• The Transactions of the Korea Information Processing Society
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• v.2 no.5
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• pp.721-732
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• 1995

The IPSE(Integrated Project Support Environment) tool can be seen as a result of an attempt to synthesize the key aspects of language-centered, specific methodology-based and toolkit oriented environments, which are current CASE tools into an organic whole. The IPSE approach based on a process model is regarded as an effective way to implement integrated CASE. The PM-CASE(Process Model based CASE) tool is currently a prototype which draw diagrams describing processes by using a new modeling technique. Attributes related with a task of withen the process model should be defined an saved the database. These attributed are used to retrieve the information of products, and to call the tool related which the task. In this paper, TSEE(Process centered Software Engineering Environment) tools are compared and analyzed. By describing the basic concept, architecture and design of PM-CASE tool, a method of building an process model-based CASE tool is proposed be support an effect software development and management.

• Journal of the Korean Society of School Health
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• v.34 no.2
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• pp.115-122
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• 2021

Purpose: The purpose of this descriptive research is to identify how stress from Covid-19, health beliefs, and social support of college students affect health prevention. Methods: The subjects of the study were 128 university students, excluding health major students, at one university in D City. The survey was conducted from August 1 to 31, 2020. The survey questionnaire consists of 8 items on stress from COVID-19, 12 items adapted from a health belief measurement tool, 12 items from a social support measurement tool, and 11 items adapted from a tool that measures health preventive behaviors. The collected data were analyzed using the hierarchical multiple regression analysis method with SPSS 26.0. Results: In model 1, stress from COVID-19 was statistically significant (β=-.403, p=.003). Model 2 added four health belief factors into Model 1. Stress (β=-.419, p<.001), perceived severity (β=-.193, p=.030), and perceived barriers (β=-.182, p=.009) were statistically significant. In model 3, stress (β=-.413, p<.001), perceived barriers (β=-.147, p=.034), and social support (β=.194, p=.011) were statistically significant. The regression equation was significant (F=15.395, p=<.001) and the model's explanatory power was 53.1%. Conclusion: The results show that when college students had a high degree of health beliefs about COVID-19, the degree of health preventive behaviors was proportionally high. To make them practice preventive health behaviors, it is necessary to develop infection control education programs to improve health beliefs.

• Geomechanics and Engineering
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• v.36 no.1
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• pp.51-69
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• 2024

Currently, composite lining mountain tunnels in China are generally classified based on the [BQ] method for the surrounding rock grade. Increasingly, tunnel field construction is replicated indoors for scale down model tests. However, the development of analogous materials for model tests of composite lining tunnels with different surrounding rock grades is still unclear. In this study, typical Class III and V surrounding rock analogous materials and corresponding composite lining support materials were developed. The whole processes of excavation-support dynamics of the mountain tunnels were simulated. Data on the variation of deformations, contact pressures and strains on the surrounding rock were obtained. Finally, a comparative analysis between model tests and numerical simulations was performed to verify the rationality of analogous material development. The following useful conclusions were obtained by analyzing the data from the tests. The main analogous materials of Class III surrounding rock are barite powder, high-strength gypsum and quartz sand with fly ash, quartz sand, anhydrous ethanol and rosin for Class V surrounding rock. Analogous materials for rockbolts, steel arches are replaced by aluminum bar and iron bar respectively with both shotcrete and secondary lining corresponding to gypsum and water. In addition, load release rate of Class V surrounding rock should be less than Class III surrounding rock. The fenestration level had large influence on the load sharing ratio of the secondary lining, with a difference of more than 30%, while the influence of the support time was smaller. The Sharing ratios of secondary lining in Class III surrounding rock do not exceed 12%, while those of Class V surrounding rock exceed 40%. The overall difference between the results of model tests and numerical simulations is small, which verifies the feasibility of similar material development in this study.

• Journal of Preventive Medicine and Public Health
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• v.47 no.1
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• pp.64-71
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• 2014

Objectives: Physical activity behavior begins to decline during adolescence and continues to decrease throughout young adulthood. This study aims to explain factors that influence physical activity behavior in a sample of female adolescents using a health promotion model framework. Methods: This cross-sectional survey was used to explore physical activity behavior among a sample of female adolescents. Participants completed measures of physical activity, perceived self-efficacy, self-esteem, social support, perceived barriers, and perceived affect. Interactions among the variables were examined using path analysis within a covariance modeling framework. Results: The final model accounted for an $R^2$ value of 0.52 for physical activity and offered a good model-data fit. The results indicated that physical activity was predicted by self-esteem (${\beta}$=0.46, p<0.001), perceived self-efficacy (${\beta}$=0.40, p<0.001), social support (${\beta}$=0.24, p<0.001), perceived barriers (${\beta}$=-0.19, p<0.001), and perceived affect (${\beta}$=0.17, p<0.001). Conclusions: The findings of this study showed that the health promotion model was useful to predict physical activity behavior among the Iranian female adolescents. Information related to the predictors of physical activity behavior will help researchers plan more tailored culturally relevant health promotion interventions for this population.

• Tribology and Lubricants
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• v.16 no.1
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• pp.39-45
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• 2000

In this paper, the effects of shaft and bearing flexibilities are investigated for the accurate modeling of a shaft-bearing system supported by ball bearings. Generally, rolling bearings are modeled by simple rigid pin-joint in the mechanical design. However, they can no longer be modeled by ideal boundary conditions in the advanced applications because the rigid pin-joint model cannot satisfy the current trends of mechanical design decreasing mass and reducing volume. Consequently the flexible support model of ball bearing is investigated using the static analysis module developed by A .B. Jones and T. A. Harris. A simple two-bearing system, supported by two deep groove ball bearings and radially loaded on the shaft midway between the bearings, is utilized to validate the coupled model of shaft-bearing system. Numerical computations using the model indicate that the shaft span length, locating/floating bearing arrangements and applied bearing size are significant factors in determining the mechanical behaviors. The flexible support model of ball bearing can be escaped to over-estimate in the bearing fatigue life. The proposed simple design formulation obtained by numerical simulations can approximately predict a rate of bearing life reduction as a function of shaft span length/shaft diameter (L/d).

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.817-824
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• 2017

Residual stress is a critical element in determining the integrity of parts and the lifetime of welded structures. It is necessary to estimate the residual stress of a welding zone because residual stress is a major reason for the generation of primary water stress corrosion cracking in nuclear power plants. That is, it is necessary to estimate the distribution of the residual stress in welding of dissimilar metals under manifold welding conditions. In this study, a cascaded support vector regression (CSVR) model was presented to estimate the residual stress of a welding zone. The CSVR model was serially and consecutively structured in terms of SVR modules. Using numerical data obtained from finite element analysis by a subtractive clustering method, learning data that explained the characteristic behavior of the residual stress of a welding zone were selected to optimize the proposed model. The results suggest that the CSVR model yielded a better estimation performance when compared with a classic SVR model.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.69-81
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• 2005

As the competition in business becomes severe, companies are focusing their capacity on customer relationship management (CRM) for survival. One of the important issues in CRM is to build a purchase prediction model, which classifies customers into either purchasing or non-purchasing groups. Until now, various techniques for building purchase prediction models have been proposed. However, they have been criticized because their performances are generally low, or it requires much effort to build and maintain them. Thus, in this study, we propose the support vector machine (SVM) a tool for building a purchase prediction model. The SVM is known as the technique that not only produces accurate prediction results but also enables training with the small sample size. To validate the usefulness of SVM, we apply it and some of other comparative techniques to a real-world purchase prediction case. Experimental results show that SVM outperforms all the comparative models including logistic regression and artificial neural networks.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.157-167
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• 2012

The purpose of this paper is to develop an OLAP (online analytical processing) database system that supports the SMBA (Small and medium Business Administration) policy funding. A heterogeneous dimension schema will be central in staged support of policy funds. In this paper, therefore, we designed the FREQUENCY dimension table which has a heterogeneous dimension schema structure. In this paper, we made a model of measuring SME (small and medium-sized enterprise) size first. The model is composed of six determinants of firm growth such as sales, employment, own technology, the operating profit to sales ratio, the debt ratio, and the current ratio. We developed the OLAP database system by using three dimensions including the FREQUENCY dimension, and using the model of measuring SME size. Also we assessed past decisions on policy funding in the Small Business Policy Funds Support Project (2004-2007) by using the OLAP database system.

• Journal of Nutrition and Health
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• v.54 no.1
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• pp.67-75
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• 2021

Purpose: The theory of planned behavior (TPB) was used to investigate how the psychological constructs of attitude, subjective norms, and perceived behavioral control (PBC) affect the individual intention of behaviors in adults. Social support is also important in enabling the stability of healthy eating. This study examined the relationship between three major constructs of TPB as well as social support and the intention of healthy dietary behaviors in adults residing in Beijing, China using the extended TPB. Methods: The study questionnaire was based on previously validated items and an online survey was conducted from October to November 2020. Using a total of 244 Chinese adults in Beijing, multiple linear regression analysis was used to test the relationships between three major constructs of TPB as well as the social support and intention of healthy eating. Results: Among the three major constructs of TPB, subjective norms (p = 0.044) and PBC (p = 0.000) were significantly related to the behavioral intention of healthy eating (p = 0.000), and the model explained 76.6% of the variance of the behavioral intention from the three constructs of TPB included in the multiple linear regression model. The additional inclusion of social support to the model did not increase the explanatory power of the model to describe the behavioral intention of healthy eating. The subjective norms (p = 0.040) and PBC (p = 0.000) were still significant where social support did not explain the variance of the behavioral intention adequately. Conclusion: The subjective norms and PBC may be potential determinants of the behavioral intention of healthy eating in adults residing in Beijing, China. These study results can be used to promote healthy eating in Chinese adults living in urban areas. Large-scale intervention studies will be needed to determine if social norms and PBC predict the actual behaviors of healthy eating in Chinese adults.