• Proceedings of the Korea Society of Design Studies Conference
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• 2003.05a
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• pp.274-275
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• 2003

• Proceedings of the CALSEC Conference
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• 2001.02a
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• pp.545-560
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• 2001

Agenda ■ Modeling S/W Components ■ Methods of how to implement a Component ■ History of Web Computing ■ e-Biz. frame wok using EJB ■ Legacy Integration Modeling S/W Components ■ Simple components ■ Component assembly-plugging ■ Processes ＆ Methodologies. (omitted)

• Journal of Environmental Impact Assessment
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• v.12 no.4
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• pp.245-257
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• 2003

$SO_2$ concentrations in the Seoul Metropolitan Area (SMA) were predicted by the regional segment ISCST3 modeling. The SMA was segmented by three modeling regions where the weather monitoring station exists since the area of the SMA, approximately $100km{\times}100km$, is too wide to be modeled by one modeling domain. The predicted concentrations by the model were compared with the measured concentrations at 39 air monitoring stations located in the SMA to validate the ISCST3 modeling coupled with the regional segment approach. The predicted concentrations by the regional segment method showed better performance in depicting the measurements than those by the non-segment ISCST3 modeling. The correction methods of the calculated concentrations reviewed were here the correlation method by the first order linear equation and the ratio method of observed to calculated concentrations. The corrected concentrations by two methods showed good agreement with the measured data. The ratio method was, however, easily applicable to the concentration correction in case of a wide modeling region considered in this study.

• International journal of advanced smart convergence
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• v.6 no.4
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• pp.88-95
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• 2017

The conventional scan methods are based on a laser scanner and a depth camera, which requires high cost and complicated post-processing. Whereas in photometric scanning method, the 3D modeling data is acquired through multi-view images. This is advantageous compared to the other methods. The quality of a photometric 3D model depends on the environmental conditions or the object characteristics, but the quality is lower as compared to other methods. Therefore, various methods for improving the quality of photometric scanning are being studied. In this paper, we aim to investigate the effect of illumination conditions on the quality of photometric scanning data. To do this, 'Moai' statue is 3D printed with a size of $600(H){\times}1,000(V){\times}600(D)$. The printed object is photographed under the hard light and soft light environments. We obtained the modeling data by photometric scanning method and compared it with the ground truth of 'Moai'. The 'Point-to-Point' method used to analyseanalyze the modeling data using open source tool 'CloudCompare'. As a result of comparison, it is confirmed that the standard deviation value of the 3D model generated under the soft light is 0.090686 and the standard deviation value of the 3D model generated under the hard light is 0.039954. This proves that the higher quality 3D modeling data can be obtained in a hard light environment. The results of this paper are expected to be applied for the acquisition of high-quality data.

• Geophysics and Geophysical Exploration
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• v.17 no.3
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• pp.163-170
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• 2014

Development of a modeling technique for accurately interpreting electromagnetic (EM) data is increasingly required. We introduce finite difference (FD) and finite-element (FE) methods for three-dimensional (3D) frequency-domain EM modeling. In the controlled-source EM methods, formulating the governing equations into a secondary electric field enables us to avoid a singularity problem at the source point. The secondary electric field is discretized using the FD or FE methods for the model region. We represent iterative and direct methods to solve the system of equations resulting from the FD or FE schemes. By applying the static divergence correction in the iterative method, the rate of convergence is dramatically improved, and it is particularly useful to compute a model including surface topography in the FD method. Finally, as an example of an airborne EM survey, we present 3D modeling using the FD method.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.2
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• pp.116-129
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• 2000

Due to the great complexity of the physical phenomena involved in most ultrasonic nondestructive testing, the numerical method is effective in many cases of their theoretical modeling. A brief overview is provided in this paper of the numerical methods used in modeling ultrasonic nondestructive testing, with an emphasis on the finite difference and the finite element methods. The procedures of execution, special considerations required, and some previous research results of the finite difference and the finite element methods are presented, with a rather extensive list of work reported in the literature. These numerical modeling techniques for ultrasonic nondestructive testing are expected to be more reliable and more convenient, as a result of the continuing technological development of computers.

• Structural Engineering and Mechanics
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• v.61 no.2
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.4
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• pp.585-594
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• 2006

This study focused on the causes of fish kills and its prevention methods in Yudeung Stream, Daejeon, Korea. Intense field data, continuous water quality monitoring system and water quality modeling were applied to analyze the causes. Pollutant can be delivered to urban streams by surface runoff and combined sewer overflows in rainfall events. However, water quality analysis and water quality modeling results indicate that the abrupt fish kills in the Yudeung stream seems to be caused by combined effect of DO depletion, increase in turbidity and other toxic material. Excessive fish population in the study area may harm the aesthetic value of the stream and also has greater potential for massive fish kills. It is suggested to implement methods to reduce delivery of pollutants to the stream not only to prevent fish kills but also to keep balance of ecosystem including human uses. Frequent clean up of the urban surface and CSO, installation of detention basin will be helpful. In the long run, it seems combined sewer system has be replaced with separate sewer system for more effective pollutant removal in the urban area.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.145-149
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• 2006

Building Information Modeling (BIM) is the process of integrating and re-using information generated and used throughout the lifecycle of a building. BIM may not be very different from other management methods in that it aims to minimize the economic loss and maximize the profit by designing, building, and maintaining a building better, faster, yet cheaper. The major difference between BIM and other methods is that BIM approaches the goal from a system point of view whereas other methods generally approaches the goal from a business management point of view. Since a project cannot succeed without considering both systematic and managerial aspects of a project, the line has been blurred these days. This paper explores a historical background of BIM and discusses the key technologies for successful implementation of BIM.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.