• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.443-451
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• 2010

This paper proposes the methodology about advisory safety speed based on real-time vehicular data collected from highway. The proposed model is useful information to drivers by appling seamless wireless communication and being collected from ECU(Engine Control Unit) equipment in every vehicle. Furthermore, this model also permits the use of realtime sensing data like as adverse weather and road-surface data. Here, the advisory safety speed is defined "the safety speed for drivers considering the time-dependent traffic condition and road-surface state parameter at uniform section", and the advisory safety speed model is developed by considering the parameters: inter-vehicles safe stopping distance, statistical vehicle speed, and real-time road-surface data. This model is evaluated by using the simulation technique for exploring the relationships between advisory safety speed and the dependent parameters like as traffic parameters(smooth condition and traffic jam), incident parameters(no-accident and accident) and road-surface parameters(dry, wet, snow). A simulation's results based on 12 scenarios show significant relationships and trends between 3 parameters and advisory safety speed. This model suggests that the advisory safety speed has more higher than average travel speed and is changeable by changing real-time incident states and road-surface states. The purpose of the research is to prove the new safety related services which are applicable in SMART Highway as traffic and IT convergence technology.

• 한국노년학
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• v.29 no.2
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• pp.489-511
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• 2009

While late-life suicide has been increasing and become an important issue for public health in Korea, little is known about the phenomenon and its contributing risk factors based on which effective preventive measures can be made. Since suicidal ideation is a major precursor to attempted and completed suicide, the objective of the present study was to reveal primary contributors to suicidal ideation. Data were collected from a cross-sectional survey of 247 community-dwelling Korean older adults (age≥60) in the mid-size city in Korea. The statistical methodology employed a robust hierarchical regression procedure that iteratively downweights outliers. Based on the stress-diathesis model, the study examined major diathesis and stressors directly explaining suicidal ideation. The study also explored the significant interaction among these factors. The findings revealed that living alone and depression were significant main antecedents of suicidal ideation. In addition, neuroticism X life events and neuroticism X depression were significant interaction terms with the strongest explanatory power, which provides an empirical evidence to support the stress-diathesis model in explaining suicidal phenomenon of the Korean elderly. The result demonstrates the theoretical implication as well as the practical implication for developing and implementing late-life suicide prevention strategies. Limitations and directions for future research are discussed.

• 한국노년학
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• v.29 no.1
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• pp.195-213
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• 2009

The purpose of this study is to analyse how consumption value affects the loyalty of green product through satisfaction and trust among elderly consumers. Data were collected from a cross-sectional survey of 314 older adults (age≥60) in the U. S., who bought and possessed a hybrid car, a representative green product. The statistical methodology is employed a structural equation model. The results demonstrated several important findings. First, perceived social value among elderly population had significant effects on green product satisfaction, while hedonic value did not. Second, both perceived functional value and environment friendly value had a significant positive effect on trust in green products. Third, satisfaction with green products also led to trust in green products. Finally, trust in green products showed their significant effects on loyalty in green product. These results provide practical implications to improve the trust and loyalty in green products among the elderly consumers. Furthermore, by deriving major components of consumption values in green products among the elderly, and analyzing the mechanism of satisfaction, trust, and loyalty, the study emphasizes relationship marketing in implementing "green" marketing strategies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.119-138
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• 2024

Text indicators are increasingly valuable in economic forecasting, but are often hindered by noise and high dimensionality. This study aims to explore post-processing techniques, specifically noise filtering and dimensionality reduction, to normalize text indicators and enhance their utility through empirical analysis. Predictive target variables for the empirical analysis include monthly leading index cyclical variations, BSI (business survey index) All industry sales performance, BSI All industry sales outlook, as well as quarterly real GDP SA (seasonally adjusted) growth rate and real GDP YoY (year-on-year) growth rate. This study explores the Hodrick and Prescott filter, which is widely used in econometrics for noise filtering, and employs sufficient dimension reduction, a nonparametric dimensionality reduction methodology, in conjunction with unstructured text data. The analysis results reveal that noise filtering of text indicators significantly improves predictive accuracy for both monthly and quarterly variables, particularly when the dataset is large. Moreover, this study demonstrated that applying dimensionality reduction further enhances predictive performance. These findings imply that post-processing techniques, such as noise filtering and dimensionality reduction, are crucial for enhancing the utility of text indicators and can contribute to improving the accuracy of economic forecasts.

• Industry Promotion Research
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• v.9 no.1
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• pp.21-29
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• 2024

In the rapidly advancing information society, electronic devices, including smartphones and tablets, are increasingly digitized and equipped with high-performance features such as flexible displays. This study focused on optimizing the manufacturing process for Transparent Conductive Films (TCF) by using the cost-effective conductive polymer PEDOT and transparent substrate PET as alternatives to expensive materials in flexible display technology. The variables considered are production speed (m/min), coating maximum temperature (℃), and PEDOT supply speed (rpm), with surface resistivity (Ω/□) as the response parameter, using Response Surface Methodology (RSM). Optimization results indicate the ideal conditions for production: a speed of 22.16 m/min, coating temperature of 125.28℃, and PEDOT supply at 522.79 rpm. Statistical analysis validates the reliability of the results (F value: 18.37, P-value: < 0.0001, R²: 0.9430). Under optimal conditions, the predicted surface resistivity is 145.75 Ω/□, closely aligned with the experimental value of 142.97 Ω/□. Applying these findings to mass production processes is expected to enhance production yields and decrease defect rates compared to current practices. This research provides valuable insights for the advancement of flexible display manufacturing.

• Korea Journal of Hospital Management
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• v.29 no.2
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• pp.57-79
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• 2024

Purpose: The present study identified the determinants in the development of intrapreneurial intention in small and medium-sized local hospitals. A careful literature review led to the development of a conceptual model which identified two types of employee competence-individual competence and managerial competence-to influence intrapreneurial orientation positively. It was hypothesized that intrapreneurial orientation predicts intrapreneurial intention and is mediated by intrapreneurial commitment. Methodology/Approach: The target population was chosen from two medical institutions of 'D' Hospital and 'E' Geriatric Hospital in Changwon City, South Korea. Samples were collected from 299 respondents who completed a structured questionnaire. Findings: The results from a structural equation modeling statistical analysis indicated that (1) individual competence and managerial competence positively and significantly predict intrapreneurial orientation, (2) intrapreneurial orientation positively and significantly influences intrapreneurial intention, (3) intrapreneurial commitment partially mediates the relation of intrapreneurial orientation to intrapreneurial intention, and (4) the mediation effect of intrapreneurial commitment was significant in the medical-personnel group, but not in the non-medical group. Practical Implications: Overall findings from the present work provide vital insights into understanding the preconditions for developing employee intrapreneurship in small and medium-sized local hospitals.

• 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 Life Science
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• v.23 no.6
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• pp.757-769
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• 2013

A microorganism producing carboxymethylcellulase (CMCase) was isolated from seawater, identified as Bacillus velezensis by analyses of 16S rDNA and partial sequences of the gyrA, and designated as B. velezensis A-68. The optimal conditions for production of CMCase by B. velezensis A-68 were established using response surface methodology (RSM). The optimal concentrations of rice hulls and yeast extract, and initial pH of the medium for cell growth were 60.2 g/l, 7.38 g/l, and 7.18, respectively, whereas those for production of CMCase were 50.0 g/l, 5.00 g/l, and 7.30. The analysis of variance (ANOVA) implied that the most significant factor for cell growth as well as production of CMCase was yeast extract. The optimal concentrations of $K_2HPO_4$, NaCl, $MgSO_4{\cdot}7H_2O$, and $(NH_4)_2SO_4$ in the medium for cell growth were 7.50, 1.00, 0.10, and 0.80 g/l, respectively, which were the same as those for production of CMCase. The optimal temperatures for cell growth and production of CMCase were 30 and $35^{\circ}C$, respectively. The maximal production of CMCase under optimized conditions was 83.8 U/ml, which was 3.3 times higher than that before optimization. In this study, rice hulls, agro-byproduct, were developed as a substrate for production of CMCase and time for production of CMCase was reduced to 3 days using a newly isolated marine bacterium.

• KSBB Journal
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• v.23 no.3
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• pp.257-262
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• 2008

In this study, the reaction conditions for lipase-catalyzed resolution of racemic naproxen 2,2,2-trilfluoroethyl thioester were optimized, and the effect of surfactants was investigated. Among the organic solvents tested, the isooctane showed the highest conversion (92.19%) in a hydrolytic reaction of (S)-naproxen 2,2,2-trifluoroethyl thioester. In addition, the isooctane induced the highest initial reaction rate of (S)-naproxen 2,2,2-trifluoroethyl thioester ($V_s=2.34{\times}10^{-2}mM/h$), the highest enantioselectivity (E = 36.12) and the highest specific activity ($V_s/(E_t)=7.80{\times}10^{-4}mmol/h{\cdot}g$) of lipase. Furthermore, reaction conditions such as temperature, concentration of the substrate and enzyme, and agitation speed were optimized using response surface methodology (RSM), and the statistical analysis indicated that the optimal conditions were $48.2^{\circ}C$, 3.51 mM, 30.11 mg/mL and 180 rpm, respectively. When the optimal reaction conditions were used, the conversion of (S)-naproxen 2,2,2-trifluoroethyl thioester was 96.5%, which is similar to the conversion (94.6%) that was predicted by the model. After optimization of reaction conditions, the initial reaction rate, lipase specific activity and conversion of (S)-naproxen 2,2,2-trifluoroethyl thioester increased by approximately 19.54%, 19.12% and 4.05%, respectively. The effect of surfactants such as Triton X-100 and NP-10 was also studied and NP-10 showed the highest conversion (89.43%), final reaction rate of (S)-naproxen 2,2,2-trifluoroethyl thioester ($V_s=1.175{\times}10^{-2}mM/h$) and enantioselectivity (E = 59.24) of lipase.