• Korean Journal of Radiology
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• v.22 no.3
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• pp.476-488
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• 2021

Objective: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. Materials and Methods: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. Results: The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). Conclusion: The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.167-178
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• 2020

The purpose of this study is to examine the relationship between internal corporate, supplier, and customer integrations for domestic SMEs on non-financial and financial performance through SCM performance such as flexibility and reduction of uncertainties. To this end, data was collected on 286 SMEs in Korea, and the structural relationships between SCM integration level, SCM performances, and management performance were analyzed. As a result of the analysis, first, it was found that the SCM integration level had a significant positive effect on the flexibility and reduction of uncertainties, which are SCM performances. Second, the flexibility and reduction of uncertainties showed significantly positive effects on the non-financial performance of the companies, but did not directly affect the financial performance positively. Third, the non-financial performance was found to have a positive effect on the financial performance. In addition, the SCM integration level did not have a direct effect on the financial and non-financial performance, but it was found that it affected management performance by mediating the flexibility and reduction of uncertainties, which are SCM performances. That is, although the SCM integration level did not directly affect financial and non-financial performance, it was confirmed that it affects management performance by mediating SCM performances, flexibility and uncertainty reduction. In other words, it was confirmed that the SCM integration level directly or indirectly affects SCM performances and overall management performance. These results imply the necessity to focus on competency in the supply chain management area according to the SCM performance expected by SMEs, and the step by step approaches to the expected effects. In a situation where prior SCM related studies have not been able to present SCM performances and management performance of SMEs that are relatively lacking in their capital and SCM construction capabilities, the findings of this study could suggest the importance of SCM integration from the perspective of SMEs. In addition, from the viewpoint of SMEs, this study suggested that a sequential approach for performance measurement is required (SCM performance → management performance) in relation to the performance factors to be established through SCM.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2078-2087
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• 2010

This study conducted a chi-square test and a logistic regression analysis by not only using a investigation data of discharged patients with damages from 2004 to 2006, but also using a census and a research data on the actual condition from health care system. The result follows; First, the centralization of medical utilization of operation patients is becoming apparent, centrally the capital area. To improve this, a policy that can not only provide medical centers and sickbed, but also improve the quality of local medical treatments for the localization of medical treatments. Second, propelling localization policy of certain diseases for the localization of medical utilization is needed as the rate of non-residential of operation patients that have diseases of the eye and adnexa, or cancers is high. Third, a localization policy for patients with damages is needed as the rate of treatments in other regions of operation patients with industrial accidents is increasing day by day.

• Asian Spine Journal
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• v.12 no.6
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• pp.1085-1091
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• 2018

Study Design: Case-control. Purpose: To determine optimal cut-off value for body mass index (BMI) in predicting surgical success in patients with lumbar spinal canal stenosis (LSCS). Overview of Literature: BMI is an essential variable in the assessment of patients with LSCS. Methods: We conducted a prospective study with obese and non-obese LSCS surgical patients and analyzed data on age, sex, duration of symptoms, walking distance, morphologic grade of stenosis, BMI, postoperative complications, and functional disability. Obesity was defined as BMI of ${\geq}30kg/m^2$. Patients completed the Oswestry Disability Index (ODI) questionnaire before surgery and 2 years after surgery. Surgical success was defined as ${\geq}30%$ improvement from the baseline ODI score. Receiver operating characteristic (ROC) analysis was used to estimate the optimal cut-off values of BMI to predict surgical success. In addition, correlation was assessed between BMI and stenosis grade based on morphology as defined by Schizas and colleague in total, 189 patients were eligible to enter the study. Results: Mean age of patients was $61.5{\pm}9.6years$. Mean follow-up was $36{\pm}12months$. Most patients (88.4%) were classified with grades C (severe stenosis) and D (extreme stenosis). Post-surgical success was 85.7% at the 2-year follow-up. A weak correlation was observed between morphologic grade of stenosis and BMI. Rates of postoperative complications were similar between patients who were obese and those who were non-obese. Both cohorts had similar degree of improvement in the ODI at the 2-year followup. However, patients who were non-obese presented significantly higher surgical success than those who were obese. In ROC curve analysis, a cut-off value of ${\leq}29.1kg/m^2$ for BMI in patients with LSCS was suggestive of surgical success, with 81.1% sensitivity and 82.2% specificity (area under the curve, 0.857; 95% confidence interval, 0.788-0.927). Conclusion: This study showed that the BMI can be considered a parameter for predicting surgical success in patients with LSCS and can be useful in clinical practice.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.459-466
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• 2020

This study aimed to analyze characteristics and factors affecting of depression by households type of elderly with disabilities. Data were analyzed using chi-square test, logistic regression analyses according to the PSED. Data were 608 individuals. First, The one-person households group showed significantly female, non-capital, severe disorder, lower health status, lower economic level, higher help for daily life, higher experience of discrimination, lower satisfaction with relationship, lower participation in social activities. Second, Factors affecting depression included experience of discrimination, participation in social activities. Multi-person group included gender, occupied area, health status, economic level and one-person group were help for daily life. In order to reduce depression, it is necessary to reduce experience of discrimination, improve awareness of disability, health care, economic supports and so on. This study is meaningful in that it classifies households types. In the future, there is a need to longitudinal studies.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.449-453
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• 2009

The primary purpose of this study was to probe the relationship between negotiating efforts on leisure restriction and participation in leisure sports. This study has sampled for 26 days from 2009 February 9th to March 6th 250 adult at the age of 19 living in capital area using the Purposive Sampling Method. The study has then divided these samples into 3groups which are : public people, members of an association club, members of a club. To exclude non-participant of leisure sports, the researcher has visited the leisure sports facilities to sampling survey. The collected data were put into the SPSSWIN 15.0 program to be Factor Analysis, Reliability Analysis, Multiple Regression Analysis, Path Analysis. The results are: 1) Negotiating efforts on leisure sports affect the frequency of leisure sports participation: thus, the more effort on leisure activity and eagerness to change the more frequent the participation rate becomes. 2) Negotiating efforts on leisure sports affect the length of leisure sports participation. The means that the recharging through leisure activity and regulatory the intensity can extend the length of participation. 3) Negotiating efforts on leisure sports affect the intensity of participation. The more effort on leisure activity result in higher intensity of participation.

• The Journal of Fisheries Business Administration
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• v.33 no.1
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• pp.87-107
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• 2002

The sea of north-east Asia is biologically interrelated and one country's mishap in the management of fisheries could have a critical effect upon the other. Accordingly under the TAC system adopted by all the countries of Korea, Japan and China, the mismanagement o( trans-boundary fish stocks under the provisional fisheries agreement prior to the delimitation of EEZ could lead to the irrevocable depletion of fisheries resources in case of absence of close cooperation among the countries concerned. To tackle the problems above, it is necessary, from a short term perspective, to promote the combined efforts to do researches on fisheries resources, find ways to improve the transparency of fisheries management, adjust the fisheries management regulations of each country, standardize fishing gears and methods, and exchange fisheries-related statistics and data for socio-economic analysis and strengthen joint research activities for the mutual benefits. From a longer term prospective, regional fisheries organization need to be set up to oversee the whole area of north-east Asian sea. The organization as such could play a role in adjusting the conflicting interests of Korea, Japan and China, and efficiently manage the fisheries resources, which is complex and challenging in nature. In addition, unlike China, the historical fisheries relationship between Korea and Japan, spirit of reciprocity and the Article 62 of the United Nations Convention On the Law Of Sea need to be taken into account when seeking for fisheries cooperation between the two countries through the international specialization. In other words, the data obtained through the joint researches on the fisheries resources for the specific ocean along with such factors as capital, labor, fisheries technology and consumption of fish products could be used to assign the specific sector of fisheries to the country who has a comparative advantage, thus achieving the mutually benefiting results Up to the present, concerted efforts by Korea, Japan and China on the fisheries cooperation have been consistently made, but the results have yet to be materialized, It is also beyond doubt that governmental consultations among the countries should be made on a consistent basis, but non-governmental organizations' exchanges and related joint researches will more likely help bring about the desired fruition in a shorter time.

• International Area Studies Review
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• v.13 no.3
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• pp.213-238
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• 2009

This paper empirically examines effects of exporting on employment over the period 2000-2007 for Korean listed and non-listed manufacturing firms. The paper employs the dynamic panel model of labor demand and controls for simultaneity of the exports and real wages using a two step random effect Tobit-DPD (Dynamic Panel Data) procedure. Our empirical results suggest that surprisingly, there is no robust evidence for employment effects of exporting of Korea's large firms and small-medium sized firms during the sample period after Korean financial crisis. This implies that Korean exporting patterns have been changed in a way that exporting highly capital intensive goods leads to importing more intermediate goods and thus to countervailing the employment effects of exporting. This suggests that expansion of exporting by lowering exporting prices through the bilateral FTA might not be helpful to enhancing employment in Korea.

• 한국노년학
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• v.39 no.1
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• pp.145-167
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• 2019

The purpose of this study is to examine the solidarity types between parents-children and to verify whether the types of solidarity relationships are directly related to life satisfaction of older adults. To achieve this, 2,072 Korean elderlies from proportional stratified sample were participated, and the data was divided into urban and rural area where respondents live in. The Latent Profile Analysis(LPA), multiple regression analysis were mainly used for data analysis. The results of the study were as follows: The major findings are following. Patterns of parental bond among urban and rural elderly were classified into three(urban) and two(rural) patterns. For the effect of each parental bonding pattern on life satisfaction, positive effect of parental bond was found only in urban dwellers. In other words, for older people in rural areas, parental bond did not significantly affected on life satisfaction. However, elderly in rural area showed non-familial factor based relationship, such as social capital(community trust, social cohesion, and social support), was influenced their life satisfaction rather than relationship with children. Finding from the study highlight political and practical implications for improving life satisfaction for the elderly.