.629, p = 0.000). The rule of empathy according to the pupil size was defined through discriminant analysis, and the rule was verified (Estimation accuracy: 75%) new 12 subjects (6 men and 6 women, mean age ± SD = 22.84 ± 1.57 years). The method proposed in this study is non-contact camera technology and is expected to be utilized in various virtual reality with smart glasses.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.339-348
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• 2017

One of the major issues of statistical process control for variables data is monitoring both the mean and the standard deviation. The traditional approach to monitor these parameters is to simultaneously use two seperate control charts. However there have been some works on developing a single chart using a single plotting statistic for joint monitoring, and it is claimed that they are simpler and may be more appealing than the traditonal one from a practical point of view. When using these control charts for variables data, estimating in-control parameters and checking the normality assumption are the very important step. Nonparametric Shewhart-Lepage chart, proposed by Mukherjee and Chakraborti (2012), is an attractive option, because this chart uses only a single control statistic, and does not require the in-control parameters and the underlying continuous distribution. In this paper, we introduce the Shewhart-Lepage chart, and propose the design procedure to find the optimal diagnosis limits when the location and the scale parameters change simultaneously. We also compare the efficiency of the proposed method with that of Mukherjee and Chakraborti (2012).

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.9
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• pp.955-960
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• 2008

We investigated and estimated at the characteristics of decomposition and mineralization of benomyl using a design of experiment(DOE) based on the general factorial design in an E-beam process, and also the main factors(variables) with benomyl concentration(X$_1$) and E-beam irradiation(X$_2$) which consisted of 5 levels in each factor was set up to estimate the prediction model and the optimization conditions. At frist, the benomyl in all treatment combinations except 17 and 18 trials was almost degraded and the difference in the decomposition of benomyl in the 3 blocks was not significant(p > 0.05, one-way ANOVA). However, the % of benomyl mineralization was 46%(block 1), 36.7%(block 2) and 22%(block 3) and showed the significant difference of the % that between each block(p < 0.05). The linear regression equations of benomyl mineralization in each block were also estimated as followed; block 1(Y$_1$ = 0.024X$_1$ + 34.1(R$^2$ = 0.929)), block 2(Y$_2$ = 0.026X$_2$ + 23.1(R$^2$ = 0.976)) and block 3(Y$_3$ = 0.034X$_3$ + 6.2(R$^2$ = 0.98)). The normality of benomyl mineralization obtained from Anderson-Darling test in all treatment conditions was satisfied(p > 0.05). The results of prediction model and optimization point using the canonical analysis in order to obtain the optimal operation conditions were Y = 39.96 - 9.36X$_1$ + 0.03X$_2$ - 10.67X$_1{^2}$ - 0.001X$_2{^2}$ + 0.011X$_1$X$_2$(R$^2$ = 96.3%, Adjusted R$^2$ = 94.8%) and 57.3% at 0.55 mg/L and 950 Gy, respectively. A Microtox test using V. fischeri showed that the toxicity, expressed as the inhibition(%), was reduced almost completely after an E-beam irradiation, whereas the inhibition(%) for 0.5 mg/L, 1 mg/L and 1.5 mg/L was 10.25%, 20.14% and 26.2% in the initial reactions in the absence of an E-beam illumination.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1556-1566
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• 2020

The purpose of this study was to investigate the effect of Nurida-Ball exercise on isokinetic muscle function, spinal alignment, and dynamic balance capacity in middle-aged men. All middle-aged men(n=16) were divided into 2 groups: Ball exercise(BE, n=8) and control(CON, n=8) group. BE group performed the Nurida-Ball exercise(30 min/day, 3 days/week, 8 weeks) and isokinetic knee and trunk muscle function, spinal alignment, and dynamic balance capacity were measured. All of the measured variables calculated the mean and standard deviation and verified normality using the Shapiro-Wilk test. The independent t-test method and the Paired t-test method were then analyzed to identify differences between groups. This study found that isokinetic knee and trunk muscle function was significantly strengthened in the BE compared with CON group by increasing peak torque(PT) of right and left knee extension(60°/sec, p<0.01, respectively), average power(AP) of right and left knee extension(60°/sec, p<0.05, p<0.01, respectively), and PT of right knee flexion(180°/sec, p<0.05) and AP of right knee extension(180°/sec, p<0.05). In the change of isokinetic trunk muscle function, only PT of trunk extension(180°/sec) was increased in the BE compared with the CON group(p<0.05). In addition, Nurida-ball exercise can improve the spinal alignment by reducing the trunk inclination(p<0.05) in the BE compared with the CON group. Finally, dynamic balance capacity was also enhanced in the BE compared with the CON group by decreasing the score of overall balance index(OBI, p<0.01) and Antero-posterior balance index(p<0.05) in the Stage-6, and OBI(p<0.05) in the Stage-1. This result demonstrated that Nurida-ball exercise may improve spinal alignment, dynamic balance capacity, and isokinetic muscle function, which might be an effective way for the improvement of health-related fitness in middle-aged men.

• The Journal of Korean Academy of Prosthodontics
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• v.54 no.3
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• pp.234-238
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• 2016

Purpose: Increasing use of computer aided design-computer aided manufacturing (CAD-CAM) system and number of design software made design of restoration easy and quick. Outcome of restoration has been dependent on dental technician's wax up proficiency, dentists can design restoration for themselves now. This study aims to investigate the outcome of restoration designs, according to handling skill of CAD-CAM design tool. Materials and methods: A patient's mandibular right 1st molar was prepared. After taking impression, stone model was made, scanned the stone model with 3 shape intra-oral scanner, stereolithography (STL) file was extracted. With 3shape dental designer, one dental technician with more than 5 years work experience (designer 0) and three dental technicians with less than 2years work experience (designer 1, 2, 3-group DT) and 4 1st year residents (designer 4, 5, 6, 7-group RT) designed gold crown on the same STL file. Designed crown's MD (mesio-distal) and BL (bucco-lingual) diameter, height of crown, inter-cuspal distance, number of occlusal contact points were compared. Statistical analysis was carried out, test of normality within each group, using independent t-test. Number of contact points were compared, using Wilcoxon signed-rank test. Results: There was no significant difference between group DT and group RT. Number of contact points also resulted in no significant difference. Conclusion: The outcome of each designed crowns showed no statistical differences, in values which can be expressed as numbers. Subjective factors were different. With increasing proficiency in handling designing software, fabrication of restorations according to each designer's occlusal concept can be made easy.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.179-188
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• 2023

Purpose. The purpose of this study was to evaluate the effect of surface treatments on the shear bond strength of two types of zirconia (3-TZP and 5Y-PSZ) with resin cement. Materials and methods. Two different types of zirconia specimens with a fully sintered size of 14.0×14.0×2.0 mm³ were prepared, polished with 400, 600, and 800 grit silicon carbide paper, and buried in epoxy resin. They were classified into four groups each control, sandblasting, primer, and sandblasting & primer. Cylindrical resin adhered to the surface-treated zirconia with resin cement. It was stored in distilled water (37℃) for 24 hours, and a shear bond strength test was performed. The normality of the experimental group was confirmed with the Kolmogorov-Smirnov & Shapiro-Wilk test. The interaction and statistical difference were analyzed using a two-way ANOVA. A post-hoc analysis was performed using Dunnett T3. Results. As a result of two-way ANOVA, there was no significant difference in shear bonding strength between zirconia types (P > .05), but there was a significant correlation in the sandblasting, primer, and alumina sandblasting & primer group (P < .05). Dunnett T3 post-test showed that, regardless of the type of zirconia, shear bonding strength was sandblasting & primer > Primer > sandblasting > control group (P < .05). Conclusion. There was no difference in shear bond strength between the types of zirconia. The highest shear bond strength was shown when the mechanical and chemical treatments of the zirconia surface was performed simultaneously.

• Journal of Korean Medicine Rehabilitation
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• v.24 no.4
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• pp.155-162
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• 2014

Objectives Advanced researches on the relationship between obesity and heart rate variability (HRV), heretofore, focused on characteristics of HRV depending on the state of obesity. However, the previous researches have not quantified predictive power of HRV toward the obesity-related variables, which is rather more meaningful for clinicians who regularly treat obese patients. Hence, we designed a research to investigate whether HRV could predict serum levels of obesity-related metabolites. Methods Ninety obese premenopausal women meeting the inclusion criteria were recruited. The HRV test, blood sampling, and measurement of physical traits were conducted. Multiple regression analysis of the measurement data was carried out, putting obesity-related metabolites (insulin, glucose, triglyceride, hs-CRP, HDL, LDL, total cholesterol) as outcome variables and the others as predictors. To select appropriate predictive variables, the Akaike's Information Criterion (AIC) was applied. Normality and homoskedasticity of residuals for each model were tested to identify if there were any violations of the regression analysis's basic assumption. Logarithm transformation was used for the values of the concentration of metabolites and the HRV. Results The regression model including Total Power (TP) value and BMI had significant predictive power for serum insulin concentration (F(2, 88)=835.7, p<0.001, $R^2=0.95$). The regression coefficient of ln (TP) was -0.1002. However, it was not sure if the HRV could predict concentrations of other metabolites. Conclusions The results suggest that the Total Power (TP) value of the HRV can predict the level of serum insulin. If the BMI could be assumed as being constant, when the TP value is multiplied by n, the predicted change of insulin could be drawn by multiplying $n^{-0.1002}$. The uncertainty of this model can be assumed as approximately 5%.