• Journal of the Korean Geographical Society
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• v.47 no.4
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• pp.606-624
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• 2012

The main objective of this paper is to provide an adequate and comprehensive review of what has been done in South Korea in the field of geospatial analysis and modeling. This review focuses on spatial data analysis and spatial statistics, spatial optimization, and geosimulation among various aspects of the field. It is recognized that geospatial analysis and modeling in South Korea got through the initial stage during the 1990s when computer and analytical cartography and GIS were introduced, moved to the growth stage during the first decade of the $21^{st}$ century when there was a surge of relevant researches, and now is heading for its maturity stage. In spatial data analysis and spatial statistics, various topics have been addressed for spatial point pattern data, areal data, geostatistical data, and spatial interaction data. In spatial optimization, modeling and applications related to facility location problems, districting problems, and routing problems have been mostly researched. Finally, in geosimulation, while most of research has focused on cellular automata, studies on agent-based model and simulation are in beginning stage. Among all these works, some have fostered methodological advances beyond simple applications of the standard techniques.

• KDI Journal of Economic Policy
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• v.12 no.4
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• pp.3-20
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• 1990

The multisector model is designed to analyze and forecast structural change in industrial output, employment, capital and relative price as well as macroeconomic change in aggregate income, interest rate, etc. This model has 25 industrial sectors, containing about 1,300 equations. Therefore, this model is characterized by detailed structural disaggregation at the sectoral level. Individual industries are based on many of the economic relationships in the model. This is what distinguishes a multisector model from a macroeconomic model. Each industry is a behavioral agent in the model for industrial investment, employment, prices, wages, and intermediate demand. The strength of the model lies in the simulating the interactions between different industries. The result of its simulation will be introduced in the next paper. In this paper, we only introduce the structure of the multisector model and the coefficients of the equations. The multisector model is a dynamic model-that is, it solves year by year into the future using its own solutions for earlier years. The development of a dynamic, year-by-year solution allows us to combine the change in structure with a consideration of the dynamic adjustment required. These dynamics have obvious advantages in the use of the multisector model for industrial planning. The multisector model is a medium-term and long-term model. Whereas a short-term model can taken the labor supply and capital stock as given, a long-term model must acknowledge that these are determined endogenously. Changes in the medium-term can be analyzed in the context of long-term structural changes. The structure of this model can be summarized as follow. The difference in domestic and world prices affects industrial structure and the pattern of international trade; domestic output and factor price affect factor demand; factor demand and factor price affect industrial income; industrial income and relative price affect industrial consumption. Technical progress, as measured in terms of total factor productivity and relative price affect input-output coefficients; input-output coefficients and relative price determine the industrial input cost; input cost and import price determine domestic price. The differences in productivity and wage growth among different industries affect the relative price.

• KSBB Journal
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• v.21 no.3
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• pp.224-229
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• 2006

FEMLAB is a powerful interactive environment for modeling, solving all kinds of scientific and engineering problems based on partial differential equations(PDEs). Separation process of chiral compound in HPLC columns was simulated by FEMLAB. To study change of elution profile with isotherm models, non-competitive and competitive Langmuir adsorption isotherm were adopted. Separated material was (R, S)-ibuprofen [(R, S)-2-(4-isobutyl phenyl) propionic acid], an anti-inflammatory agent, which retain the pharmacological activity in the (S)-(+)-enantiomer. Sample concentrations were changed from 0.5 mg/ml to 2.0 mg/ml at a flow rate of 1 ml/min and flow rate varied from 1 ml/min to 3 ml/min at an ibuprofen concentration of 2.0 mg/ml and $20{\mu}l$ of injection volume. Simulated results were well fitted with experimental data.

• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.341-349
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• 2002

The target controlled infusion(TCI) pump system is a logical approach to the development of improved administration techniques of an intravenous anaesthetic agent. The principle of TCI system is based on an understanding of the pharmacokinetic properties, three or four compartment model. The TCI system is optimal and flexible control of the plasma drug concentration. But the clinical goal is always to achieve a therapeutic drug effect, not a therapeutic concentration. So we developed the algorithm to target the concentration at the site of drug effect rather than the concentration in the plasma. If impulse drug is inputted into body, the decline of plasma concentration with time is shown, resulting in the expression of the differential equation. Therefore, we must reformulate our three-compartment model as four-compartment model with the effect compartment. And we tested plasma targeting and effect targeting algorithm by computer simulation using four-compartment model. So we developed the TCI capable of applying all intravenous drugs by adjusting individual pharmacokinetic parameters independently.