• Computers and Concrete
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• v.19 no.5
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• pp.501-507
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• 2017

The predictive utility of a damage model depends heavily on its particular choice of a damage variable, which serves as a macroscopic approximation in describing the underlying micromechanical processes of microdefects. In the case of spatially perfectly randomly distributed microcracks or microvoids in all directions, isotropic damage model is an appropriate choice, and scalar damage variables were widely used for isotropic or one-dimensional phenomenological damage models. The simplicity of a scalar damage representation is indeed very attractive. However, a scalar damage model is of somewhat limited use in practice. In order to entirely characterize the isotropic damage behaviors of damaged materials in multidimensional space, a system theory of isotropic double scalar damage variables, including the expressions of specific damage energy release rate, the coupled constitutive equations corresponding to damage, the conditions of admissibility for two scalar damage effective tensors within the framework of the thermodynamics of irreversible processes, was provided and analyzed in this study. Compared with the former studies, the theoretical formulations of double scalar damage variables in this study are given in the form of matrix, which has many features such as simpleness, directness, convenience and programmable characteristics. It is worth mentioning that the above-mentioned theoretical formulations are only logically reasonable. Owing to the limitations of time, conditions, funds, etc. they should be subject to multifaceted experiments before their innovative significance can be fully verified. The current level of research can be regarded as an exploratory attempt in this field.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.7-14
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• 2006

Nowadays, many people are deeply interested in health and well-being. Ministry of Health and Welfare showed the results of evaluation of medical service of seventy eight general hospitals in 2005. The number of its medical service variables was eighteen and the evaluation was carried out in eight regions on a national scale. Our study used the result data of the evaluation and presented the descriptive and inferential research findings. Thus, relations between the eight regions in Korea and 18 variables were studied, using ANOVA, etc., the significant results were found, and through regression analysis we estimated slope parameters of significant variables and could let the persons concerned know which variables were more important or less important and suggested which variables should specially be strengthened for higher ranking of the overall evaluation and for acquisition of the excellent grade, A.

• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.327-338
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• 2001

This paper presents the results of a structural analysis to determine design variables such as the inner basket array type, and thicknesses of the outer shell, and lid and bottom of a spent nuclear fuel disposal canister. The canister construction type introduced here is a solid structure with a cast iron insert and a corrosion resistant overpack, which is designed for the spent nuclear fuel disposal in a deep repository in the crystalline bedrock, entailing an evenly distributed load of hydrostatic pressure from the groundwater and high swelling pressure from the bentonite buffer. Hence, the canister must be designed to withstand these high pressure loads. Many design variables may affect the structural strength of the canister. In this study, among those variables, the array type of inner baskets and thicknesses of outer shell and lid and bottom are attempted to be determined through a linear structural analysis. Canister types studied hear are one for the pressurized water reactor (PWR) fuel and another for the Canadian deuterium and uranium reactor (CANDU) fuel.

• Journal of Korean Clinical Nursing Research
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• v.23 no.1
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• pp.100-109
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• 2017

Purpose: Nurses' resilience plays an important role in overcoming the challenges that nurses often encounter at clinic, and many factors have been examined which influence on nurses' resilience levels. Through this study, those factors were systematically searched and quantitatively synthesized. Methods: In order to find relevant studies, both English and Korean academic databases were searched, and, finally, a total of 33 articles were identified and included in this analysis. Results: The effect size on the protective variables was large and that of the risk variables was medium. In the protective variable group, the job variable group showed a larger effect size compared to the organizational variable group. Among the protective variables, compassion satisfaction showed the highest contribution on enhancing the resilience level of nurses. In the risk variable group, the personal variable group showed the highest effect size, which was followed by the organizational and job variables. Among the risk variables, the personal stress response showed the highest contribution to decreasing the level of resilience of nurses. Conclusion: This study provides a meaningful data for future studies in terms of developing evidence-based interventions to enhance the levels of resilience among Korean nurses.

• Journal of the Ergonomics Society of Korea
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• v.28 no.4
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• pp.61-69
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• 2009

With increasing needs for affective design, it became an essential part in a product development process to look up quantitative ergonomic data that reflects customers' preferences on design factors in various products. This study looked at wide LCD monitors and analyzed customers' affective preferences regarding monitor's bezel frame size and shape. The monitor's bezel frame depth, size and ratio were selected as independent variables among many design parameters. As dependent variables, customer's subjective preferences were measured. A statistical analysis revealed that monitor's bezel frame depth, size and ratio had significant effects on customer's preferences. Also, it was possible to find a different tendency on affective variables and their levels for 19" and 24" wide LCD monitors. In general, experiments revealed that customers reacted more sensitively in 24" wide LCD monitors to all variables. In 19" wide LCD monitors, only the lower frame bezel size had a significant effect, otherwise, lower, upper and side frame bezels appeared to be effective variables in 24" monitors. In order to reflect customer's affective preferences to new design of wide LCD monitors, this study is expected to provide quantitative ergonomic data and guidelines for the design of wide LCD monitor's bezel frame depth and size.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.171-179
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• 2021

In the international markets, financial variables can be volatile and may affect each other, especially in the crisis times. COVID-19, which began in China in 2019 and spread to many countries of the world, created a crisis not only in the global health system but also in the international financial markets and economy. The purpose of this study is to analyze the contagious effect of the COVID-19 pandemic on the volatility of selected financial variables such as Bitcoin, gold, oil price, and exchange rates and the connections between the volatilities of these variables during the pandemic. For this aim, we use the ARMA-EGARCH model to measure the impact of volatility and shocks. In other words, it is aimed to measure whether the impact of the shock on the financial variables of the contagiousness of the epidemic is also transmitted to the markets. The data was collected from secondary and daily data from September 2th 2019 to December 20th, 2020. It can be said that the findings obtained have statistically significant effects on the conditional variability of the variables. Therefore, there are findings that the shocks in the market are contaminated with each other.

• Proceedings of the IEEK Conference
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• 2004.06b
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• pp.481-484
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• 2004

To make production and equipment investment plans in semiconductor Line, implementation of many variables is needed. But these factors could bring many changes and the result is hard to predict. Because prediction is hard, it is hard to make a standard. So this project established Semiconductor production plans using LP Algorithm to satisfy all the conditions from the factors and came up with thesis on reasonable and standardized process.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.96-113
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• 1992

A time-varying nonhomogeneous poisson process approximation of the nonexponential stochastic Lanchester model is defined and evaluated over a range of combat parameters including initial force sizes. breakpoints. and interkilling random variables. The proposed approximation is far excellent and takes much less CPU time than the existing models. The sensitivity analysis was peformed to evaluate the efficiency of the proposed model and three recommended factors are suggested to guide the combat operators.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.57-73
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• 2021

The purpose of this study is to analyze the diffusion period and productivity of smartwork in an organization. Firms are increasingly interested in smartwork for non contact work and working from home because of the corona 19. The smartwork is a new technology that changes face-to-face work in an organization. It helps the work of individuals and organizations regardless of time and place. The theoretical background describes the complexity, system thinking, diffusion theory, smart work, organizational resistance, and productivity. This study analyzes the diffusion period and productivity of smart work through business simulation techniques. A simulation study progresses four stages. There are problem definition, hypothesis establishment and causal loop diagram, model construction and verification, and policy evaluation. The simulation models contain an individual's resistance variables organizational investment and leadership variables related to the operation of smartwork. The organizational investment variables include organizational culture, legal system, implement systems and technology investment. The individual resistance variables include cognitive, attitude, structure and technological resistance. The leadership includes leadership interest variables and performance linkage variables. The simulation executed the changes of a people number adopting smart work and the organizational productivity monthly. As a result of the simulation, many organization members have accepted the smart work innovation after 20 months. The organizational productivity through smart work showed very high value after 16 months. In scenario analysis, the individuals' awareness and attitude resistance showed very important variables to productivity and a personal change of smart work adoption. Meanwhile, The organizational investment showed that the high driving-force increased not productivity and the low driving-force showed decreased low productivity. Also, leadership variables showed a powerful driver for changing smart work productivity. The implication of the study has suggested extending complexity, diffusion theory and organization resistance theory based on simulation methods.