• Korean Journal of Clinical Pharmacy
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• v.3 no.1
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• pp.15-20
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• 1993

The ability to precisely predict serum digoxin concentration using 7 published methods in a group of 50 patients was undertaken. Two methods of estimating creatinine clearance and two estimates of lean body weight were employed as input variables using the 7 dosing methods. TDX was used to determine the nadir SDCs(serum digoxin concentrations) in 50 in patients meeting predetermined study criteria. All patients, whose ages ranged 19-71 years, had steady-state digoxin levels, were in oral digoxin, and were free from liver dysfunction, thyroid dysfunction and renal failure. The correlation coefficients(r) of predicted versus observed SDCs were determined,. and mean error(ME) was determined for each method to reflect bias, respectively. No substantial differance in predictive reliabliity was evident among the methods studied in total group. Poor correlations existed between predicted and observed SDCs(r<0.4) and these correlations were not significantly affected by age and gender. But relatively higher correlation and lower ME was founded for the CHF group in Jelliffe method(r=0.5, p<0.05).

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.180-180
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• 2023

To improve global risk management, understanding the characteristics and distribution of precipitation is crucial. However, obtaining spatially and temporally resolved climatic data remains challenging due to sparse gauge observations and limited data availability, despite the use of satellite and reanalysis products. To address this challenge, merging available precipitation products has been introduced to generate spatially and temporally reliable data by taking advantage of the strength of the individual products. However, most of the existing studies utilize all the available products without considering the varying performances of each dataset in different regions. Comprehensively considering the relative contributions of each parent dataset is necessary since their contributions may vary significantly and utilizing all the available datasets for data merging may lead to significant data redundancy issues. Hence, for this study, we introduce a site-specific precipitation merging method that utilizes the Quadruple Collocation (QC) approach, which acknowledges the existence of error-cross correlation between the parent datasets, to create a high-resolution global daily precipitation data from 2001-2020. The performance of multiple gridded precipitation products are first evaluated per region to determine the best combination of quadruplets to be utilized in estimating the error variances through the QC approach and computation of merging weights. The merged precipitation is then computed by adding the precipitation from each dataset in the quadruplet multiplied by each respective merging weight. Our results show that our approach holds promise for generating reliable global precipitation data for data-scarce regions lacking spatially and temporally resolved precipitation data.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.244-253
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• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.82-96
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• 2019

A semi-submersible structure has been widely used for offshore drilling and production of oil and gas. The small water plane area makes the structure very sensitive to weight increase in terms of payload and stability. Therefore, it is necessary to lighten the substructure from the early design stage. This study aims at an optimization of hull structure based on a sophisticated yield and buckling strength in accordance with classification rules. An in-house strength assessment system is developed to automate the procedure such as a generation of buckling panels, a collection of required panel information, automatic buckling and yield check and so on. The developed system enables an automatic yield and buckling strength check of all panels composing the hull structure at each iteration of the optimization. Design variables are plate thickness and stiffener section profiles. In order to overcome the difficulty of large number of design variables and the computational burden of FE analysis, various methods are proposed. The steepest descent method is selected as the optimization algorithm for an efficient search. For a reduction of the number of design variables and a direct application to practical design, the stiffener section variable is determined by selecting one from a pre-defined standard library. Plate thickness is also discretized at 0.5t interval. The number of FE analysis is reduced by using equations to analytically estimating the stress changes in gradient calculation and line search steps. As an endeavor to robust optimization, the number of design variables to be simultaneously optimized is divided by grouping the scantling variables by the plane. A sequential optimization is performed group by group. As a verification example, a central column of a semi-submersible structure is optimized and compared with a conventional optimization of all design variables at once.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.144-160
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• 2022

Research on urban air mobility (UAM) is being actively conducted as a method of next-generation transportation. eVTOL, an airplane to be used for urban air mobility, is classified into a complex type, a tilt rotor type, a tilt wing type, a tilt duct fan type, and a multicopter type according to the propulsion method. In this study, conceptual design was performed for the next generation eVTOL of the new tilt rotor type in accordance with the existing design requirements. The aerodynamic analysis programs of OpenVSP and XFLR5 were used to perform aerodynamic analysis. The power required for each flight mission stage was calculated, the battery and motor were selected accordingly, and MTOW (Maximum Take-Off Weight) was predicted by estimating the weight of each component.

• Fisheries and Aquatic Sciences
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• v.27 no.6
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• pp.392-409
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• 2024

The present study aims to analyze the biological aspects and population dynamics of Indian mackerel in Barru waters. Data was collected in Barru for 11 months, from June 2022 to April 2023. The observed parameters of biological aspects included gonadal maturation stages (GMSs), size at first gonadal maturation, and length-weight relationship. Meanwhile, the aspects of population dynamics encompass age group, growth, mortality rate, and exploitation rate. Data analysis consisted of morphological selection of general maturation stages, Spearman-Kärber method in estimating gonadal first maturation size, Bhattacharya method in identifying age group, von Bertalanffy function through FISAT II to measure growth (L_∞ and K), Pauly Model to estimate mortality rate, Beverton & Holt Model to estimate Y/R, and virtual population analysis (VPA) analysis to estimate stock and fish yield. The results demonstrated that GMS I was observed to be dominant, followed by stages II and III. The initial gonadal maturation was estimated to be 17.98-19.28 cm (FL) for females and 17.98-19.27 cm (FL) for males. The length-weight relationship in male and female Indian mackerels indicated a positive allometric growth. The mode grouping analysis results from the fork length measurement revealed three age groups. It was also identified that the asymptotic length (L_∞) = 29.5 cm (fork length), growth rate coefficient (K) = 0.46 per year, and theoretical age at zero length (t₀) = -0.3576 per year. Total mortality (Z) = 2.67 per year, natural mortality (M) = 1.10 per year, fishing mortality (F) = 1.57 per year, and exploitation rate (E) = 0.59, the actual Y/R = 0.083 gram/recruitment, and optimal Y/R 0.03 gram/recruitment. Fishing mortality is higher than the natural mortality rate, and a high exploitation value (E > 0.5) also reflects over-exploitation. VPA analysis on fish yields and stock estimation reported a highly exploited rate between the 11.5 cm and 14.5 cm length classes and an exceeding current yield of 467.07 tons/year with a recommended yield of 233.53 tons/year to ensure population sustainability.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.1
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• pp.50-56
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• 2020

Determination of the precise size at sexual maturity is very important for science-based stock assessment and fisheries resource management. In this study, two different models, (1) a visual method and (2) a gonadosomatic index (GSI) method, were employed to estimate length at sexual maturity of the small yellow croaker Larimichthys polyactis in the Yellow Sea of Korea. The visual method is a common qualitative method using visual gonadal identification. Conversely, the GSI method is a quantitative method using the GSI, which can be easily and precisely collected. We compared results from these methods to determine the best approach, and to examine the practicality of the GSI method. Logistic regression of the maturity ogive was conducted using a general linear model (GLM) with the R statistics program. Also, the bootstrapped 95% confidence intervals of all estimates were calculated. The best-fit model was the visual method (R_Mc²=0.805, AUC=0.989, L₅₀=15.1). Among models using the GSI method, the model computing GSI_ref=0.94 was the best-fit model (R_Mc²=0.792, AUC=0.989, L₅₀=15.2). There was no significant difference between the two models, evidencing the effectiveness and accuracy of the GSI method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• Journal of Ecology and Environment
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• v.35 no.3
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• pp.243-249
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• 2012

Biomass expansion factors, which convert timber volume (or dry weight) to biomass, are used for estimating the forest biomass and accounting for the carbon budget at a regional or national scale. We estimated the biomass conversion and expansion factors (BCEF), biomass expansion factors (BEF), root to shoot ratio (R), and ecosystem biomass expansion factor (EBEF) for Quercus mongolica Fisch. and Quercus variabilis Bl. forests based on publications in Korea. The mean BCEF, BEF, and R for Q. mongolica was 1.0383 Mg/$m^3$ (N = 27; standard deviation [SD], 0.5515), 1.3572 (N = 27; SD, 0.1355), and 0.2017 (N = 32; SD, 0.0447), respectively. The mean BCEF, BEF, and R for Q. variabilis was 0.7164 Mg/$m^3$ (N = 17; SD, 0.3232), 1.2464 (N = 17; SD, 0.0823), and 0.1660 (N = 8; SD, 0.0632), respectively. The mean EBEF, as a simple method for estimating the ground vegetation biomass, was 1.0216 (N = 7; SD, 0.0232) for Q. mongolica forest ecosystems, and 1.0496 (N = 8; SD, 0.0725) for Q. variabilis forest ecosystems. The biomass expansion factor values in this study may be better estimates of forest biomass in Q. mongolica or Q. variabilis forests of Korea compared with the default values given by the Intergovernmental Panel on Climate Change (IPCC).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1693-1705
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• 2013

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.