• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• Journal of the Microelectronics and Packaging Society
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• v.22 no.1
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• pp.75-81
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• 2015

In this paper, numerical analysis by finite element method and parameter design by the Taguchi method were used to reduce warpage of a two passive components embedded double side substrate for PoP(Package on Package). The effect of thickness of circuit layers (L1, L2) and thickness of solder resist (SR_top, SR_BTM) were analyzed with 4 variations and 3 levels(minimum, average and maximum thickness) to find optimized thickness conditions. Also, paste effect of solder resist on unit area of top surface was analyzed. Finally, experiments was carried out to prove numerical analysis and the Taguchi method. Based on the numerical and experimental results, it was known that circuit layer in ball side of substrate was the most severe determining deviation for reducing warpage. Buried circuit layer in chip side, solder resist and were insignificant effects on warpage relatively. However, warpage decreased as circuit layer in ball side thickness increased but effect of solder resist and circuit layer in chip side thickness were conversely.

• Korean Journal of Agricultural Science
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• v.40 no.4
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• pp.339-345
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• 2013

The objective of this study was to establish genetic evaluation systems with carcass data collected by 68 individual farms from 2007 to 2011 in Pyeongchang area of Kangwon province. All the possible of environment effects were corrected by analysis of variance (ANOVA) to estimate more accurate genetic parameters. Heritabilities and genetic correlations were estimated from carcass data collected from Hanwoo steers(n=10,441) born in Pyeongchang region from 2005 to 2008. Traits evaluated included carcass weight (CWT), eye muscle area (EMA), back fat thickness (BF) and marbling score (MS). As for the mean value and standard deviation for carcass traits, CWT, EMA, BF and MS were 424.5, 92, 13.7 and 5.7. Parameters were estimated using a multiple trait animal model and derivative-free restricted maximum likelihood procedures. Estimated heritabilities for CWT, EMA, BF and MS were 0.30, 0.21, 0.42 and 0.42, respectively. Genetic correlation of CWT with EMA, BF and MS were estimated to 0.24, 0.36 and 0.07, respectively. Genetic correlation of EMA with BF and MS was -0.27 and 0.61, respectively.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.2
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• pp.99-105
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• 2012

Purpose : This study is designed to compare two parameters reflecting $^{18}F$-FDG uptake, SUV and radioactivity, for diagnosis of thyroid cancer in dual time $^{18}F$-FDG PET/CT imaging and to find which parameter is more useful to decide whether the tumor is malignant or not. Materials and Methods : We performed retrospective study for 40 patients. All patients are diagnosed as primary thyroid cancer and examined $^{18}F$-FDG PET/CT. First, we got the dispersion of scattering beam of neck and lung apex to set a background and compared each dispersion, mean value, standard deviation of maxSUV and radioactivity. Also, mean maxSUV, ${\Delta}maxSUV$, ${\Delta}maxBq$/ml(%) and radioactivity between groups according to lesion's size based on biopsy are compared with independent-sample t-test. Results : the values that were from maxSUV and radioactivity measurement technique were compensated and calculated to practical values for mean comparison and patients were divided to two groups based on tumor size, Group1 ($size{\leq}1$ cm, n=21), Group2 (size>1 cm, n=19) for accurate comparison. In Group1, maxSUV (semi-quantitative analysis) was increased from $5.64{\pm}5.85$ (1.89~17.84) at first image to $5.90{\pm}5.01$ (1.95~18.22) at second image and radioactivity (Bq/ml) (quantitative analysis) showed similar increase from $5.93{\pm}6.38$ (2.50~16.75) at first image to $6.01{\pm}5.25$ (2.66~16.58) at second image. In Group2, TFmaxSUV was $10.54{\pm}14.36$ (2.54~33.89) in true first image, TSmaxSUV was $9.85{\pm}12.88$ (2.62~26.20) in true second image separately. The maxSUV showed a significant difference in the mean comparison between the two groups (p=0.035) But, mean radioactivity (Bq/ml) was $5.93{\pm}6.38$ (4.81~40.99) in true first image, $6.01{\pm}5.25$ (4.51~36.93) in true second image and didn't show a significant difference statistically (p=0.126) Conclusion : In diagnosis of thyroid tumor, SUV and radioactivity depending on $^{18}F$-FDG uptake showed high similarity with coefficient of determination (R2=0.939) and malignant evaluation results using dual time also showed similar aspect. Radioactivity for evaluation of malignant tumor didn't show better specificity or sensitivity than maxSUV.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.35-40
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• 2011

While high-definition precipitation maps with a 270 m spatial resolution are available for South Korea, there is little information on geospatial availability of precipitation water for the famine - plagued North Korea. The restricted data access and sparse observations prohibit application of the widely used PRISM (Parameter-elevation Regressions on Independent Slopes Model) to North Korea for fine-resolution mapping of precipitation. A hybrid method which complements the PRISM grid with a sub-grid scale elevation function is suggested to estimate precipitation for remote areas with little data such as North Korea. The fine scale elevation - precipitation regressions for four sloping aspects were derived from 546 observation points in South Korea. A 'virtual' elevation surface at a 270 m grid spacing was generated by inverse distance weighed averaging of the station elevations of 78 KMA (Korea Meteorological Administration) synoptic stations. A 'real' elevation surface made up from both 78 synoptic and 468 automated weather stations (AWS) was also generated and subtracted from the virtual surface to get elevation difference at each point. The same procedure was done for monthly precipitation to get the precipitation difference at each point. A regression analysis was applied to derive the aspect - specific coefficient of precipitation change with a unit increase in elevation. The elevation difference between 'virtual' and 'real' surface was calculated for each 270m grid points across North Korea and the regression coefficients were applied to obtain the precipitation corrections for the PRISM grid. The correction terms are now added to the PRISM generated low resolution (~2.4 km) precipitation map to produce the 270 m high resolution map compatible with those available for South Korea. According to the final product, the spatial average precipitation for entire territory of North Korea is 1,196 mm for a climatological normal year (1971-2000) with standard deviation of 298 mm.

• Korean Journal of Environmental Agriculture
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• v.20 no.3
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• pp.192-200
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• 2001

The event-based agricultural non-point source(AGNPS) pollution model was applied to estimate the loads of nitrogen and phosphorus in a stream draining small agricultural watersheds. Calibration and verification of the model were performed using observed data collected from rainfall events in the Imgo watersheds during 1997-1998. Parameter calibrations were made for the runoff curve number. The peak flow volumes in the watersheds were well reproduced by the modified model. Average deviation between observed and simulated values was 10%, and this match was confirmed by the coefficient of efficiency value of 0.97. The deviations tended to increase as the peak flows increased. The simulated total N concentrations in the stream water were fairly close to the measured values, and the coefficient of efficiency in the estimation was 0.93. However, there were relatively large variations between calculated and observed values of total P concentration, and the coefficient of efficiency in the estimation was 0.74. Any inaccuracies that arise in estimating runoff flow and nutrient loading can not be explained exactly and further adjustment and refinements may be needed for application of AGNPS in agricultural watersheds. With this restrictions in mind, it can be concluded that AGNPS can provide realistic estimates of nonpoint source nutrient yields.

• Korean Journal of Food Science and Technology
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• v.27 no.5
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• pp.799-806
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• 1995

The steady shear and small amplitude oscillatory dynamic rheological properties of citrus pectin $([\eta]=3.75\;dL/g)$ were characterized for a wide range of pectin concentrations $({\sim}6%)$. The typical power-law flow was observed above 2.0% concentration, and the shear rate dependence of viscosity increased with pectin concentration. The transition from dilute to concentrated regime, determined from the double logarithmic plot of ${\eta_{sp.o}}\;vs\;C[\eta]$, occurred at a critical coil overlap parameter $C^{*}[\eta]\approx4.0$, at which ${\eta_{sp.o}}$ corresponded to approximately 10.0. The slopes of ${\eta_{sp.o}}\;vs\;C[\eta]$, at $C[\eta]\;at\;C[\eta]C^{*}[\eta]$were 1.1 and 4.5, respectively. The steady viscosity $(\eta)$ displayed a good superposition at ${\eta}/{\eta}_o\;vs\;{\gamma}/{\gamma}_{0.8}$ relation with an exception of high concentration (6%), which arised from the significant deviation of flow behavior index (n values of $\eta_{a}=K\gamma^{n-1}$) at high concentration. Dynamic measurements showed that the loss modulus $(G^{\prime\prime})$ was much higher than the storage modulus $(G^\prime)$for all concentrations studied, indicating predominant viscoelastic liquid-like behavior of pectin solutions. The frequency dependence of $G^\prime$ was higher than that of $G^\prime\prime$ at the same concentration, whose trend was more pronounced with decreasing pectin concentration. The shear viscosity $(\eta)$ was almost identical to the complex viscosity $(\eta^{*})$ at low concentration, following the Cox-Merz rule, but they became increasingly different at high concentration.

• Journal of Energy Engineering
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• v.29 no.2
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• pp.1-9
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• 2020

In recent days, fuel cell has received attention from the world as an alternative power source to hydrocarbon used in automobile engines. With the industrial advances of fuel cell, There have been a lot of researches actively conducted to find a way of generating hydrogen. Among many hydrogen production methods, Solid Oxide Electrolysis Cell(SOEC) is not only a basic way but also environment-friendly method to produce hydrogen gas. Solid Oxide Electrolysis Cell has lower electrical energy demands and high thermal efficiency since it is possible to operate under high temperature and high pressure conditions. For these reasons, experimental researches as well as studies on numerical modeling for Solid Oxide Electrolysis Cell have been under way. However, studies on numerical modeling are relatively less enough than experimental accomplishments and have limited performance prediction, which mostly is considered as a result from inadequate effects of electrochemical properties by temperature and pressure. In this study, various experimental studies of commercial Membrane Electrode Assembly (MEA) composed of Ni-YSZ (40wt%, Ni-60 wt% YSZ)/8-YSZ (TOSOH, TZ8Y)/LSM (La_0.9Sr_0.1MnO₃) was utilized for improving effectiveness of SOEC model. After numerically analyzing effects of electrochemical properties according to operating temperature, causing the largest deviation between experiments and simulation are that Charge Transfer Coefficient (CTC), exchange current density, diffusion coefficient, electrical conductivity in SOEC. Analyzing temperature effect on parameter used in overpotential model is conducted for modeling of SOEC. cross-validation method is adopted for application of various MEA and evaluating feasibility of model. As a result, the study confirm that the numerical model of SOEC based on structured process of effectiveness evaluation makes performance prediction better.

• Korean Journal of Veterinary Research
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• v.40 no.2
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• pp.383-391
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• 2000

Milk urea nitrogen (MUN) determination is being used an indicator of the protein-energy balance in dairy herds. A faulty balance can be corrected to optimize milk production and animal health. This parameter is regarded as a potential tool to evaluate suboptimal feeding practices and reproductive disorders. Therefore, the purpose of this study was to investigate the response of milk composition by regular feeding analysis and to compared the relationship between MUN and milk protein(MP) and fertility at the insemination period in Holstein dairy cows. Total of 355 artificial insemination (AI) for 150 Holstein cows in the herd were used to examine the relationship between MUN and MP content and conception rate. The AI occured for the cows 50 to 150 day in milk, and MUN and MP concentration were determined using automated infrared procedures. The mean${\pm}$standard deviation of MUN and MP concentration in the herd were $15.6{\pm}2.1mg/dl$ and $3.23{\pm}0.38%$, respectively. MUN contents of bulk milk were increase by elevated crude protein intake. The conception rate was lower in the cows in which the level of MUN was lower than > 8.0mg/dl (10.0%) or > higher than 25mg/dl (15.4%) relative to the cows in MUN content of 12.0~17.9 mg/dl (36.7%) at the time of insemination. Also, lower MP than 3.0% or higher MP than 3.25% were associated with a lower conception rates. Consequently, MUN and MP analyses may be used serve as a monitoring tool of protein and energy nutritional balance to improve reproduction efficiency in Holstein dairy cows.