• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.674-680
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• 2013

As the range of marine leisure activity gradually expands to ocean-going, a habitable cruise boat has been getting the limelight. Advanced countries in the marine leisure industry in Europe and North America have already secured their competitiveness in designing and building cruise boats by elegant design, ergonomic structure and fuel efficiency through the adoption of light-weight hull materials. In contrast, mostly small power boats are developed and built in Korea, and GFRP take up the most of hull materials. This study inquired into the design and characteristics of a 50ft-class CFRP that ocean-going is possible. The hull-form of the CFRP cruise boast were analyzed to propose a hull form for the designed ship (MMU-C.B), and based on that, the design model of the MMU-C.B was built. Finally, the MMU-C.B's characteristics of the resistance performance and hull-planing were found by comparative reviews with the results of model tests of GFRP pleasure yachts.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.165-174
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• 2013

Recently, many universities in Korea have been faced with critical crisis such as the decrease in the number of freshmen, the pressure for tuition cuts, M&A between universities and so on. Nobody has expected that universities will have this kind of difficulties. The universities are making attempts to innovate the quality of education to secure high level of education and to meet social needs to overcome these internal and external environment of crisis. For this innovation, the universities have sought to reduce the budget as well as conducted the self-evaluation to figure out their relative positions annually. Innovations cannot have having the limitation without education funds. Budget spent in universities have influences directly or indirectly on the structural improvement of the finance and on the growth of universities. The purpose of this study is to explore the decision-making method to find the optimal budget allocation so as to minimize the execution budget and to maximize the management evaluation by taking the advantage to analyse the relationship between the evaluation and the budget. Therefore, in this paper, we implement the development of the mathematical model for the University Evaluation and Budget Allocation Optimization in the form of the linear programming.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.5
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• pp.315-323
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• 2015

Damage level of coastal structures has been scaled up according to increase of wave height and duration of the storm due to the abnormal global climate change. So, the design criteria for new breakwaters is being intensified and structural strengthening is also conducted for the existing breakwaters. Recently, interlocking concept has been much attention to enhance the structural stability of the conventional caisson structure designed individually to resist waves. The interlocking caisson breakwater may be survival even if unusual high wave occurs because the maximum wave force may be reduced by phase lags among the wave forces acting on each caisson. In this study, the dispersion characteristics of wave forces using interlocking system that connect the upper part of caisson with cable in the normal direction of breakwater was investigated. A simplified linear model was developed for computational efficiency, in which the foundation and connection cables were modelled as linear springs, and caisson structures were assumed to be rigid. From numerical experiments, it can be found that the higher wave forces are transmitted through the cable as the angle of incident wave is larger, and the larger the stiffness of the interlocking cable makes larger wave dispersion effect.

• Horticultural Science & Technology
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• v.29 no.3
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• pp.181-186
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• 2011

To predict the bud bursting date of 'Campbell Early' grapevines, the bud developmental rate (DVR) models were constructed. The DVRs for bud bursting were calculated from the demanded times at controlled air temperatures. The DVRs were examined on the 'Campbell Early' grapevines incubated in three different temperatures at 4.6, 11.8, and $16.6^{\circ}C$. The DVR increased exponentially or linearly on the air temperature with a slope of about 0.0019. The DVR equations were computed as $DVR=0.0249+0.0020e^{0.1654x}$ or DVR = 0.0019x + 0.0187. These DVR equations offered developmental indices and predicted dates for bud bursting with air temperature data. The DVR equations were validated to the bud bursting data observed in the field. When bud bursting dates were calculated with daily temperature data, the root mean squared error (RMSE) between the observed and the predicted dates was less than 4 days. When those were calculated with hourly temperature data, on the other hand, the RMSE was less than 3 days. These results suggest that the DVR models are useful to predict bud bursting date of 'Campbell Early' grapevines.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.671-683
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• 2018

The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.5
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• pp.47-56
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• 2008

This study was performed to evaluate the effect of Response modification factors (R-factor) in 3-, 9- and 20- story steel Moment Resisting Frame (MRF) buildings. Each structure was designed using a R-factor of 8, as tabulated in the 2000 International Building Code provision (IBC 2000) and Korea Building Code (KBC) 2008. In order to evaluate the maximum and minimum performance expected for such structures, an upper bound and lower bound design were adopted for each model. Next, each analytical model was designed using different R-factors (8, 9, 10, 11, 12) and four different structural periods with the original fundamental period. For a detailed case study, a total of 150 analytical models were subjected to 20 ground motions representing a hazard level with a 2% probability of being exceeded in 50 years. In order to evaluate the performance of the structures, static push-over and non-linear time history analysis (NTHA) were performed, and displacement ductility demand was investigated to consider the ductility capacity of the structures. The results show that the dynamic behaviors for the 3- and 9-story buildings are relatively stable and conservative, while the 20-story buildings show a large displacement ductility demand due to dynamic instability factors. (e.g. P-delta effect and high mode effect)

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.315-323
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• 2010

In Wireless Sensor Networks (WSNs), various Data-Centric Storages (DCS) schemes have been proposed to store the collected data and to efficiently process a query. A DCS scheme assigns distributed data regions to sensor nodes and stores the collected data to the sensor which is responsible for the data region to process the query efficiently. However, since the whole data stored in a node will be lost when a fault of the node occurs, the accuracy of the query processing becomes low, In this paper, we propose an in-network aggregation query processing method that assures the high accuracy of query result in the case of data loss due to the faults of the nodes in the DCS scheme. When a data loss occurs, the proposed method creates a compensation model for an area of data loss using the linear regression technique and returns the result of the query including the virtual data. It guarantees the query result with high accuracy in spite of the faults of the nodes, To show the superiority of our proposed method, we compare E-KDDCS (KDDCS with the proposed method) with existing DCS schemes without the data-loss correction method. In the result, our proposed method increases accuracy and reduces query processing costs over the existing schemes.

• International Area Studies Review
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• v.13 no.3
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• pp.287-308
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• 2009

This article examines the distributional characteristics of the return of Chinese stock market indices. The majority of previous empirical researches have tended to focus upon the simple stock market index. However, this study focuses on the four indices which represent the characteristics of each stock market index. The empirical findings indicate that the returns of the four chinese indices are not normally distributed at conventional levels. The Ljimg-Box -statistics indicate the returns of the index of A shares are not serially autocorrelated. However, the returns of the index of B shares are serially autocorrelated. The empirical findings also indicate returns of the four chinese indices are not serially autocorrelated. The statistics of Regression Specification Error Test and ARCH indicate the returns of all four indices are not serially linear. The findings also indicate that E- GARCH model is the most fittest model for the returns of the four chinese indices and the forecast error can be reduced by using student t distribution rather normal distribution.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.414-421
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• 2022

Subsea oil and gas exploration is increasingly moving into deeper water depths, and typically, subsea pipelines operate under high pressure and temperature conditions. Owing to the difference in these components, the axial force in the pipe is accumulated. When a pipeline is operated at a high internal pressure and temperature, it will attempt to expand and contract for differential temperature changes. Typically, the line is not free to move because of the plane strain constraints in the longitudinal direction and soil friction effects. For a positive differential temperature, it will be subjected to an axial compressive load, and when this load reaches a certain critical value, the pipe may experience vertical (upheaval buckling) or lateral (snaking buckling) movements that can jeopardize the structural integrity of the pipeline. In these circumstances, the pipeline behavior should be evaluated to ensure the pipeline structural integrity during operation in those demanding loading conditions. Performing this analysis, the correct mitigation measures for thermal buckling can be considered either by accepting bar buckling but preventing the development of excessive bending moment or by preventing any occurrence of bending.

• 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.