• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.181-191
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• 2014

Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.281-289
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• 2006

Advanced controllers among Flexible AC Transmission System(FACTS) devices employ self-commutated switching converters, VSI (Voltage Sourced Inverters), as the synchronous voltage source. Such controllers are SSSC (Static Synchronous Series Compensator), STATCOM (Static Synchronous Compensator) and UPFC (Unified Power Flow Controller). UPFC is series-shunt combined controller. Its series and shunt inverters can be modeled as SSSC and STATCOM but the dependant relation between the inverters is very complex. For that reason, the complexity makes it difficult to develop the UPFC model by simply combining the SSSC and STATOM models when we apply the model for conventional power system dynamic simulation algorithm. Therefore, we need each relevant models of VSI type FACTS devices for power system analysis. This paper proposes a modeling approach which can be applied to modeling of VSI type FACTS devices. The proposed method using Newton-type current injection method can be used to make UPFC, SSSC, and STATCOM models. The proposed models are used for 2-area test system simulation, and the results verify their effectiveness.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.259-265
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• 2018

Compartment models, a type of mathematical model, have been widely applied to characterize the changes in a dynamic system with sequential events or processes, such as the spread of an epidemic disease. A compartment model comprises compartments, and the relations between compartments are depicted as boxes and arrows. This principle is similar to that of the system dynamics (SD) approach to constructing a simulation model with stocks and flows. In addition, both models are structured using differential equations. With this mutual and translatable principle, this study, in terms of SD, translates a reference SEIR model, which was developed in a recent study to characterize the transmission of the Middle East respiratory syndrome (MERS) in South Korea. Compared to the replicated result of the reference SEIR model (Model 1), the translated SEIR model (Model 2) demonstrates the same simulation result (error=0). The results of this study provide insight into the application of SD relative to constructing an epidemic compartment model using schematization and differential equations. The translated SD artifact can be used as a reference model for other epidemic diseases.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.625-633
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• 2015

The effect of altitude on thermal conduction, surface temperature, and thermal radiation of partial arc was investigated on the basis of molecular gas dynamics to facilitate a deep understanding of the pollution surface discharge mechanism. The DC flashover model was consequently modified at high altitude. The validity of the modified DC flashover model proposed in this paper was proven through a comparison with the results of high-altitude simulation experiments and earlier models. Moreover, the modified model was found to be better than the earlier modified models in terms of forecasting the flashover voltage. Findings indicated that both the thermal conduction coefficient and the surface thermodynamics temperature of partial arc had a linear decrease tendency with the altitude increasing from 0 m to 3000 m, both of which dropped by approximately 30% and 3.6%, respectively. Meanwhile, the heat conduction and the heat radiation of partial arc both had a similar linear decrease of approximately 15%. The maximum error of DC pollution flashover voltage between the calculation value according to the modified model and the experimental value was within 6.6%, and the pollution flashover voltage exhibited a parabola downtrend with increasing of pollution.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2222-2229
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• 2023

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.175-180
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• 1993

The nonlinear integro-differential equations of motion of a two-link structurally flexible planar manipulator executing contact tasks are presented. The equations of motion are derived using the extended Hamilton's principle and the Galerkin criterion. Also, Models for the wrist-force sensor and impact that occurs when the manipulator's end point makes contact withthe environment are presented. The dynamic models presented can be used to studythe dynamics of the system and to design controllers.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.31-44
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• 1993

Zeigler's DEVS(Discrete Event Systems Specification) formalism supports formal specification of discrete event systems in a hierarchical , modular manner. Associated are hierarchical, distributed simulation algorithms, called abstract simulators, which interpret dynamics of DEVS models. This paper deals with performance evaluation of P-DEVSIM ++, a parallel simulation environment which implements the DEVS formalism and associated simulation algorithms in a parallel environment. Performance simulator has been developed and used to experiment models of parallel simulation executions in different conditions. The experimental result shows that simulation time depends on both the number of processors in the parallel system and the communication overheads among such processors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1009-1012
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• 1993

An association memory, solving an optimization problem, a Boltzmann machine scheme learning and a back propagation learning in our chaos neuron models are reviewed and some new results are presented. In each model its microscopicrule (a parameter of a chaos system in a neuron) is subject to its macroscopic state. This feedback and chaos dynamics are essential mechanisms of our model and their roles are briefly discussed.

• Journal of applied mathematics & informatics
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• v.32 no.5_6
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• pp.555-566
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• 2014

In this paper, we formulate two chemostat type models of phytoplankton and zooplankton population dynamics with instant nutrient recycling to study the role of viral infection on phytoplankton. The infection is transmitted only among phytoplankton population and it makes them more vulnerable to predation by zooplankton. It is observe that the chemostat system is very stable in the absence of viral infection but the presence of viral infection make the chemostat system sensitive with respect to the grazing rate of infected-phytoplankton by zooplankton. Further, if the grazing rate is less than certain threshold the system remain stable and exhibits Hopf-bifurcation after crossing it.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.475-486
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• 2004

The interaction of HIV and human immune system was studied in the perspective of dynamics. We summarized the recent researches on drug scheduling using optimal control theory for HIV treatment. The drug treatment to make immune system to work properly is investigated based on mathematical models including memory CTLp. In the simulation results, it was verified that stopping medication after a certain period of treatment can lead a patient to be cured naturally by one s immune system. Also, we summarized and categorized the advantages and disadvantages of each HIV drug scheduling method. In conclusion, model-based predictive control is more efficient for making decision of drug dose than other methods, when there exist uncertainties on model parameters or state variables.