• Journal of the Korea Society for Simulation
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• v.8 no.2
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• pp.13-28
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• 1999

The main purpose of this paper is to design and implement forest fire information system (FOFIS) for effective prevention of forest fire using GIS, database, 3-D graphics, and simulation techniques. In contrast to conventional fire information systems that are mostly based on the 2-D graphics and analytic modeling approaches, we have proposed the cell-based modeling approaches, i.e., spatial, data, and simulation modeling approaches. The cell-based spatial modeling is proposed by eliminating the cliff effect of the typical elevation model so that it can provide realistic 3-D graphics of the forest fire. The cell-based data modeling of geography, meteorology, and forestry information is also proposed. The cell-based dynamic modeling for forecasting of the fire diffusion is developed using the variable structure modeling techniques. Several simulation tests of FOFIS performed on a sample forest area of Chungdo, Kyungsangbukdo will demonstrate our approaches.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.109-112
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• 2007

In this paper, a mathematical modeling was developed to simulate 1kW class air cooled Polymer Electrolyte Membrane Fuel Cell(PEMFC) system. The proposed modeling was conducted under SIMULINK based environment. The model ing was developed based on the thermodynamic and chemical equilibrium. The objective is to design and implement the entire fuel cell system model ing including the system controller modeling. The fuel cell process and the control system modeling should have to be connected with each other simultaneously， therefore the two types of modeling influences each other when the system simulator run. The fuel cell modeling libraries are simulated using the SIMULINK under the thermodynamic and chemical equilibrium base. The PID controller application was designed and developed to test the process modeling and verify it. This the prototype development of the fuel cell system to design and test more complicate fuel cell systems, like the residential power generation system. The simulation results was compared to the real PEMFC system performance. We have achieved the reasonable accordance with the Lab test and the simulation results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.127-130
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• 1998

In this paper, we propose a method of cooperative control(T-cell modeling) and selection of group behavior strategy(B-cell modeling) based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Geophysics and Geophysical Exploration
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• v.22 no.2
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• pp.56-61
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• 2019

In this paper, we studied efficient modeling strategies when we simulate the 3D time-domain acoustic wave propagation using a cell-based finite difference method which can handle the variations of both P-wave velocity and density. The standard finite difference method assigns physical properties such as velocities of elastic waves and density to grid points; on the other hand, the cell-based finite difference method assigns physical properties to cells between grid points. The cell-based finite difference method uses average physical properties of adjacent cells to calculate the finite difference equation centered at a grid point. This feature increases the computational cost of the cell-based finite difference method compared to the standard finite different method. In this study, we used additional memory to mitigate the computational overburden and thus reduced the calculation time by more than 30 %. Furthermore, we were able to enhance the performance of the modeling on several media with limited density variations by using the cell-based and standard finite difference methods together.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• Journal of Biomedical Engineering Research
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• v.34 no.4
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• pp.218-225
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• 2013

In this paper hair bundle feature model and integration method for hair cell models were proposed. The proposed hair bundle feature model was based on spring-damper-mass model. Input of integrated vestibular hair cell model was frequency and output was interspike interval of hair cell that was reflected the feature of hair bundles. Irregular afferents that had a great gain variation showed reduction of negative stiffness section. Regular afferents that had a small gain variation, however, showed same feature with base negative stiffness feature. As a result, integrated vestibular hair cell model showed almost the same modeling data with experimental data in the modeled eleven frequency bands. It is verified that the proposed model is a good model for hair bundle feature modeling.

• BMB Reports
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• v.48 no.5
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• pp.256-265
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• 2015

Cardiovascular and neurodegenerative diseases are major health threats in many developed countries. Recently, target tissues derived from human embryonic stem (hES) cells and induced pluripotent stem cells (iPSCs), such as cardiomyocytes (CMs) or neurons, have been actively mobilized for drug screening. Knowledge of drug toxicity and efficacy obtained using stem cell-derived tissues could parallel that obtained from human trials. Furthermore, iPSC disease models could be advantageous in the development of personalized medicine in various parts of disease sectors. To obtain the maximum benefit from iPSCs in disease modeling, researchers are now focusing on aging, maturation, and metabolism to recapitulate the pathological features seen in patients. Compared to pediatric disease modeling, adult-onset disease modeling with iPSCs requires proper maturation for full manifestation of pathological features. Herein, the success of iPSC technology, focusing on patient-specific drug treatment, maturation-based disease modeling, and alternative approaches to compensate for the current limitations of patient iPSC modeling, will be further discussed. [BMB Reports 2015; 48(5): 256-265]

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• Journal of the Korean Institute of Gas
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• v.10 no.2 s.31
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• pp.55-60
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• 2006

Detailed modeling of PEMFCs has been getting considerable interest for predicting the fuel cell performance and also for use in various systems engineering activities. While CFD-based equipment models provide detailed analyses of the performance, they are very time-consuming to develop and run. The computations become quite complex when such models have to be embedded into the flowsheet-level optimization of fuel cell systems. In this paper, we present results about building and using NN-based reduced order models for quickly and repetitively predicting the flow of reactants in a PEMFC manifold.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.9
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• pp.938-945
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• 2012

To design and estimate material failures of Type III pressure vessels, which have excellent stability and performance, various modeling techniques have been introduced. This paper provided a hybrid modeling technique composed of ply-based modeling for a cylinder part and laminate-base modeling technique for a dome part for enhancing modeling efficiency. The ply-based modeling technique provided accurate ply stresses directly for predicting material failure, on the other hand, additional manipulations in stress calculations, which may cause some errors, were needed for the case of the laminate-based modeling technique. The ply stresses in fiber, transverse and in-plane shear directions were compared with the corresponding material strengths to predict material failure.