• Korean Journal of Social Welfare Studies
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• no.37
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• pp.117-144
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• 2008

This is a timeseries study on the riving forces behind the changes of Korean welfare state. There are a few previous studies on the determinants of korean welfare state. These previous studies have some limitations in terms of reliability of the data source and validity of the statistical method used. Using the Comparative Social Policy Data-set(CSPD), we try to overcome the limitation of these previous studies. And adapting the time series regression, we examine the hypotheses about the changes of korean welfare state. In this study, four dependent variables are examined: the ratio of public social welfare expenditure to the GDP(WELGDP), the ratio of public social welfare expenditure to the government budget(WELGOV), the ratio of social expenditure to the GDP(SOCX), social welfare expenditure per capita. And independent variables were selected based on the theoretical background on the changes of welfare state. The results of this study as follows: First, the variables based on structural functionalism (industrialization) are the major driving forces behind the changes of korean welfare state since 1960s. Second, the effect of unemployment variable may be reasonably interpreted as reflecting the residual characteristics of korean welfare state. Third, the politics of the left based on power resource theory should be restrictedly interpreted. Ultimately, korean welfare state is still at rudimentary stage where the theory of industrialization is well applied as a driving forces behind the changes of welfare state.

• Journal of the Korean Society of Propulsion Engineers
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• v.4 no.4
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• pp.87-97
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• 2000

A steady state performance simulation and diagnostics program for the turboprop engine (PT6A-62), which is the power plant of the first developed military basic trainer KT-1 in Republic of Korea, was developed. The developed steady state performance analysis program was evaluated with the performance data provided by the engine manufacturer and with analysis results of GASTURB program, which is well known for the performance simulation of gas turbines. Performance parameters were discussed to evaluate validity of the developed program at various cases such as altitude, flight velocity and part load variation. GPA(Gas Pass Analysis) allows engine performance deterioration to be identified at the module level in terms of reduction in component efficiencies and changes in mass flow. In order to find optimal instrument set to detect the physical faults such as fouling, erosion and corrosion, a gas path analysis approach is utilized. This study was performed in two cases for selection of optimal measurement parameters. One case was considered with the effect of instrument number by changing independent parameter number. The other case was performed with selection of independent parameter set. According to the analysis results, the optimal measurement parameters selected were eight dependent variables such as shaft horsepower, fuel flow rate, compressor exit pressure and temperature, compressor turbine inlet pressure and temperature and power turbine inlet pressure and temperature.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.220-227
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• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.50-55
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• 2019

Recently Ransomware attacks are continuously increasing, and new Ransomware, which is difficult to detect just with a basic vaccine, continuously has its upward trend. Various solutions for Ransomware have been developed and applied. However, due to the disadvantages and limitations of existing solutions, damage caused by Ransomware has not been reduced. Ransomware is attacking various platforms no matter what platform it is, such as Windows, Linux, servers, IoT devices, and block chains. However, most existing solutions for Ransomware are difficult to apply to various platforms, and there is a limit that they are dependent on only some specific platforms while operating. This study analyzes the problems of existing Ransomware detection solutions and proposes the onboard module based Ransomware detection system; after the system defines the function of necessary elements through analyzing requirements that can actually reduce the damage caused by the Ransomware from the viewpoint of users, it supports various OS without pre-installation and is able to restore data even after being infected. We checked the feasibility of each function of the proposed system through the analysis of the existing technology and verified the suitability of the proposed techniques to meet the user's requirements through the questionnaire survey of a total of 264 users of personal and corporate PC users. As a result of statistical analysis of the questionnaire results, it was found that the score of intent to introduce the system was at 6.3 or more which appeared to be good, and the score of intent to change from existing solution to the proposed system was at 6.0 which appeared to be very high.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.337-347
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• 2021

Land is key natural resource that Chinese government actually owns. Real estate and land development have played an important part in China's urban development and economic development. The Chinese local governments' land development policies can mainly be characterized as the establishment of economic development zones and the development of new towns. Given the great importance of these measures, we can expect that these policies can generate noticeable impacts on land development and land price. However, little research has explored these impacts empirically. Using the data collected from land development projects of three districts in Linyi city-old town, new town, economic development zone, this paper attempts to investigate the impact of government development policy on land development and land price. This research chooses investment amount and land price as dependent variables. The multiple regression results demonstrate that the local government's land Development policies can affect land investment size and land price significantly. As we have noticed, the target of government development policy is to make use of urban land resources more scientifically and efficiently. Based on my empirical analysis, some useful insights can be provided for improving our understanding concerning the effects of these government land development policies.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.2
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• pp.185-203
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• 2013

This study was to suggest the new rules for revision of KCR4 by comparing between ISBD consolidated edition and KCR4. The study methods was to compare the rules in each element after mapping the description elements in each area of ISBD and KCR4. Resultingly, first, content forms and media types must be included for describing resource types. Second, it is needed for rules about the common title and the dependent title. Third, it is needed for rules about "parallel" such as parallel title, parallel other title information, parallel statement of responsibility relating to title, parallel edition statement, parallel statement of responsibility relating to edition, parallel numbering system, parallel place of publication, production and distribution, et. al. Fourth, the rules about material or type of resource specific area must be regulated in terms of the contents of the resource. Fifth, the home country principle must be not applied in describing the place of publication, production and distribution for the consistency. Sixth, it is needed to regulate the extent, other physical details, dimensions, and accompanying material statement for all materials instead of the material description according to material types. Seventh, rule number of notes must be agreed to number of main rules. Eighth, it is needed for detailed rules about resource identifier. This study might be contributed to revise the KCR4.

• International Area Studies Review
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• v.22 no.2
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• pp.121-138
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• 2018

This study aims to suggest effective management performances of nonprofit arts organizations among competitive environment. To examine the factors which affect the performance of nonprofit performing arts organizations such as external environment, cultural capital, orchestra characters and government grants, this study analyzed the data of 990 tax forms of the US Internal Revenue Service(IRS) of the top 73 symphony orchestras and government sources in USA. Independent variables are measured as sponsorship amount which is the biggest part of orchestras' income, and ticket sales which is profit of inherent business purpose. As a result, the performance of the orchestra is affected positively by the income and influenced by the orchestras' characteristics including age, size, and conductor. Also government grants to the orchestras are essential for organizations to sustain their business. But, there are two different sides on these effects that small to medium groups are influenced by crowding in effect and bigger groups are more likely conducted by business overview. Lastly, cultural capital is no significant relationships to orchestras' management performances. This scope of the study is limited to American symphony orchestras; however, this study is significant in that empirical analysis on nonprofit performing arts organizations from an economic view point and contribute on other nonprofit arts organizations to develop their strategic plan for sustainable business.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.73-89
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• 2021

The handysize bulk carriers are capable of transporting a variety of cargo that cannot be transported by mid-large size ship, and the spot chartering market is active, and it is a market that is independent of mid-large size market, and is more risky due to market conditions and charterage variability. In this study, Granger causality test, the Impulse Response Function(IRF) and Forecast Error Variance Decomposition(FEVD) were performed using monthly time series data. As a result of Granger causality test, coal price for coke making, Japan steel plate commodity price, hot rolled steel sheet price, fleet volume and bunker price have causality to Baltic Handysize Index(BHSI) and charterage. After confirming the appropriate lag and stability of the Vector Autoregressive model(VAR), IRF and FEVD were analyzed. As a result of IRF, the three variables of coal price for coke making, hot rolled steel sheet price and bunker price were found to have significant at both upper and lower limit of the confidence interval. Among them, the impulse of hot rolled steel sheet price was found to have the most significant effect. As a result of FEVD, the explanatory power that affects BHSI and charterage is the same in the order of hot rolled steel sheet price, coal price for coke making, bunker price, Japan steel plate price, and fleet volume. It was found that it gradually increased, affecting BHSI by 30% and charterage by 26%. In order to differentiate from previous studies and to find out the effect of short term lag, analysis was performed using monthly price data of major cargoes for Handysize bulk carriers, and meaningful results were derived that can predict monthly market conditions. This study can be helpful in predicting the short term market conditions for shipping companies that operate Handysize bulk carriers and concerned parties in the handysize chartering market.