• Journal of Korean Society for Atmospheric Environment
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• v.17 no.1
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• pp.1-17
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• 2001

The high-ozone episode in the Greater Seoul Area for the period of July 27 to August 1 1997 was modeled by the CIT(California Institute of Technology) three-dimensional photochemical model. Emission data were prepared by scaling the NIER(1994) data through and optimization method using VOC measurements in August 1997 and EKMA(Empirical Kinetic Modeling Approach). Two sets of meteorological data were prepared by the diagnostic routine. a part of the CIT model : one only utilized observations from the surface weather stations and the other also utilized observations from the automatic weather stations that were more densely distributed than those from the surface weather stations. The results showed that utilizing observations from the automatic weather stations could represent fine variations in the sind field such as those caused by topography. A better wind field gave better peak ozones and a more reasonable spatial distribution of ozone concentrations. Nevertheless, there were still many differences between predictions and observations particularly for primary pollutant such as NOx and CO. This was probably due to the inaccuracy of emission data that could not resolve both temporal and spatial variations.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.6
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• pp.127-136
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• 2006

Prediction of diesel particulate filtration is typically made by virtue of modeling of particulate matter(PM) collection. The model is closed with filtration parameters reflecting all small scale phenomena associated with PM trapping, and these parameters are to be traced back by inversely analyzing large-scale empirical data-the pressure drop histories. Included are soot cake permeability, soot cake density, soot density in the porous filter wall, and percolation constant. In the present study, a series of single channel DPF experiment is conducted, pressure histories are inversely analyzed, and the essential filtration parameters are deducted by DPF filtration model formulated with non-linear description of soot cake regression. Sensitivity analyses of model parameters are also made. Results showed that filtration transients are significantly altered by the extent of percolation constant, and the soot density in the porous filter wall is controlling the filtration qualities in deep-bed filtration regime. In addition, effect of soot particle size on filtration quality is distinct in a period of soot cake regime.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.373-386
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• 2014

Purpose: This paper seeks to analyze the relationship between project sponsor roles and project manager competencies which may account for project performance quality. Methods: After an extensive literature review, the structural equation modeling was conducted test the research model. The data was collected from project manager training course attendees by the questionnaire. Results: The results of structural equation modeling show that project sponsor roles effect on project manager competencies. Project manager competencies have a positive effect on project performance quality. Conclusion: The authors demonstrate that project sponsor external roles are related positively to project performance quality. And project manager professionalism has a positive effect on project manager communication. A number of significant findings are reported including the empirical verification that project sponsor roles, and project manager competencies may all be directly related to project performance quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.3
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• pp.416-420
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• 2002

Traditional queueing analyses are very useful for designing a network's capacity and predicting there performances, however most of the predicted results from the queueing analyses are quite different from the realistic measured performance. And recent empirical studies on LAN, WAN and VBR traffic characteristics have indicated that the models used in the traditional Poisson assumption can't properly predict the real traffic properties due to under estimation of the long range dependence of network traffic and self-similarity In this parer self-similar characteristics over statistical approaches and real time network traffic measurements are estimated It is also shown that the self- similar traffic reflects network traffic characteristics by comparing source model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.1
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• pp.167-175
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• 1992

This paper reports the application study of the bedding reinforcement under a buried pipeline subjected to differential settlement. Three different field conditions have been considered and evaluated via a finite element modeling. The deformation of a buried pepeline has been evaluated for each boundary condition together with the settlement restraining effects of the bedding reinforcement. A guideline of the bedding reinforcement design has been proposed so as to put it to practical use for general field applications. The design guideline incorporates the procedure and method for the selection of typical sections suggested by conventional empirical approach and for the determination of bedding thickness based on the numberical analysis results performed in this research.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Structural Engineering and Mechanics
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• v.68 no.4
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• pp.423-442
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• 2018

In this paper, masonry infilled reinforced concrete (RC) frames are analyzed through a probabilistic approach. A macro-modeling technique, based on an equivalent diagonal pin-jointed strut, has been resorted to for modelling the stiffening contribution of the masonry panels. Since it is quite difficult to decide which mechanical characteristics to assume for the diagonal struts in such simplified model, the strut width is here considered as a random variable, whose stochastic characterization stems from a wide set of empirical expressions proposed in the literature. The stochastic analysis of the masonry infilled RC frame is conducted via the Probabilistic Transformation Method by employing a set of space transformation laws of random vectors to determine the probability density function (PDF) of the system response in a direct manner. The knowledge of the PDF of a set of response indicators, including displacements, bending moments, shear forces, interstory drifts, opens an interesting discussion about the influence of the uncertainty of the masonry infills and the resulting implications in a design process.

• Geomechanics and Engineering
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• v.22 no.6
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• pp.519-528
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• 2020

A mathematical wetting model is usually used to predict the deformation of core wall rockfill dams induced by the wetting effect. In this paper, a series of wetting triaxial tests on a rockfill was conducted using a large-sized triaxial apparatus, and the wetting deformation behavior of the rockfill was studied. The wetting strains were found to be related to the confining pressure and shear stress levels, and two empirical equations, which are regarded as the proposed mathematical wetting model, were proposed to express these properties. The stress and deformation of a core wall rockfill dam was studied by using finite element analysis and the proposed wetting model. On the one hand, the simulations of the wetting model can estimate well the observed wetting strains of the upstream rockfill of the dam, which demonstrated that the proposed wetting model is applicable to express the wetting deformation behavior of the rockfill specimen. On the other hand, the simulated additional deformation of the dam induced by the wetting effect is thought to be reasonable according to practical engineering experience, which indicates the potential of the model in dam engineering.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.662-674
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• 2017

This paper describes the multidisciplinary design optimization (MDO) process of a tailless unmanned combat aerial vehicle (UCAV) using global variable fidelity aerodynamic analysis. The developed tailless UAV design framework combines multiple disciplines that are based on low-fidelity and empirical analysis methods. An automated high-fidelity aerodynamic analysis is efficiently integrated into the MDO framework. Global variable fidelity modeling algorithm manages the use of the high-fidelity analysis to enhance the overall accuracy of the MDO by providing the initial sampling of the design space with iterative refinement of the approximation model in the neighborhood of the optimum solution. A design formulation was established considering a specific aerodynamic, stability and control design features of a tailless aircraft configuration with a UCAV specific mission profile. Design optimization problems with low-fidelity and variable fidelity analyses were successfully solved. The objective function improvement is 14.5% and 15.9% with low and variable fidelity optimization respectively. Results also indicate that low-fidelity analysis overestimates the value of lift-to-drag ratio by 3-5%, while the variable fidelity results are equal to the high-fidelity analysis results by algorithm definition.

• The Journal of Industrial Distribution & Business
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• v.11 no.10
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• pp.7-16
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• 2020

Purpose: It suggests that making a policy and strategies in a way of AI and its impact of commercialization on economic efficiency, social custom ethics. Research design, data, and methodology: The paper has analyzed the data based on the proposed model when derived as AI vs. FI job, etc. It is very different for each professional evaluation, which is artificial intelligence or robot job. One concept case was selected as a substitute job, with a relatively low level of occupation ability, such as direct labors, easily replaced. By the induction data has resulted in modeling. Results: The paper suggests that AI at high level become something how to make real decisions on ethical value modeling. Through physical simulation with the deduction data, it can be tuned to design and control what has not been solved, from human senses to climate. Conclusion: For the exploiting of new AI decision-making jobs in markets, the deduction data is possible to prove to AI's Decision-making that the percentage who can easily have different leadership as is different for each person. what is generated by some information silos may be applied to occupation societies. The empirical results indicate the deduction data that if AI determines ethical decisions (VC) for that modifications, it may replace future jobs.