• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.355-383
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• 2019

This study is to investigate whether averting costs for wearing $anti-PM_{10}$ masks and using air purifiers at home to reduce exposure from $PM_{10}$ are influenced by subjective risk perceptions and/or objective $PM_{10}$ concentration levels, whose estimates will be used to measure the willingness to pay for $PM_{10}$ risk reduction. An empirical analysis was conducted on a sample of 1,224 respondents who participated in the web-based survey in the late October of 2017. As we reflect the potential endogeniety bias in the estimation of averting cost functions of using air purifiers, the coefficients of risk perception were differed by 6~7 times. Respondents. subjective risk perceptions were influenced by individuals' knowledge, attitudes and demographic variables, as well as the levels of $PM_{10}$ concentrations in their residential region. The marginal willingness to pay for risk reductions at the mean levels of their risk perceptions were measured at 1,000 won per month from wearing $anti-PM_{10}$ masks and 6,000 won for using air purifiers respectively.

• Korean Journal of Social Welfare Studies
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• v.41 no.3
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• pp.213-239
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• 2010

The purpose of this research is to explore the explanatory factors of preference for the universality of the welfare system at the time of expanding the welfare system. In particular, considering endogenous problem that may occur in the process of analyzing the causal relationship between subjective perception and preference for welfare policy, the 2SLS regression analysis using instrument variables was attempted in this research. The key findings of this research were as follow. First, the groups who are opposed to the welfare state expansion, for example high income earners, low risk group, and employer/self-employer, prefer the more universal welfare systems. Second, the negative perception of welfare policy and recipients, which is stronger in older generation who experienced a much longer period of industrializaion, have a negative effect on preference for the universal welfare system. Last, we find that the endogenous problem arise in this research and distort the estimated regression coefficients. Therefore, subsequent studies must be mindful of this problem when they explain attitudes with attitudes.

• Journal of Music and Human Behavior
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• v.10 no.2
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• pp.1-33
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• 2013

The aim of this study was to systematically review the latest clinical trials in music medicine and medical music therapy for pediatric patients. Thirteen databases were searched to obtain randomized controlled/crossover design studies published between the year 2000 and 2012 in English language. Out of 1012 articles retrieved in the initial search, fifteen studies were identified based on an exclusion criteria. Overall, selected articles involved children 1 month to 18 years, sample size of 11 to 150, and total participants of 987. Studies were classified and compared as music medicine or music therapy studies through a systematic synthesis assessing general characteristics, methodological quality, measured outcomes, types of interventions and the study results. Seven music medicine and eight music therapy studies measured seven dependent variables using thirty-six different measurement tools with a large heterogeneity in the selection, type, and method of music interventions. Evaluation of the methodological quality revealed that many studies did not provide a full report of the research method, and did not meet some or most methodological standards, such as randomization, allocation concealment, double or partial blinding, and intention to treat analysis. Although overall research results were positive if not significant, poor methodological quality and heterogeneity in design and intervention strategies raise the question of research bias and trustworthiness issues. The systematic review concluded that music may have a valuable clinical effect in addressing the physical and psychosocial needs of hospitalized children, although more rigorous, homogeneous and replicable studies are greatly needed.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.203-208
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• 2005

We propose an algorithmic approach for handling linguistic values defined in the same linguistic variable. Using the proposed approach, we can explicitly capture the differences of individuals' subjectivity with respect to linguistic values defined in the same linguistic variable. The proposed approach can be employed as a useful tool for discovering hidden relationship among linguistic values defined in the same linguistic variable. Consequently, it provides a basis for improving the precision of knowledge acquisition in the development of fuzzy systems including fuzzy expert systems, fuzzy decision tree, fuzzy cognitive map, ok. In this paper, we apply the proposed approach to a collective linguistic assessment among multiple experts.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.247-253
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• 2017

Feature selection or variable selection is a data mining scheme for selecting highly relevant features with target concept from high dimensional data. It decreases dimensionality of data, and makes it easy to analyze clusters or classification. A feature selection scheme requires an evaluation function. Most of current evaluation functions are based on statistics or information theory, and they can evaluate only for single feature (one-dimensional data). However, features have interactions between them, and require evaluation function for multi-dimensional data for efficient feature selection. In this study, we propose modification of FSDD evaluation function for utilizing evaluation of multiple features using extended distance function. Original FSDD is just possible for single feature evaluation. Proposed approach may be expected to be applied on other single feature evaluation method.

• Tunnel and Underground Space
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• v.24 no.2
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• pp.155-165
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• 2014

Multiple thermal energy storage (TES) caverns can be used for storing thermal energy on a large scale and for a high-aspect-ratio heat storage design to provide good thermal performance. It may also be necessary to consider the use of multiple caverns with a reduced length when a single, long tunnel-shaped cavern is not suitable for connection to aboveground heat production and injection equipments. When using multiple TES caverns, the separation distance between the caverns is one of the significant factors that should be considered in the design of storage space, and the optimal separation distance should be determined based on a quantitative stability criterion. In this paper, we described a numerical approach for determining the optimal separation distance between multiple caverns for large-scale TES utilization. For reliable stability evaluation of multiple caverns, we employed a probabilistic method which can quantitatively take into account the uncertainty of input parameters by probability distributions, unlike conventional deterministic approaches. The present approach was applied to the design of a conceptual TES model to store hot water for district heating. The probabilistic stability results of this application demonstrated that the approach in our work can be effectively used as a decision-making tool to determine the optimal separation distance between multiple caverns. In addition, the probabilistic results were compared to those obtained through a deterministic analysis, and the comparison results suggested that care should taken in selecting the acceptable level of stability when using deterministic approaches.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.8
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• pp.3866-3875
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• 2013

The purpose of this study was to investigate the degree of mental health among nursing students in Korea and identify factors influencing the tendency to mental health. A self-report survey was conducted with South Korean nursing students who were from 19 to 23 years old. 269 nursing students were included in the study. The instruments utilized in the study were Symptom Checklist-90-Revision, Stress and multidimensional coping. Data were analyzed using descriptive statistics, Pearson correlation and Simultaneous multiple regression with SPSS WIN 20.0. The average mental health score of the participants was 0.57. Significant predictors for mental health 'College-level stress', 'Self-criticism', 'Passive withdrawal', 'Nursing satisfaction' and 'Health state'. The study findings suggest that nursing students require special concern regarding the risk of mental health. Multi and interdisciplinary mental health promotion program will enhance the mental health of nursing students.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of the Korean Geotechnical Society
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• v.27 no.3
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• pp.75-83
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• 2011

Emerging issues related with fully coupled Thermo-Hydro-Mechanical (THM) behavior of unsaturated soil demand the development of a numerical tool in diverse geo-mechanical and geo-environmental areas. This paper presents general governing equations for coupled THM processes in unsaturated porous media. Coupled partial differential equations are derived from three mass balances equations (solid, water, and air), energy balance equation, and force equilibrium equation. With Galerkin formulation and time integration of these governing equations, finite element code is developed to find nonlinear solution of four main variables (displacement-u, gas pressure-$P_g$), liquid pressure-$P_1$), and temperature-T) using Newton's iterative scheme. Three cases of numerical simulations are conducted and discussed: one-dimensional drainage experiments (u-$P_g-P_1$), thermal consolidation (u-$P_1$-T), and effect of pile on surrounding soil due to surface temperature variation (u-$P_1$-T).

• Korean Journal of Psychosomatic Medicine
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• v.27 no.1
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• pp.69-76
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• 2019

Objectives : The purpose of this study was to examine the prevalence and clinical characteristics of nonsuicidal self-injury (NSSI), and its association with suicide attempts among clinically depressed adolescents in Korea. Methods : In total, 113 depressed adolescents aged 12-18 years in South Korea were enrolled in this study. We assessed sociodemographic and clinical characteristics including suicidality and non-suicidal self-injury (NSSI) using various self-reported scales and semi-structured interview for diagnosis of psychiatric disorders. Demographic and clinical characteristics of the subjects were compared between NSSI and non-NSSI groups. We examined significant predictors of suicide attempts using logistic regression analysis. Results : Among 113 depressed participants, 48 (42.1%) adolescents were classified into the NSSI group. In the NSSI group, there were significantly more females, showed higher depression, higher state-anxiety, and more suicide ideation. The most predictive factors of suicide attempts were history of NSSI, observed suicide/NSSI behaviors of their family or friends, and total state anxiety score. Conclusions : NSSI is more common problem among clinically depressed adolescents and history of NSSI is a significant predictor of present suicide attempts. To include the assessment of NSSI for clinically depressed adolescent may be crucial for intervention programs for high risk adolescents of suicide in Korea.