• Journal of the Korea Institute of Building Construction
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• v.16 no.6
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• pp.519-527
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• 2016

The demand of construction risk analysis is rapidly increased to improve the competitiveness of construction companies and the sound management of the construction project. However, estimating the amount and uncertainty of the risk is difficult due to the wide range of risks in the construction industry. Moreover, most of the research on risk management of construction risk is only focused on the causes of risk without separate the internal and external risk. This study statistically analysis the internal risk and external risk based on the accidents cases which are caused at construction sites to define the difference and importances of the risk. An accident cause analysis and T-test analysis are carried out to reach the goal of study. The results of the study are expected to be used as a guideline of construction project risk analysis.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.147-176
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• 2019

This paper uses research on the Levelized Cost of Electricity (LCOE) in South Korea to conduct a simulation analysis on the impact of nuclear power dependency and usage rates on the social costs of power generation. We compare the $7^{th}$ basic plan for long-term electricity supply and demand, which was designed to increase nuclear power generation, to the $8^{th}$ basic plan for long-term electricity supply and demand that decreased nuclear power generation and increased renewable energy generation in order to estimate changes in social costs and electricity rates according to the power generation mix. Our environmental generation mix simulation results indicate that social costs may increase by 22% within 10 years while direct generation cost and electricity rates based on generation and other production costs may increase by as much as 22% and 18%, respectively. Thus we confirm that the power generation mix from the $8^{th}$ basic plan for long-term electricity supply and demand compared to the $7^{th}$ plan increases social costs of generation, which include environmental external costs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.10-21
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• 2021

This study analyzed how external variables, such as the supply policy of shared bicycles and the spread of infectious diseases, affect the demand for shared bicycle use in the COVID-19 era. In addition, this paper presents a methodology for more accurate predictions. The Seasonal Auto-Regulatory Integrated Moving Average with Exogenous stressors methodology was applied to capture the effects of exogenous variables on existing time series models. The exogenous variables that affected the future demand for shared bicycles, such as COVID-19 and the supply of public bicycles, were statistically significant. As a result, from the supply volume and COVID-19 outbreak according to the scenario, it was estimated that approximately 46,000 shared bicycles would be supplied by 2022, and the COVID-19 cases would continue to be at the current level. In addition, approximately 32 million and 45 million units per year will be needed in 2021 and 2024, respectively.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.214-223
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• 2022

The purpose of this study is to present a new industrial land demand prediction method that can consider external economic factors. The analysis model used ARIMA-X, which can consider exogenous variables. Exogenous variables are composed of macroeconomic variable, Business Survey Index, and Composite Economic Index variables to reflect the economic and industrial structure. And, among the exogenous variables, only variables that precede the supply of industrial land are used for prediction. Variables with precedence in the supply of industrial land were found to be import, private and government consumption expenditure, total capital formation, economic sentiment index, producer's shipment index, machinery for domestic demand and composite leading index. As a result of estimating the ARIMA-X model using these variables, the ARIMA-X(1,1,0) model including only the import was found to be statistically significant. The industrial land demand forecast predicted the industrial land from 2021 to 2030 by reflecting the scenario of change in import. As a result, the future demand for industrial land was predicted to increase by 1.91% annually to 1,030.79 km². As a result of comparing these results with the existing exponential smoothing method, the results of this study were found to be more suitable than the existing models. It is expected to b available as a new industrial land forecasting model.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.93-103
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• 2021

The special means of transportation introduced to improve the mobility of the transportation vulnerable met the number of legal standards in 2016, but lack of development in terms of quality, such as the existence of long waiting times. In order to streamline the operation of special means of transportation, long-term standby traffic, which is the top 25% of the wait time, was extracted from the Daegu Metropolitan Government's special transportation history data, and spatial autocorrelation analysis and social network analysis were conducted. As a result of the analysis, the correlation between the average waiting time of special transportation users and the space was high. As a result of the analysis of internal degree centrality, the peak time zone is mainly visited by general hospitals, while the off-peak time zone shows high long-term waiting demand for visits by lawmakers. The analysis of external degree centrality showed that residential-based traffic demand was high in both peak and off-peak hours. The results of this study are considered to contribute to the improvement of the quality of the operation of special transportation means, and the academic implications and limitations of the study are also presented.

• Journal of Preventive Medicine and Public Health
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• v.29 no.4 s.55
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• pp.831-842
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• 1996

Demand for high quality medical care has recently been increasing in step with high level of income and education. Patients prefer the use of large general hospitals to small community hospitals. Large hospitals, usually located at urban area, expand their capacities to cope with the increasing demand, therefore, they easily secure revenue necessary for growth and development of hospitals. However, small community hospitals are facing with serious financial difficulties caused from the reduction of patients in one hand and the inflation of cost in another. If small rural hospitals were closed, the closure would have negative impacts on local economies in addition to the decrease in access to medical care. Community leaders should have an insight on the contribution of community hospitals to local economies. They could make a rational decision on the hospital closure only with the understanding of hospital's contribution to the community. This study is designed to develop an economic model to estimate the contribution of rural hospital to local economies, and also to apply this model with a specific hospital. The contribution of a hospital to local economies consists of two elements, direct effect and multiplier effects. The direct impacts include hospital's local purchasing power, employee's local purchasing power, and the consumption of patients coming from outside the community. The direct impact induces multiplication effect in the local economy. The seed money invested to other industries grows through economic activities in the region. This study estimated the direct effect with the data of expenditure of the case hospital. The total effect was calculated by multiplied the direct effect with a multiplier. The multiplier was drown from the ratio of marginal propensity of income and expenditure. Beside the estimation of the total impacts, the economic effect from the external resources was also analyzed by the use of the ratio of patients coming outside the region. The results are as follows. 1. The direct economic contribution of the hospital to the local economy is 1,104 million won. 2. The value of multiplier in the region is 2.976. 3. The total economic effect is 3,286 million won, and the multiplication effect is 2,182 million won. 4. The economic contribution from the external resources is 245 million won which is 7.5% of the total economic effect.

• Journal of Radiation Industry
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• v.18 no.1
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• pp.35-42
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• 2024

Demand for RW transportation is expected to increase due to the continuous generation of RW from nuclear power plants and facilities, decommissioning of plants, and saturation of spent fuel temporary storage facilities. The locational aspect of plants and radiation protection optimization for the public have led to an increasing demand for maritime transportation, necessitating to apprehend the overseas and domestic current status. Given the potential long-term radiological impact on the public in the event of a sinking accident, a pre-transportation exposure assessment is necessary. The objective of this study is to investigate the overseas and domestic RW maritime transportation current status and overseas dose assessment cases for the public in sinking accident. Selected countries, including Japan, UK, Sweden, and Korea, were examined for transport cases, Japan and the U.S were chosen for dose assessment case in sinking accidents. As a result of the maritime transportation case analysis, it was performed between nuclear power plants and reprocessing facilities, from plants to disposal or intermediate storage facilities. HLW and MOX fuel were transported using INF 3 shipments, and all transports were performed low speed of 13 kn or less. As a result of the dose assessment for the public in sinking accident, japan conducted an assessment for the sinking of spent fuel and vitrified HLW, and the U.S conducted for the sinking of spent fuel. Both countries considered external exposure through swimming and working at seashore, and internal exposure through seafood ingestion as exposure pathway. Additionally, Japan considered external exposure through working on board and fishing, and the U.S considered internal exposure through spray inhalation and desalinized water and salt ingestion. Internal exposure through seafood ingestion had the largest dose contribution. The average public exposure dose was 20 years after the sinking, 0.04 mSv yr^-1 for spent fuel and 5 years after the sinking, 0.03 mSv yr^-1 for vitrified HLW in Japan. In the U.S, it was 1.81 mSv yr^-1 5 years after the sinking of spent fuel. The results of this study will be used as fundamental data for maritime transportation of domestic RW in the future.

• Biotechnology and Bioprocess Engineering:BBE
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• v.5 no.6
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• pp.441-448
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• 2000

Experimental work was carried out on nitrogen and phosphorus removal from real wastewater using a bench-scale SBR process. The phosphorus removal was stable and the phosphorus concentration remaining in the reactor was maintained within 1.5ppm, regardless of the addition of an external carbon source. In the case of nitrogen, an external carbon source was necessary for denitrification. The effect on denitrification with the addition of various carbon sources, such as glucose, methanol, acetate, and propionate, was also investigated. Acetate was found to be the most effective among those tested in this study. When 100ppm (theoretical oxygen demand) of sodium acetate was added, the average rate of denitrifiaction was 2.73mg NO$_3$-N (g MLSS)(sup)-1 h(sup)-1, which was ca. 4 times higher than that with the addition of 200 ppm of methanol. The phosphorus and nitrogen concentrations were both maintained within 1.5ppm by the addition of an appropriate amount of a carbon source during a long-term operation of the SBR. The mathematical modeling was performed using Monod kinetics, other microbial kinetics, mass balances, and stoichiometry. The modeling was found to be useful for predicting the SBR operation and optimizing the HRT.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• Journal of Korean Society of Rural Planning
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• v.19 no.1
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• pp.123-135
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• 2013

The purpose of this study is to investigate industry-wise employment growth factors in rural areas. Regional economic vitalization is sensitive to internal and external interaction changes among various industrial and occupational sectors. Thus, rural regional economic vitalization requires a comprehensive approach in analyzing industry-dependent employment structures and growth factors in rural areas. However, research conducted thus far has mostly focused on agriculture and farmers. Considering the evidence that rural communities continue to be stagnant and 80% of the rural population is engaged in nonagricultural activities, it becomes necessary to review industry-specific employment change factors in rural areas. This study targeted 5 counties in Chungnam. The results revealed that agriculture, forestry, and fisheries occupied the foremost positions with regard to population employed and regional GRDP share. The influence of national growth on employment and business variation effects was as high as 98.1% and 78.6%, respectively, thus demonstrating the high likelihood of rural economy to be influenced by external factors. Growth in the public sector appeared to support employment structure. Moreover, wholesale and retail businesses, constituting 14.4% of employment in rural areas, showed a strong trend toward degeneration, to the extent that difficulties have been forecasted for the supply of goods and services essential for basic livelihood of the rural residents. The implications based on the above observations need to be considered for policy-making to ensure that industrial structure is modified on the basis of internal demand of the region, and support for small businesses is integrated in rural area development projects.