• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.47-66
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• 2016

Once it has been built, a container terminal is impossible to move to another location. It is hard to rectify wrong decisions in a container terminal. This highlights the importance of decision making for a container terminal. The port management about a container terminal has developed from a cargo interface location between sea and land transport, to the standardization of information and procedures due to globalization among global shipping and terminal operators. This research focuses on the current states of market and management for global container terminal operators by investigating up-to-date data for them. The current market states for global container terminal operators are analyzed by using by Herfindahl-Hirschman Index. The analyses of current management states for global container terminal operators are divided into profitability analysis, activity analysis, and bankruptcy risk analysis. Finally, global container terminal operators are clustered into three groups by the current management states.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.609-621
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• 2022

Purpose: Natural Disaster has well formed framework more than social disaster, because natural disaster is controlled by one department, such as MOIS, but social disaster is distributed. This study is on the design of the integrated service platform for the social diaster data. and then, apply to the local governments. Method: Firstly, we design DB templates for the incident cases considering the incident investigation reports. For the risk management, life-damage oriented social disaster risk assessment is defined. In case of the real-time incident data from NDMS, AI system provides the prediction information in the life damage and the cause of the incident. Result: We design the structured and unstructured incident data management system, and design the integrated social disaster and safety incident management system. Conclusion: The integrated social disaster and safety incident management system may be used in the local governments

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.261-266
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• 2007

In this research, we studied an estimating method in client's sight to estimate the total construction cost which is based on the historical cost data at the early stage of the office buildings as a public phase. It is very difficult to analyze the estimation accurately and logically. When a client estimates a project, he/she has to consider there are many issues at the planning step, according as office buildings become gradually diversified as well as their roles continuously extended. Therefore, those are usually make problems for wasting the budget in accordance with the cost estimation errors. Moreover, many kinds of public construction projects, especially such as school, office, sports complex, and the others, have been invested the private finances defined as BTL(Build Transfer Lease) method that are required to manage the detailed process more strictly from initial planning. In order to make an effective planning, the long-term users amount and the building life cycle at the beginning of project should be considered previously and then it may enable to achieve an appropriate project plan. But actually considering overall variables in a building planning is impossible. Accordingly, suggesting a regression model based on the historical cost data from many similar types of office building to support client's role known as estimating the total cost at the early stage. And then performing the test against the proposed model to research the reasonability as using the historical cost data of Japan office buildings.

• Journal of Distribution Science
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• v.12 no.11
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• pp.77-83
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• 2014

Purpose - The National Agricultural Cooperative Federation of Korea and National Fisheries Cooperative Federation of Korea have prosecuted both financial and retail businesses. As cooperatives are public institutions and receive government support, their sound management is required by the Financial Supervisory Service in Korea. This is mainly managed by CAEL, which is changed by CAMEL. However, NFFC's business section, managing the finance and retail businesses, is unified and evaluated; the CAEL model has an insufficient classification to evaluate the retail industry. First, there is discrimination power as regards CAEL. Although the retail business sector union can receive a higher rating on a CAEL model, defaults have often been reported. Therefore, a default prediction model is needed to support a CAEL model. As we have the default prediction model using a subdivision of indexes and statistical methods, it can be useful to have a prevention function through the estimation of the retail sector's default probability. Second, separating the difference between the finance and retail business sectors is necessary. Their businesses have different characteristics. Based on various management indexes that have been systematically managed by the National Fisheries Cooperative Federation of Korea, our model predicts retail default, and is better than the CAEL model in its failure prediction because it has various discriminative financial ratios reflecting the retail industry situation. Research design, data, and methodology - The model to predict retail default was presented using logistic analysis. To develop the predictive model, we use the retail financial statements of the NFCF. We consider 93 unions each year from 2006 to 2012 to select confident management indexes. We also adapted the statistical power analysis that is a t-test, logit analysis, AR (accuracy ratio), and AUROC (Area Under Receiver Operating Characteristic) analysis. Finally, through the multivariate logistic model, we show that it is excellent in its discrimination power and higher in its hit ratio for default prediction. We also evaluate its usefulness. Results - The statistical power analysis using the AR (AUROC) method on the short term model shows that the logistic model has excellent discrimination power, with 84.6%. Further, it is higher in its hit ratio for failure (prediction) of total model, at 94%, indicating that it is temporally stable and useful for evaluating the management status of retail institutions. Conclusions - This model is useful for evaluating the management status of retail union institutions. First, subdividing CAEL evaluation is required. The existing CAEL evaluation is underdeveloped, and discrimination power falls. Second, efforts to develop a varied and rational management index are continuously required. An index reflecting retail industry characteristics needs to be developed. However, extending this study will need the following. First, it will require a complementary default model reflecting size differences. Second, in the case of small and medium retail, it will need non-financial information. Therefore, it will be a hybrid default model reflecting financial and non-financial information.

• The Journal of Industrial Distribution & Business
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• v.8 no.4
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• pp.47-52
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• 2017

Purpose - This current study will investigate the average financial ratio of top and failed five-star hotels in the Jeju area. A total of 14 financial ratio variables are utilized. This study aims to; first, assess financial ratio of the first-class hotels in Jeju to establishing variables, second, develop distress prediction model for the first-class hotels in Jeju district by using logit analysis and third, evaluate distress prediction capacity for the first-class hotels in Jeju district by using logit analysis. Research design, data, and methodology - The sample was collected from year 2015 and 14 financial ratios of 12 first-class hotels in Jeju district. The results from the samples were analyzed by t-test, and the independent variables were chosen. This was an empirical study where the distress prediction model was evaluated by logit analysis. This current research has focused on critically analyzing and differentiating between the top and failed hotels in the Jeju area by utilizing the 14 financial ratio variables. Results - The verification result of the accuracy estimated by logit analysis has shown to indicate that the distress prediction model's distress prediction capacity was 83.3%. In order to extract the factors that differentiated the top hotels in the Jeju area from the failed hotels among the 14 chosen, the analysis of t-black was utilized by independent variables. Logit analysis was also used in this study. As a result, it was observed that 5 variables were statistically significant and are included in the logit analysis for discernment of top and failed hotels in the Jeju area. Conclusions - The distress prediction press' prediction capability was compared in this research analysis. The distress prediction press prediction capability was shown to range from 75-85% by logit analysis from a previous study. In this current research, the study's prediction capacity was shown to be 83.33%. It was considered a high number and was found to belong to the range of the previous study's prediction capacity range. From a practical perspective, the capacity of the assessment of the distress prediction model in the top and failed hotels in the Jeju area was considered to be a prominent factor in applications of future hotel appraisal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.126-134
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• 2016

The aim of this cross sectional study was to examine the prevalence of sarcopenia in association with ADL, nutritional status and depression among community dwelling elderly women. The study subjects were 90 elderly women, 65 years and over, who were living in the communities of B and D metropolitan cities from May to July, 2014. The measurements were anthropometric measures, The mini-nutritional assessment instrument (MNA), ADL, IADL, MMSE, and SGDS-K were used. The mean age of the subjects was 74.7(8.22), the prevalence of sarcopenia of this population was 37.8%, almost none of them (94.4%) required assistance in ADL, 15.6% had a risk of undernutrition, and 12.2% had the symptom of depression. The sarcopenic subjects were characterized as low income, low education, living alone, and had more co-morbidity than those of the non-sarcopenic subjects. The sarcopenic subjects were undernourished, and had higher depression scores (SGDS-K), but not in the ADL, than those of the non-sarcopenic subjects. The calf and thigh circumferences, and cognitive ability were the best predictors of sarcopenia, In conclusion, low calf and thigh circumferences and low cognitive ability will increase the risk of sarcopenia in those 65 and over in community dwelling facilities and those three predictors will be useful in the early detection of sarcopenia in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.368-368
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• 2020

최근 초대형화 되어 나타나고 있는 이상홍수와 지진 등에 의한 저수지 붕괴와 같은 대규모 비상상황 발생으로 하류지역 주민의 생명과 재산의 피해가 발생하고 있다. 국내의 경우 1996년 이후로 지속적으로 발생하고 있는 이상홍수로 인해 1998년에는 40개,1999년에는 5개의 소규모 저수지가 붕괴되었으며 최근 2013년과 2014년에도 저수지가 붕괴되는 상황이 발생했다. 댐붕괴의 원인은 구조물의 자연적 노화, 극심한 강우나 홍수, 지진, 제체전도, 파이핑, 침윤발생, 월류 및 파랑 등에 의한 자연적 상황 등이 요인이 될 수 있으며, 시공결함, 사고 또는 전쟁과 같은 인위적인 요인으로 발생할 수도 있다. 과거에 설계 및 시공기술이 부족하였거나 경제적인 이유로 부실하게 건설되어 있는 댐이 세계적으로 산재되어 있어 잠재적인 위험을 상당수 내재하고 있는 실정이다. 본연구는 댐의 점진적인 파괴에 의해 발생하는 유출수문곡선을 구하고 파괴의 성질을 예측 및 홍수파를 수리학적으로 추적하기위해 BREACH 모형과 DAMBRK 모형을 사용했으며 극한홍수(PMF)조건과와 최대지진발생(MCE)조건을 적용하여 원주시 관내 저수지 붕괴 모의 시나리오를 구축했다. 저수지 붕괴에 따른 유출수문곡선을 유도하기 위해서 본 연구에서는 기존의 EAP보고서 자료를 참고하여 붕괴지속시간, 붕괴부 평균폭, 붕괴부 측벽면 경사의 변화에 따라 다양한 모의를 수행함으로써 발생되는 붕괴부 유량 수문곡선을 도출하여 각각의 조건들이 붕괴파 형성에 미치는 영향에 대한 분석을 실시하였다. 그 결과 저수지의 붕괴시 첨두유출량에 민감한 영향을 주는 인자는 붕괴지속시간과, 붕괴부 평균폭으로서 이들 값이 붕괴유출량 변화에 많은 영향을 주는 것으로 나타났다. 최대지진발생(MCE)조건 해석결과 홍수류의 범람으로 인해 홍수파가 하류측으로 진행할수록 완만히 감소하며, 하천 중·상류부 인근 제내지로 홍수류의 범람이 발생하는 것으로 검토되었으며, 극한홍수(PMF)조건 해석결과 최대지진발생(MCE)조건과 같이 홍수파가 하류측으로 진행할수록 완만히 감소하는 특성을 보이며, 하천 전체 구간에서 인근제내지로 홍수류의 범람이 발생하는 것으로 검토되었다. 본 연구는 침수구역 피해규모 산정 및 비상대처계획도를 작성시 기초데이터가 되어 상황별 피해예상지역에 대해 응급행동요령, 주민대피계획비상대처계획을 수립하여 지역 주민생활에 안정을 기여하고자 한다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.479-493
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• 2023

The need for safety management has arisen due to the increasing number of years of operated underground structures, such as tunnels and utility tunnels, and accidents caused by those aging infrastructures. However, in the case of privately managed underground utility ducts, there is a lack of detailed guidelines for facility safety and maintenance, resulting in inadequate safety management. Furthermore, the absence of basic design information and the limited space for safety assessments make applying currently used non-destructive testing methods challenging. Therefore, this study suggests non-destructive inspection methods using ultrasonic and impact-echo techniques to assess the quality of underground structures. Thickness, presence of rebars, depth of rebars, and the presence and depth of internal defects are assessed to provide fundamental data for the safety assessment of box-type general underground structures. To validate the proposed methodology, different conditions of concrete specimens are designed and cured to simulate actual field conditions. Applying ultrasonic and impact signals and collecting data through multi-channel accelerometers determine the thickness of the simulated specimens, the depth of embedded rebar, and the extent of defects. The predicted results are well agreed upon compared with actual measurements. The proposed methodology is expected to contribute to developing safety diagnostic methods applicable to general underground structures in practical field conditions.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.558-568
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• 2021

Poor supervision and tourism activities have resulted in forest degradation in islands in Korea. Since the southern coastal region of the Korean peninsula was originally dominated by warm-temperate evergreen broad-leaved forests, it is desirable to restore forests in this region to their original vegetation. In this study, we identified suitable areas to be restored as evergreen broad-leaved forests by analyzing the environmental factors of existing evergreen broad-leaved forests in the islands of Jeollanam-do. We classified forest lands in the study area into six vegetation types from Sentinel-2 satellite images using a deep learning algorithm and analyzed the tolerance ranges of existing evergreen broad-leaved forests by measuring the locational, topographic, and climatic attributes of the classified vegetation types. Results showed that evergreen broad-leaved forests were distributed more in areas with a high altitudes and steep slope, where human intervention was relatively low. The human intervention has led to a higher distribution of evergreen broad-leaved forests in areas with lower annual average temperature, which was an unexpected but understandable result because an area with higher altitude has a lower temperature. Of the environmental factors, latitude and average temperature in the coldest month (January) were relatively less contaminated by the effects of human intervention, thus enabling the identification of suitable restoration areas of the evergreen broad-leaved forests. The tolerance range analysis of evergreen broad-leaved forests showed that they mainly grew in areas south of the latitude of 34.7° and a monthly average temperature of 1.7℃ or higher in the coldest month. Therefore, we predicted the areas meeting these criteria to be suitable for restoring evergreen broad-leaved forests. The suitable areas cover 614.5 km², which occupies 59.0% of the total forest lands on the islands of Jeollanamdo, and 73% of actual forests that exclude agricultural and other non-restorable forest lands. The findings of this study can help forest managers prepare a restoration plan and budget for island forests.