• Journal of the Korean School Mathematics Society
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• v.7 no.1
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• pp.103-120
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• 2004

Today most of Korean students have no interest in mathematics and lack of confidence in it due to the burden of the college entrance examination, which often results in a failure or a dropout in mathematics in school. Therefore there is a need to diagnose the true causes and to find out a solution. As one of these solutions this study has developed and applied a learning model based upon cooperative group activities for the improvement of mathematical power to classroom. For developing this learning model a variety of research methods are used; questionnaires, observation, and analysis of the interview materials. After the application of this learning model, a lot of positive results in mathematics class have been observed.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.77-85
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• 2013

In this paper, we present the cost measurement metrics for the operation management and service improvement besides the general maintenance. The cost measurement metrics of the operation management and service improvement are based on the metrics that are classified and summarized precedent studies and complemented by empirical measuring indicators, and we propose the maintenance cost estimation model based on this metrics. The maintenance costs can be calculated detailedly, because the proposed metrics can be used selectively, depending on the scope of the information system maintenance. The effectiveness of the proposed maintenance cost measurement metrics and cost estimation model is verified by comparison between existing studies and our research.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.437-442
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• 2005

For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of a test speaker by making use of the obtained probabilistic model. The purpose of this study is twofold: improving the existing ML based methods, and comparing the performance of what is called 'soft decision' method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained tv adjusting the number of clusters in GMM appropriately.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.

• Journal of Wind Energy
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• v.14 no.3
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• pp.25-33
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• 2023

In order to secure the safety of increasing offshore activities such as offshore wind farm maintenance and fishing, IMPACT, a mid-term marine weather forecasting system, was established by predicting marine weather up to 7 days in advance. Forecast data from the Korea Hydrographic and Oceanographic Agency (KHOA), which provides the most reliable marine meteorological service in Korea, was used, but wind speed and wave height forecast errors increased as the leading forecast period increased, so improvement of the accuracy of the model results was needed. The Model Output Statistics (MOS) method, a post-correction method using statistical machine learning, was applied to improve the prediction accuracy of wave height, which is an important factor in forecasting the risk of marine activities. Compared with the observed data, the wave height prediction results by the model before correction for 6 to 7 days ahead showed an RMSE of 0.692 m and R of 0.591, and there was a tendency to underestimate high waves. After correction with the MOS technique, RMSE was 0.554 m and R was 0.732, confirming that accuracy was significantly improved.

• Journal of Korea Port Economic Association
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• v.39 no.2
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• pp.165-178
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• 2023

This paper presented a management model for continuous development and co-prosperity between freight car transportation companies and consigned car owners, which are the subjects of the consignment system. A structural equation-based questionnaire was created to measure the variables necessary for establishing a management model, such as business owners' perceptions of each other and their needs for improvement, by analyzing the internal and external environment of the freight transport market and conducting surveys of freight companies and consigned vehicle owners. As a result of the analysis, it was confirmed that the rationality of the consignment system did not have a significant effect on financial performance or the level of transportation service, while the external environment and compensation system of the transportation business had a significant impact on financial performance and level of transportation service. In addition, the rationality of the consignment system does not affect the improvement of the relationship between project entities, but it does affect the level of trust. It was confirmed that the external environment and compensation system of the transport business have an effect on both relationship improvement and trust level improvement. It was found that the level of trust affects financial performance, and relationship improvement does not affect both financial performance and transportation service level. It is necessary to manage the consignment system based on the confirmed analysis results in order for the domestic freight company and the consigned vehicle owner to coexist and develop each other in the truck transportation market.

• Journal of the Korean GEO-environmental Society
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• v.19 no.10
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• pp.5-11
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• 2018

The PBD (Plastic Board Drain) method is one of effective ground improvement methods on the soft dredging reclamation ground. This method has outstanding economic efficiency and constructability, and it is widely used for the soft ground improvement. However, the PBD method reduces permeability and drainage capacity of the ground due to the long construction period. Therefore, the nano coated Plastic drain board (PDB) was developed to solve problems. It is the non-metallic electrode and improves the weakness of the PBD method by using electric force of the electro-osmosis method. Various researches have been conducted to apply the nano coated PDB, but these researches were limited to model tests in laboratory. In this study, model and field tests were conducted to assess field applicability of the nano coated PDB. The result showed that the nano coated PDB had the better effect on the ground improvement compared to the normal PDB.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.91-102
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• 2005

In the past it was generally felt that software quality was the same as product quality. But as the software industry is getting bigger and more complex, it has become hard to develop quality software that meets customers' needs just depending on product quality. Therefore, lately many domestic and foreign companies are making efforts to upgrade product quality by implementing Process Improvement. Measurement in Process Improvement activities enhances the effect of Process Improvement, and also identifies and helps the activities. In support of this paper, research on the measurement program was conducted based on the CMMI adopted by many domestic and foreign companies for their process improvement model. MA process area that deals with measurement process setup is one of the CMMI process areas and was the basis of the structure of the measurement program. This study presents differences of the measurement program for each level of CMMI and how to apply it. The results of this study can help a business to apply CMMI-based Process Improvement by backing up the structure of the measurement program. Furthermore, it can offer a roadmap for progress of the measurement program for each level.

• Korean Management Science Review
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• v.14 no.1
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• pp.131-149
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• 1997

Software cost estimation is an important both for buyers and sellers(developers). We reviewed domestic and foreign researches and practices on software cost estimation with function point method comprehensively, In this paper, we derived four promising alternative function point models. They are an IFPUG(International Function Point User Group)-based model(Model I), a shorthand model for client/sever software systems(Model II), a data-oricnted model for relatively large software projects(Model III), and a general- purpose function point model for non business application softwares as well as business applications(Model IV). Empirical data shows that Model I, II, and IV are very useful function point models. In particular, model II and IV look very useful models since they are concise and accurate. These models can be incorporated in a new improved guideline for software cost estimation. General opinion survey shows that Model I, II and IV are preferable. There are no significant differences in preference between buyers and sellers. The survey also shows that users think function point method is better than step(line of code)-oriented cost estimation methods in many ways including objectivity and estimation accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.