• Korea journal of population studies
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• v.28 no.2
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• pp.1-32
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• 2005

The optimum population of a society or country can be defined as 'the population growth path that maximizes the welfare level of the society over the whole generations of both the present and the future, under the paths allowed by its endowments of production factors such as technology, capital and labor'. Thus, the optimum size or growth rate of population depends on: (i) the social welfare function, (ii) the production function, and (iii)demographic economic interrelationship which defines how the national income is disposed into consumption(birth and education of children included) and savings on the one hand and how the demographic and economic change induced thereby, in turn, affect production capacities on the other. The optimum population growth path can, then, be derived in the process of dynamic optimization of (i) under the constraints of (ii) and (iii), which will give us the optimum population growth rate defined as a function of parameters thereof. This paper estimates the optimum population growth rate of Korea by: specifying (i), (ii), and (iii) based on the recent development of economic theories, solving the dynamic optimization problem and inserting empirical estimates in Korea as the parametric values. The result shows that the optimum path of population growth in Korea is around TFR=1.81, which is affected most sensitively, in terms of the size of the partial elasticity around the optimum path, by the cost of children, share of capital income, consumption rate, time preference, population elasticity of utility function, etc. According to a survey implemented as a follow up study, there are quite a significant variations in the perceived cost of children, time preference rate, population elasticity of utility across different socio-economic classes in Korea, which implied that, compared to their counterparts, older generation and more highly educated classes prefer higher growth path for the population of Korea.

• Journal of Pharmacopuncture
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• v.18 no.2
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• pp.7-18
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• 2015

Objectives: Lawsone (1,4 naphthoquinone) is a non redox cycling compound that can be catalyzed by DT diaphorase (DTD) into 1,2,4-trihydroxynaphthalene (THN), which can generate reactive oxygen species by auto oxidation. The purpose of this study was to evaluate the toxicity of the phytomarker 1,4 naphthoquinone and its metabolite THN by using the molecular docking program AutoDock 4. Methods: The 3D structure of ligands such as hydrogen peroxide ($H_2O_2$), nitric oxide synthase (NOS), catalase (CAT), glutathione (GSH), glutathione reductase (GR), glucose 6-phosphate dehydrogenase (G6PDH) and nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) were drawn using hyperchem drawing tools and minimizing the energy of all pdb files with the help of hyperchem by $MM^+$ followed by a semi-empirical (PM3) method. The docking process was studied with ligand molecules to identify suitable dockings at protein binding sites through annealing and genetic simulation algorithms. The program auto dock tools (ADT) was released as an extension suite to the python molecular viewer used to prepare proteins and ligands. Grids centered on active sites were obtained with spacings of $54{\times}55{\times}56$, and a grid spacing of 0.503 was calculated. Comparisons of Global and Local Search Methods in Drug Docking were adopted to determine parameters; a maximum number of 250,000 energy evaluations, a maximum number of generations of 27,000, and mutation and crossover rates of 0.02 and 0.8 were used. The number of docking runs was set to 10. Results: Lawsone and THN can be considered to efficiently bind with NOS, CAT, GSH, GR, G6PDH and NADPH, which has been confirmed through hydrogen bond affinity with the respective amino acids. Conclusion: Naphthoquinone derivatives of lawsone, which can be metabolized into THN by a catalyst DTD, were examined. Lawsone and THN were found to be identically potent molecules for their affinities for selected proteins.

• The Korean Journal of Food And Nutrition
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• v.29 no.3
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• pp.371-381
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• 2016

The present study investigated the effects of processing parameters such as time (10, 20, 30, 40 min), pressure (25, 50, 75, 100 MPa), and the salinity of brine (0~10%(w/v)) on jacopever (Sebastes schlegeli Hilgendorf) in order to establish optimization of the three factors using a high hydrostatic pressure (HHP) machine. To do so, it analyzed the quality characteristics of volatile basic nitrogen (VBN), trimethylamine (TMA), total bacterial counts, dynamic viscoelasticities, and differential scanning calorimetry (DSC) properties. First, when the time increased to 40 mins, by 10 min intervals, the total bacterial counts in HHP groups under $25^{\circ}C$, 100 MPa, and 4%(w/v) brine were significantly decreased except for the first 10 min in comparison to the control group. In regards to DSC properties, the onset temperature ($T_O$) of the first endothermal curve was significantly reduced. Second, when the pressure level increased up to 100 MPa by 25 MPa increments, the total bacterial counts in the HHP samples significantly decreased for 20 min at 50 MPa or higher. As the pressure increased, G', G" and the slope of tan ${\delta}$ decreased (except for 50 MPa). Third, in regards to the salinities of brine, when the HHP processing was treated at 100 MPa, $25^{\circ}C$ for 20 min, the total bacterial counts of all the HHP groups significantly decreased in comparison to those of the control group. A significant difference was found in the enthalpy of the second endothermic curve in the 6~10%(w/v) (except 7%(w/v)) HHP groups. Therefore, the salinity of the immersion water under the HHP condition was appropriate when it was lower than 6%(w/v). The present study demonstrated that the optimum parameter condition according to/under the condition of the microbial inhibition and economic effects using an HHP would be the reaction time for 20 min, reaction pressure at 100 MPa, and the salinity of 4%(w/v) brine.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.35-45
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• 2009

Emergency medical center(EMC) is the place for patients who need medical treatment immediately due to a disease, childbirth, or all sorts of accidents. Currently, most of EMCs use temporary beds because regular EMC beds cannot afford to serve all incoming patients. However, since it decreases the quality of service(QoS) of EMC patients and their guardians and efficiency of the EMC, some improvements are highly required to diminish the usage of temporary beds. The system duration time is one of the typical QoSs. This thesis proposes the information which is critical to make a better decision for cut down the number of temporary beds without sacrificing QoS of patients. The key point is to control the duration time of medical treatments for the consultation and hospitalization process, since it is the major reason of overcrowding in EMC and the usage of temporary beds. In this paper, we proposed an Arena simulation model reflecting real world substantially. Arena is one of the most widely accepted simulation softwares in the world. Using the developed model, we can obtain the optimal EMC operation parameters through simulation experiments. Optquest, included in the Arena, is used to make the developed simulation model collaborate with an optimization model. The results showed one can determine the set of optimal operation parameters decreasing the required number of temporary beds without deteriorating EMC patient's QoS.

• Journal of Korea Water Resources Association
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• v.41 no.8
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• pp.807-823
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• 2008

This paper presents a technique for estimating discharge rating curve parameters. In typical practical applications, the original non-linear rating curve is transformed into a simple linear regression model by log-transforming the measurement without examining the effect of log transformation. The model of pseudo-likelihood estimation is developed in this study to deal with heteroscedasticity of residuals in the original non-linear model. The parameters of rating curves and variance functions of errors are simultaneously estimated by the pseudo-likelihood estimation(P-LE) method. Simulated annealing, a global optimization technique, is adapted to minimize the log likelihood of the weighted residuals. The P-LE model was then applied to a hypothetical site where stage-discharge data were generated by incorporating various errors. Results of the P-LE model show reduced error values and narrower confidence intervals than those of the common log-transform linear least squares(LT-LR) model. Also, the limit of water levels for segmentation of discharge rating curve is estimated in the process of P-LE using the Heaviside function. Finally, model performance of the conventional log-transformed linear regression and the developed model, P-LE are computed and compared. After statistical simulation, the developed method is then applied to the real data sets from 5 gauge stations in the Geum River basin. It can be suggested that this developed strategy is applied to real sites to successfully determine weights taking into account error distributions from the observed discharge data.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Analytical Science and Technology
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• v.29 no.5
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• pp.219-224
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• 2016

불산 중 극미량 음이온의 고상추출과 이온크로마토그래프를 이용한 고감도 분석법이 개발되었다. 불산 중 불소이온이 고상에 의해 제거하였고 이어서 음이온 (F⁻, CH₃COO⁻, Cl⁻, Br⁻, NO₃⁻, PO₄³⁻, SO₄²⁻)들이 이온크로마토그래프를 이용하여 연속적으로 분리하였다. 고상 추출법에 영향을 주는 각 인자들 (흡착제의 선택, 시료의 부피 및 pH, 용출 용액과 용출용액의 부피)을 결정하였으며 그 결과 흡착제로서 Oasis WAX 컬럼이 가장 우수하였고 1.0 mL의 시료부피, 용출용액으로 50 mM 초산암모늄염 5 mL가 분리능에서 가장 우수하였다. 개발한 방법에 의한 음이온 (Cl⁻, Br⁻, NO₃⁻, PO₄³⁻, SO₄²⁻)들의 방법검출한계는 25 % 불산용액 (w/w) 중에 0.04~0.30 µg/L의 범위를 보였고 정밀도는 20.0와 40.0 µg/L의 농도에서 5 % 이내를 보였다. 한 제조회사에 의한 25 % 불산 중 음이온의 4.2에서 47.5 µg/L의 범위로 모두 검출되었다. 이 방법은 시험절차가 간단하고, 재현성 및 감도가 좋아서 반도체회사에서 불산 중 음이온 불순물을 정도 관리하는데 매우 유용한 방법이 될 것으로 판단된다.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.117-125
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• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Applied Chemistry for Engineering
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• v.31 no.4
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• pp.377-382
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• 2020

A sequential design of experiments was employed to optimize MOF-235 synthesis for acetylene adsorption process. Two experimental designs were applied: a two-level factorial design for screening and a central composite design, one of response surface methodologies (RSM). In this study, 2³ factorial design of experiment was used to evaluate the effect of parameters of synthesis temperature and time, and also mixing speed on crystallinity of MOF-235. Experiments were conducted 16 times follwing MINITAB 19 design software for MOF-235 synthesis. Half-normal, pareto, residual, main and interaction effects were drawn based on the XRD results. The analysis of variance (ANOVA) of test results depicts that the synthesis temperature and time have significant effects on the crystallinity of MOF-235 (response variable). After screening, a central composite design was performed to optimize the acetylene adsorption capacity of MOF-235 based on synthesis conditions. From nine runs designed by MINITAB 19, the result was calculated using the second order model equation. It was estimated that the maximum adsorption capacity (18.7 mmol/g) was observed for MOF-235 synthesized at optimum conditions of 86.3 ℃ and 28.7 h.