• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.257-262
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• 2004

This paper will develope an ocean environmental evaluation system. The system analysis by means of introducing the object oriented pattern analysis methodology. We will test water quality according to 40 sea water measurement points and evaluate the ocean environment by means of spatial statistical method. By analyzing the object oriented pattern ocean environmental system, we will contribute on enhancing the efficient development and maintenance other geographic information system.

• Computers and Concrete
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• v.17 no.4
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• pp.477-488
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• 2016

This paper aims to model the effects of five different variables which includes: cement content (C), the steel fiber amount (F), the silica fume amount (SF), the superplasticizer (SP), the silica fume amount (SF), and the water to cementitious ratio (w/c) on 28 days flexural toughness of Ultra High Performance Concrete (UHPC) as well as, a study on the variable interactions and correlations by using analyze of variance (ANOVA) and response surface methodology (RSM). The variables were compared by fine aggregate mass. The model will be valid for the mixes with 0.18 to 0.32 w/c ratio, 4 to 8 percent steel fiber, 7 to 13 percent cement, 15 to 30 percent silica fume, and 4 to 8 percent superplasticizer by fine aggregate mass.

• Journal of Korean Society on Water Environment
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• v.25 no.1
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• pp.96-101
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• 2009

The aim of this research was to apply experimental design methodology to optimizetion the photolytic degradation of N-nitrosodimethylamine (NDMA). Reactions were mathematically described as a function of parameters such as pH, initial NDMA concentration, and UV intensity using the Box-Behnken method. The results showed that the responses of NDMA removal (%) in photolysis were significantly affected by the synergistic effect of linear term of pH, initial NDMA concentration and UV intensity. The application of Response Surfase Methodology (RSM) using the Box-Behnken method yielded the following regression equation, which is an empirical relationship between the removal (%) of NDMA and test variables in coded unit: Y = 50.929 + 16.073(UV) - 7.909(NDMA) - 27.432(pH) - 11.385(UV)(NDMA) - 7.363(UV)(pH) +13.811(NDMA)(pH). The model predictions agreed well with the experimentally observed result ($R_2(ad.)=89%$).

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.215-222
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• 2014

A theory-based methodology for describing the thermodynamic properties of molecular nitrogen is presented. The results obtained indicate a successful application of a fully consistent statistical method for the description of a molecular system in different phase states. The method employs a density of states equation for solid nitrogen and a perturbation potential for gaseous and liquid nitrogen. The main characteristics of the calculation method include the need for a minimal number of initial data and the absence of fitting parameters. The adequacy of the physical model that is the basis for the method allows a description of existing experimental data and the peculiarities of the thermodynamic properties.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.109-122
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• 1998

A Process Quality Information System (PQIS) for the CIM environment is presented. The system includes three subsystems which are qualify control, statistical analysis, and information management. As part of CIM, the PQIS was designed by using the ICAM Definition(IDEF) methodology. The Oracle-CDE was utilized as a graphic development tool which made the system more user friendly. PQIS operates on IBM or compatible PCs under MS-Windows and Oracle DBMS. The methodology developed in this research provides a foundation for the application of CIM subsystems.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1427-1431
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• 2007

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• Journal of the Korean Applied Science and Technology
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• v.27 no.4
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• pp.452-460
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• 2010

Any theory of liquid should account for interactions between molecules, since molecules in a liquid are close to each other. For this matter statistical-mechanical methodology has been used and various models have been proposed on the basis of this methodology. Among them Kirkwood-Buff solution theory has attracted a lot of interest, because it is regarded as being the most powerful. In this article Kirkwood-Buff solution theory is revisited and its key equations are derived. On the way to these equations, the concepts of pair correlation function, radial distribution function, Kirkwood-Buff integration are explained and implemented. Since complexity of statical mechanics involved in this theory, the equations are applied to one-component systems and the results are compared to those obtained by classical thermodynamics. This may be a simple way for Kirkwood-Buff solution theory to be examined for its validity.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.669-673
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• 2008

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.132-137
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• 2002

The quality improvement methodology Six Sigma gained enormous international popularity in the past few years, mainly due to its successful implementation at General Electric. Six Sigma is now commonly understood not only as a statistical measure for process performance (6$\sigma$ stands for 3.4 defects per million opportunities) to improve product quality but it has also become a strategic initiative undertaken by many organisations to improve management quality. In the centre of the Six Sigma methodology is the improvement project, often referred to as Black Belt or Green Belt project. Although every business is different and business priorities differ from company to company, however all businesses face the same problem when it comes to Six Sigma projects: How to choose the right project\ulcorner This article intends to provide some answers to this and other frequently asked questions about Six Sigma projects.