• Korean Security Journal
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• no.24
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• pp.1-31
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• 2010

This article aims to identify organizational factors that influence the performance of implementation of the U.S. Freedom of Information Act (FOIA) and to investigate the strength and direction of their effects. Explanatory variables include administrative resources, organizational culture, litigation cost, and the complexity of FOIA requests. The study will analyze quantitative secondary data from official statistics of federal agencies and the 2006 Federal Human Capital Survey as well as qualitative data from semi-structured interviews of FOIA officers. The results of statistical analyses are as follows : FOIA funding significantly affects median processing time and number of requests pending. There is a significant relationship between bureaucratic culture and number of requests pending, but not between bureaucratic culture and number of requests pending. There exists a significant relationship between the cost of FOIA litigation to federal agencies and the performance of FOIA implementation. There exists a significant relationship between the complexity of FOIA requests and the performance of FOIA implementation. This study also has important implication in South Korea, which has been under a sharp confrontation with North Korea for more than 50 years. As illustrated by the conflict between people's right to know and national security during the investigation of recent Sinking of the ROKS Cheonan, efforts should be made to prepare legal and institutional mechanism for freedom of information policy which can maintain a balance between conflicting values as well as efficient information disclosure in Korea.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.404-416
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• 2020

Flooding is an unavoidable natural disaster for the city. Flood disasters seriously undermine the city's economy, safety, and sustained development. In the course of development and construction of waterfront space in the same city, the construction of basic disaster prevention facilities cannot be avoided completely even if huge amounts of capital are invested to reduce the economic damage of flooding. The cost of rebuilding the city after the disaster is much higher than the cost of building disaster prevention facilities. In recent years, the theory of elasticity in urban reconstruction and so on has been a subject of city problem solving, creating widespread discussion and attention in academia. In other words, how to transform the concept of elasticity into practice based on theoretical and empirical factors is a real problem facing urban disaster. Through theoretical literature on the waterfront (space) buffer zone of a city (flood-weak area) and the case study of the city's practice, this paper tries to clarify the element of 5R, the theory of elastomeric fire prevention, and present detailed measures accordingly. In addition, the following two problems are addressed while emphasizing the feasibility of implementing the urban waterfront (space) plan of the elastomeric element around the urban water buffer zone. First, the means of disaster prevention planning are used to mitigate conflicts between individual utility of urban waterfront and disaster prevention functions in waterfront buffer zones, and second, the waterfront buffer zone can respond to flood-causing problems in terms of disaster prevention as much as possible through the elastic disaster prevention plan.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.114-122
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• 2015

Real estate development requires significant amount of capital investment. The project duration has been increased according to its enlarged size. For this reason, cost overrun and time delay are important risk factors that should be managed properly. As a method to hedge the risk, varoius real option methods have been presented. However, conventional project value assesment methods such as NPV(Net Present Value) have weakness to support decision making by reflecting dynamic situations in terms of variation of cost and time. Furthermore, the decision making process is serious of actions rather than discrete event. The purpose of this paper is to present a multiple real option valuation method to overcome the deterministic aspect of real option presented in previous research and practice. The method is developed as following: firstly, to select the model that can be applied in the real estate development project through a survey from previous literature on real options analysis; secondly, to apply data from office development case in order to verify the model by applying conventional real option and multiple real option valuation. According to analysis result, multiple real option provides enhanced values comparing to NPV and single real option.

• The Journal of Bigdata
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• v.4 no.2
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• pp.159-173
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• 2019

Last-Mile delivery optimization plays a key role in the urban supply chain operation, which is the most expensive and time-consuming and most complicated part of the whole delivery process. The urban consolidation center (UCC) is regarded as a significant asset for supporting customer demand in the last-mile delivery service. It is the key benefit of UCC to improve the load balance of vehicles and to reduce the total traveling distance by finding the better route with the well-organized multi-leg vehicle journey in the urban area. This paper presents the model using multiple scenario analysis integrated with mathematical optimization techniques using Geographic Information System (GIS). The model aims to find the best solution for the distribution network consisted of DC and UCC, which is applied to the case of Ulaanbaatar Mongolia. The proposed methodology integrates two sub-models, location-allocation model and vehicle routing problem. The multiple scenarios devised by selecting locations of UCC are compared considering the general performance and delivery patterns together. It has been adopted to make better decisions the quantitative metrics such as the economic value of capital cost, operating cost, and balance of using available resources. The result of this research may help the manager or public authorities who should design the distribution network for the last mile delivery service optimization using UCC within the urban area.

• Plant Journal
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• v.10 no.1
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• pp.47-53
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• 2014

The national capacity of electricity of Korea was 81,737 MW and the peak demand was renewed by the record of 71,230 MW in 2012 which has been increasing since the first lighting ceremony had taken place in the Royal Palace(Kyung-Bok Goong) in 1887. Aa the counteract on the rapid increasing of the demand, Korean government is constructing and operating the high capacity nuclear and thermal power plants, however, the operating reserve on weekdays is small while those of weekends are more than 40% of capacity, so they are providing the pumped-storage power plants with the surplus electricity during weekends and operating the power plants which cost higher production price and located in the capital area with WSS (Weekly Start and Stop) mode including the Seoul Thermal Power Plant. Since the Seoul Thermal Power Plant is spending huge amount of expenses for more than 30 times of the WSS annually due to the high production cost even though it is in Seoul, the core of the demand, I chose the power plant unit #5 which was on the grid in 1969 for the case to confirm reducing 23% of the warm start-up time by applying the "Start-up time management program", and that reducing 35% of the water temperature increasing time by accelerate the increasing rate of the inlet temperature of the water circulating pump.

• The Korean Journal of Financial Management
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• v.22 no.1
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• pp.1-36
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• 2005

In this study, we analyzed determinant factors of leverage ratio and debt maturity for Korean firms in the simultaneous equation system using 2SLS (two stage least square) method under assumption that two variables are jointly determined in the capital structure decision. As a result of the analysis, we found that leverage ratio and debt maturity are positively related. Also, as for determinant factors of debt maturity, agency cost hypothesis, asset maturity matching hypothesis, signalling and liquidity risk hypothesis are all generally supported, and further leverage ratio are significantly positively related with firm size, but negatively related with default risk. However, when we divided samples into groups according to bank debt level and Chaebul affiliation, with contrast to existing study which worked on similar issues with OLS, we found no evidence supporting the argument that the information asymmetry problem is less severe in firms with more bank debt, whereas information asymmetry and financial constraint problems are more severe in non-Chaebul affiliated firms.

• Korea Journal of Hospital Management
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• v.12 no.3
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• pp.47-67
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• 2007

This paper aims at suggesting several ways lo change financial vulnerability and to improve managerial capability of local public hospitals (LPHs) in Korea through the identification of factors affecting profitability. Several findings of the research are as follows: To begin with, LPHs exhibited a statistically significant difference in their profitability from one another, according to tile analyses of their profitable margins from tile general characteristics. It depends on the number of hospitals in the area, the population of the hospital-built area, the number of competing hospitals, the number of staff per 100 beds, the opening of special clinic, the educational function, and the capacity of rooms. However, there was no variable in the managerial characteristics, presenting a significant difference, in contrast with hospitals which have been managed by private companies and made a great amount of profits. Second, according to the analyses of profit differences in behavioral effort-characteristics, a statistically significant difference was revealed upon the basis of the efforts to improve the clinic service, invite special patients, and shorten the period of being hospitalized. Third, the result of analyses about the difference of profitability from medical care and finance is statistically significant in the rate of labor cost, the rate of management cost, bed-occupancy rate, and the period of being hospitalized. Fourth, according to the analyses of the factors influencing the net profit ratio of the entire capital, Adjusted explanatory power(Adjusted $R^2$) was shown up to 65.2%, which is high. To compare the adjusted explanatory power stage by stage, the first stage model applying only two variables such as structural and strategic characteristics exhibited 23.8%, and the second stage model adding financial characteristics showed 51.5%. The explanatory power was much improved up to 65.2% when the third stage model incorporated the outcome of medical care performance. When the return on investment(ROI) was examined by using the multi-variate linear regression analysis at the final model of third stage, it was found that ROI had a positive relationship with the increase rate of patients, labor costs per doctor, and medical care rate of socially protected inpatients. However, it revealed that ROI had a negative relationship with the ratio of labor costs, the number of patients per managerial staff, and occupancy rate of rooms, respectively. The research suggests that in order for LPHs to increase profitability, LPH, should make efforts not only to attract patients to the hospitals without any discrimination of the patients depending on their financial status, but also to develop efficient management methods to reduce labor costs.

• Journal of Navigation and Port Research
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• v.40 no.3
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• pp.121-128
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• 2016

The present study aimed to derive decision factors for residency in the hinterland of Incheon New Port that is undergoing the development of port hinterlands and changes in leasing methods considered from the perspective of companies and analyze the priorities of determinants for residency through Fuzzy-AHP in order to present a direction to activate companies entries into the hinterland of Incheon New Port. When the comprehensive rankings of determinants for residency in the hinterland of Incheon New Port, rent levels among cost factors took the highest ranking with a value of 10.2% followed by the throughput of the port among market factors with a value of 8.2%, the scale of the market on the background with a value of 7.3%,, reduction in inland transport costs among cost factors with a value of 7.1%, connectivity to inland transportation networks among locational factors with a value of 6.7%, and designation as a free trade zone and the scale with a value of 6.4%. When seen from the viewpoint of companies to determine whether to move into the hinterland of Incheon New Port, the rent level should be provided to be more attractive compared to the hinterlands of ports in other regions. In addition, inland transportation costs which are a matter of the most serious concern of shippers in the capital region should be reduced and sea routes that can directly connect Incheon New Port to US ports and European ports should be opened.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.1
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• pp.21-28
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• 2019

Reversible solid oxide fuel cell (ReSOC) system was integrated with waste steam for electrical energy storage in distributed energy storage application. Waste steam was utilized as external heat in SOEC mode for higher hydrogen production efficiency. Three system configurations were analyzed to evaluate techno-economic performance. The first system is a simple configuration to minimize the cost of balance of plant. The second system is the more complicated configuration with heat recovery steam generator (HRSG). The third system is featured with HRSG and fuel recirculation by blower. Lumped models were used for system performance analyses. The ReSOC stack was characterized by applying area specific resistance value at fixed operating pressure and temperature. In economical assessment, the levelized costs of energy storage (LCOS) were calculated for three system configurations based on capital investment. The system lifetime was assumed 20 years with ReSOC stack replaced every 5 years, inflation rate of 2%, and capacity factor of 80%. The results showed that the exergy round-trip efficiency of system 1, 2, 3 were 47.9%, 48.8%, and 52.8% respectively. The high round-trip efficiency of third system compared to others is attributed to the remarkable reduction in steam requirement and hydrogen compression power owning to fuel recirculation. The result from economic calculation showed that the LCOS values of system 1, 2, 3 were 3.46 ¢/kWh, 3.43 ¢/kWh, and 3.14 ¢/kWh, respectively. Even though the systems 2 and 3 have expensive HRSG, they showed higher round-trip efficiencies and significant reduction in boiler and hydrogen compressor cost.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.