• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.78-90
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• 2019

The purpose of this study is to explore the direction of developing policies for parks and green spaces for inclusive urban planning and regeneration. By reviewing the status, budget, and laws pertaining to urban parks in Korea, as well as assessing the inclusivity of urban parks, this study revealed the problems and limitations in Korea as follows. First, the urban park system, which takes into account indicators such as park area per capita and green space ratio, is focused only on quantitative expansion. Second, the distribution of urban parks is unequal; hence, the higher the number of vulnerable residents, the lower the quality of urban parks and green spaces. Moreover, this study focused on the UK central government, along with the five local governments, including London, Edinburgh, Cardiff, Belfast, and Liverpool. Through an analysis of the contexts and contents establishing UK park and green space policies that can reduce socioeconomic inequalities while at the same time increase inclusiveness. This study discovered the following. The government's awareness of the necessity of tackling socioeconomic inequalities to make an inclusive society, the change in the urban regeneration policies from physical redevelopment to neighborhood renewal, and the survey and research on the correlation of parks and green spaces, inequality, health, and well-being provided the background for policy establishment. As a result, the creation of an inclusive society has been reflected in the stated goals of the UK's national plan and the strategies for park and green space supply and qualitative improvement. Deprived areas and vulnerable groups have been included in many local governments' park and green space policies. Also, tools for analyzing deficiencies in parks and methods for examining the qualitative evaluation of parks were developed. Besides, for the sustainability of each project, various funding programs have been set up, such as raising funds and fund-matching schemes. Different ways of supporting partnerships have been arranged, such as the establishment of collaborative bodies for government organizations, allowing for the participation of private organizations. The study results suggested five policy schemes, including conducting research on inequality and inclusiveness for parks and green spaces, developing strategies for improving the quality of park services, identifying tools for analyzing policy areas, developing park project models for urban regeneration, and building partnerships and establishing support systems.

• Journal of Nutrition and Health
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• v.54 no.6
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• pp.644-663
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• 2021

Purpose: This study was conducted to evaluate the safety of iodine intake based on ingestion levels and urinary iodine excretion of women of childbearing age (15-45 years old) using data from the 2013-2015 Korea National Health and Nutrition Examination Survey. Methods: Iodine intake was calculated using the 24 hours dietary recall method and urinary iodine excretion. The iodine nutrition database for the analysis of dietary iodine intake was constructed using the food composition database of the Rural Development Administration (RDA), the Korean Nutrition Society (KNS), the Ministries of Food and Drug Safety, China and, Japan. The World Health Organization (WHO) evaluation criteria and hazard quotient (HQ) calculated using biomonitoring equivalents (BE) were applied to evaluate the safety of the iodine intake. Results: Of the study subjects, 15.22% had a urinary iodine concentration level of less than 100 ㎍/L, which was diagnosed as deficient, and 48.16% had an excessive iodine concentration of over 300 ㎍/L. Urinary iodine concentration was 878.71 ㎍/L, iodine/creatinine was 589.00 ㎍/g, and iodine/creatinine was significantly higher at the age of 30-45 years. The dietary iodine intake was 273.47 ㎍/day, and the iodine intake calculated from the urinary iodine excretion was 1,198.10 ㎍/day. Foods with a high contribution to iodine intake were vegetables, seafood, seaweed and processed foods. The HQ was 1.665 when the urinary iodine content was > 1,000 ㎍/L. Conclusion: The results of this study implicate that the urinary iodine concentration, rather than the dietary iodine intake, is more appropriate to evaluate the iodine status under the current situation that a comprehensive iodine database for Koreans has not been established.

• Health Policy and Management
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• v.34 no.2
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• pp.163-177
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• 2024

Background: This study aims to examine changes in fraudulent claim counts and total reimbursements before and after enhancements in counterfeit claim controls and monitoring of provider claim patterns under the "Proactive self-audit pilot program of fraudulent claims." Methods: This study used the claims data and hospital information (July 2021-February 2022) of the Health Insurance Review and Assessment Service. The data was collected from 1,129 hospitals assigned to the pilot program, selected from the providers who filed a claim for reimbursement for intravenous injections. Paired and independent t-tests, along with regression analysis, were utilized to analyze changing patterns and factors influencing claim behaviors. Results: This program led to a reduction in the number of fraudulent claims and the total amount of reimbursements across all levels of hospitals in the experimental groups (except for physicians below 40 years old). In the control group, general hospitals and hospitals demonstrated some significant decreases based on the duration since opening, while clinics showed significant reductions in specified subjects. Additionally, a notable increase was observed among male physicians over the age of 50 years. Overall, claims and reimbursements significantly declined after the intervention. Furthermore, a positive correlation was found between hospital opening duration and claim numbers, suggesting longer-established hospitals were more likely to file claims. Conclusion: The results indicate that the pilot program successfully encouraged providers to autonomously minimize fraudulent claims. Therefore, it is advised to extend further support, including promotional activities, training, seminars, and continuous monitoring, to nonparticipating hospitals to facilitate independent improvements in their claim practices.

• 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 Intelligence and Information Systems
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• v.24 no.3
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• pp.177-198
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• 2018

Recently, as economic property it has become necessary to acquire and utilize the framework for water resource measurement and performance management as the property of water resources changes to hold "public property". To date, the evaluation of water technology has been carried out by feasibility study analysis or technology assessment based on net present value (NPV) or benefit-to-cost (B/C) effect, however it is not yet systemized in terms of valuation models to objectively assess an economic value of technology-based business to receive diffusion and feedback of research outcomes. Therefore, K-water (known as a government-supported public company in Korea) company feels the necessity to establish a technology valuation framework suitable for technical characteristics of water resources fields in charge and verify an exemplified case applied to the technology. The K-water evaluation technology applied to this study, as a public interest goods, can be used as a tool to measure the value and achievement contributed to society and to manage them. Therefore, by calculating the value in which the subject technology contributed to the entire society as a public resource, we make use of it as a basis information for the advertising medium of performance on the influence effect of the benefits or the necessity of cost input, and then secure the legitimacy for large-scale R&D cost input in terms of the characteristics of public technology. Hence, K-water company, one of the public corporation in Korea which deals with public goods of 'water resources', will be able to establish a commercialization strategy for business operation and prepare for a basis for the performance calculation of input R&D cost. In this study, K-water has developed a web-based technology valuation model for public interest type water resources based on the technology evaluation system that is suitable for the characteristics of a technology in water resources fields. In particular, by utilizing the evaluation methodology of the Institute of Advanced Industrial Science and Technology (AIST) in Japan to match the expense items to the expense accounts based on the related benefit items, we proposed the so-called 'K-water's proprietary model' which involves the 'cost-benefit' approach and the FCF (Free Cash Flow), and ultimately led to build a pipeline on the K-water research performance management system and then verify the practical case of a technology related to "desalination". We analyze the embedded design logic and evaluation process of web-based valuation system that reflects characteristics of water resources technology, reference information and database(D/B)-associated logic for each model to calculate public interest-based and profit-based technology values in technology integrated management system. We review the hybrid evaluation module that reflects the quantitative index of the qualitative evaluation indices reflecting the unique characteristics of water resources and the visualized user-interface (UI) of the actual web-based evaluation, which both are appended for calculating the business value based on financial data to the existing web-based technology valuation systems in other fields. K-water's technology valuation model is evaluated by distinguishing between public-interest type and profitable-type water technology. First, evaluation modules in profit-type technology valuation model are designed based on 'profitability of technology'. For example, the technology inventory K-water holds has a number of profit-oriented technologies such as water treatment membranes. On the other hand, the public interest-type technology valuation is designed to evaluate the public-interest oriented technology such as the dam, which reflects the characteristics of public benefits and costs. In order to examine the appropriateness of the cost-benefit based public utility valuation model (i.e. K-water specific technology valuation model) presented in this study, we applied to practical cases from calculation of benefit-to-cost analysis on water resource technology with 20 years of lifetime. In future we will additionally conduct verifying the K-water public utility-based valuation model by each business model which reflects various business environmental characteristics.