• Journal of Korean Society for Atmospheric Environment
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• 제16권E2호
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• pp.97-103
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• 2000

A Thermal Internal Boundary Layer(TIBL) develops over the landside from the coast due to the surface temperature difference between the land the sea when sea breeze froms. The TIBL plays an important role in determining the pollutant concentrations where the plume emitted from a tall stack near the coast fumigates to the ground. The fumigation results in the high ground the TIBL height from the available meterological data is very important. The TIBL models avaliable in the literature were analyzed to identify the suitable model to apply in the fumigation. The TIBL heights predicted by the existing models were compared with the measurements in the water tank experiment. The results show that the TIBL models by Raynor is appropriate to predict the height of TIBL.

• 통상정보연구
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• 제4권2호
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• pp.97-119
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• 2002

This research purposes on comparing some e-trailing models in character and presenting the obstacles of e-trailing activation and the solutions, e-trailing models this research has studied are 1) Bolero, 2) TradeCard, 3) BeXcom, 4) Identrus, and 5) TEDI etc. Comparing characteristic points of the e-trading models are as follows ; 1) Access ways as a global e-trading model, 2) Structural characteristic, 3) Functional characteristic, 4) Role as a global e-trading model, 5) Legal issues, 6) Application issues in practice, 7) Security issues for Technology.

• 한국분무공학회지
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• 제9권3호
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• pp.42-49
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• 2004

The present article investigates the influence of droplet drag models on predictions of diesel spray behaviors under ultra-high injection pressure conditions. To consider drop deformation and shock disturbance, this study introduces a new hybrid model in predicting drag coefficient from the literature findings. Numerical simulations are first conducted on transient behaviors of single droplet to compare the hybrid model with earlier conventional model. Moreover, using two different models, extensive numerical calculations are made for diesel sprays under ultra-high pressure sprays. It is found that the droplet drag models play an important role in determining the transient behaviors of sprays such as spray tip velocity and penetration lengths. Numerical results indicate that this new hybrid model yields the much better conformity with measurements especially under the ultra-high injection pressure conditions.

• 한국실내디자인학회논문집
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• 제41호
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• pp.266-274
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• 2003

Synectics is one of several techniques used to enhance brainstorming by taking a more active role and introducing metaphor and structure into the process. It is unclear at what level of specificity this should be formulated as a pattern. This thesis reviews recent computational as well as experimental work on analogical reasoning based on synectics. New results regarding information processing of analogical reasoning stages, major computational models and recent attempts to compare these models are reviewed. Computational models are also discussed in the computational as well as cognitive psychology perspectives. Future directions in analogical reasoning research are proposed. The following import is the need to accommodate the typology and normal assessment in the concrete circumstances where actual reasoning and problem solving take place. In order to get to this end, we used computational models by Thagard who take the stand of ‘Computational Philosophy of Science’, which assumes ‘Weak AI’ to explicate what constitute the very pecularity of Analogical Reasoning.

• 한국전산유체공학회지
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• 제14권2호
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• pp.46-51
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• 2009

The slip effect from the molecular interaction between fluid particles and solid surface atoms plays a key role in microscale fluid transport and heat transfer since the relative importance of surface forces increases as the size of the system decreases to the microscale. There exist two models to describe the slip effect: the Maxwell slip model in which the slip correction is made on the basis of the degree of shear stress near the wall surface and the Langmuir slip model based on a theory of adsorption of gases on solids. In this study, as the first step towards developing a general purpose numerical code of the compressible Navier-Stokes equations for computational simulations of microscale fluid flow and heat transfer, two slip models are implemented into a finite element numerical code of a simplified equation. In addition, a pressure-driven gas flow in a microchannel is investigated by the numerical code in order to validate numerical results.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2007년도 동계학술발표대회 논문집
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• pp.34-39
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• 2007

Condensing gas boiler units may make a big role for the reduction of energy consumption in heating industries. In order to decrease the energy consumption of a condensing gas boiler unit, the effective operations and controls of the system are necessary. In this study, mathematical models of a condensing gas boiler system were developed and programmed in order to predict dynamic behaviors of the system. These include dynamic models for a blower, a gas valve, a pump, a burner, a boiler heat exchanger, and a hot water heat exchanger. Control algorithms for the control of a gas valve, a blower, and a pump were also assumed. Simulation results showed good predictions of the dynamic phenomena of a boiler system. Therefore, the simulation program developed for this study may be effectively used for the development of control algorithms of the boiler system.

• Advances in Computational Design
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• 제8권1호
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• pp.37-59
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• 2023

Squat reinforced concrete (SRC) shear walls are a critical part of the structure for both office/residential buildings and nuclear structures due to their significant role in withstanding seismic loads. Despite this, empirical formulae in current design standards and published studies demonstrate a considerable disparity in predicting SRC wall shear strength. The goal of this research is to develop and evaluate hybrid and ensemble artificial neural network (ANN) models. State-of-the-art population-based algorithms are used in this research for hybrid intelligence algorithms. Six models are developed, including Honey Badger Algorithm (HBA) with ANN (HBA-ANN), Hunger Games Search with ANN (HGS-ANN), fitness-distance balance coyote optimization algorithm (FDB-COA) with ANN (FDB-COA-ANN), Averaging Ensemble (AE) neural network, Snapshot Ensemble (SE) neural network, and Stacked Generalization (SG) ensemble neural network. A total of 434 test results of SRC walls is utilized to train and assess the models. The results reveal that the SG model not only minimizes prediction variance but also produces predictions (with R²= 0.99) that are superior to other models.

• 대한임베디드공학회논문지
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• 제18권5호
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.89.3-89
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data. A generalized regression neural network (GRNN) [I] is one of the architectures that have been widely used to analyze complex chemical data. I...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.46.2-46
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data [1]. Among many types of networks, a backpropagation neural network (BPNN) is the most widely used architecture. Many training variables are...