• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.711-724
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• 2003

Some growth curve models were used to fit individual growth of 1,083 Hanwoo cows born from 1970 to 2001 in Daekwanryeong branch, National Livestock Research Institute(NLRI). The effects of year-season of birth and age of dam were analyzed. In analysis of variance for growth curve parameters, the effects of birth year-season were significant for mature weight(A), growth ratio(b) and maturing rate(k)(P〈.01). The effects of age of dam were significant for growth ratio(b) but not significant for mature weight(A) and maturing rate(k). The linear term of the covariate of age at the final weights was significant for the A(P〈.01) and k(P〈.01) of Gompertz model, von Bertalanffy model and Logistic model. For the growth curve parameters fitted on individual data using Gompertz model, von Bertalanffy model and Logistic model, resulting the linear contrasts(fall-spring), Least square means of A in three nonlinear models were higher cows born at fall and A of Logistic model was significant(P〈.05) between the seasons. According to the results of the least square means of growth curve parameters by age of dam, least square means of mature weight(A) in Gompertz model was largest in 6 year and smallest estimating for 3 and 8 years of age of dam. The growth ratio(b) was largest in 2 year of age of dam and smallest estimating in 8 year. The A and k were not different by age of dam(p〉.05), On the other hand, the b was different by age of dam(p〈.01). The estimate of A in von Bertalanffy model was largest in 6 year and smallest in 8 and 9 years of age of dam. The b was largest in 2 year and tend to decline as age of dam increased. The A and k were not different by age of dam(p〉.05), On the other hand, the b was highly significant by age of dam(p〈.01).

• Korean Journal of Agricultural Science
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• v.20 no.1
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• pp.48-55
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• 1993

This study was conducted to determine the main effect of the breed, parity, forrowing year and month on average daily gain, feed efficiency, backfat thickness, age at 90 Kg body weight and selection index. The data analysis were the record of 115 male pigs producted from Landrace. Large Yorkshire and Duroc purebreds tested at Chungnam Province Animal Breeding Station from 1986 to 1990. The results obtained are summarized as follows : 1. The breed average of daily gain, feed efficiency, backfat thickness, age at 90Kg body weight and selection index were $922.25{\pm}17.2$g, $2.85{\pm}0.04$, $2.03{\pm}0.04$cm, $135.44{\pm}1.38$ days and $168.33{\pm}2.42$, respectively. 2. The effect of breed was highly significant at 1% level in average daily gain, age at 90Kg body weight and selection index, and significant at 5% level in backfat thickness. Among the purebreds, Duroc was superior in average daily gain, age at 90Kg body weight and selection index with 977.22g, 132.47 days and 172.35, respectively. But Large Yorkshire was 1.95cm thiner than other breeds in backfat thickness. 3. The effect of parity was highly significant at 1% level in average daily gain, and significant at 5% level in backfat thickness and selection index. Among the parity, the 3rd and 4th parity were superior in average daily gain and selection index with 974.92g 177.61, 959.48g and 177.84, respectively. 4. The effect of forrowing year was highly significant at 1% level in average daily gain, feed efficiency and selection index, and that of forrowing month was highly significant at 1% level in average daily gain, and significant at 5% level in backfat thickness and selection index, respectively. Among the forrowing month, March was superior in average daily gain and selection index with 968.22g and 174.54, respectively.

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.913-922
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• 1995

Background: When we define the pressure of pulmonary vasculature in which a recruitment of blood flow occurs as $P_I$ and the proportion of change in pulmonary artery to that in cardiac output as IR and then we compare PI and IR with pulmonary vascular resistance, we would find some problems in pulmonary vascular resistance. In other words, it is the theory that, IR should be increased mainly in pulmonary embolism in which decreases the cross sectional area of pulmonary vasculature. But there are many contradictory reports resulted from various researches and the fact is known widely that any difference exists between PVR and PI, IR. For this reason, the purpose of this study is to observe how PI and IR change at the time of the outbreak and during treatment of the pulmonary embolism, and to find out the meaning of these new indicators and the difference from the pulmonary vascular resistance used generally when we subdivide the pulmonary vascular resistance into PI and IR. Method: After making AV fistula in experimental dog, we controlled cardiac output at the intervals of 15 minute in case of three kinds(all AV fistula are obstructed, only one of fistula is open and all of fistula is open), and after evoking massive pulmonary embolism with radioactive autologous blood clots, we measured the mean pulmonary artery pressure, and calculated PI and IR. We observed the pattern of change in PI and IR, without giving the control group any specific treatment and with injecting intravenously rtPA in the Group 1 and Group 2 at the dose of 1mg per kg, for 15 minutes fot the former and 3 hours for the latter. Result: 1) Pulmonary vascular resistance showed a change similar to that of pulmonary artery pressure and in all three group, PVR increased significantly, but group 1 and group 2 showed tendency that PVR keeps on decreasing after treatment, and the rate of decrease in group 1 is more rapid than group 2 significantly. 2) Both intersection(PI) and degree(IR) are proved statistically significant, in view of the straight line relationship between cardiac output and pulmonary artery pressure, calculated by minimal regression method. 3) PI changed similarly to pulmonary vascular resistance, while in the IR which is theoretically more similar to PVR, there was no significant difference or change after rtPA infusion. Conclusion: In the pulmonary embolism, Both change in IR which means real resistance of pulmonary vasculature and PI which was developed due to secondary vasoconstriction by pulmonary embolism are reflected same time.

• Journal of the Korean Chemical Society
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• v.37 no.3
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• pp.344-350
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• 1993

25,26,27,28-Tetraacetoxycalix[4]arene·monohydrate is orthorhombic, space group Pbca with a = 14.979(4), b = 15.154(4), c = 27.890(3) ${\AA}$, Z = 8, V = 6330.6 ${\AA}^{-3}$, D$_c$ = 1.28 $g{\cdot}cm^{-3}$, (Mo K${\alpha}$) = 0.71069 ${\AA}$, ${\mu}$ = 0.86 cm$^{-1}$, F(000) = 2600, and R = 0.069 for 3376 unique observed reflections with I > 1.0 ${\sigma}$(I). The structure was solved by direct methods and refined by cascade diagonal least-squares refinement. All the C-H bond lengths(= 0.96 ${\AA}$), the methyl groups and the methylene groups are fixed and refined as the rigid groups with ideal geometry. The macrocycle exists in the 1,3 alternate conformation (by Conforth) making the angles of 110.7, 684, 113.7 and 68.8$^{\circ}$ between the benzene rings and the methylenic mean plane, and four each acetoxy groups are twisted away from their own benzene rings with the angles of 68.2, 97.6, 78.9 and 71.3$^{\circ}$, respectively. The relative dihedral angles between two opposite side of the benzene rings are 135.6$^{\circ}$ for the rings (1) and (3) and 135.2$^{\circ}$ for (2) and (4). A water molecule which has nearly the same height of the methylenic plane of the macrocycle in the c-axis, is located within the distances of 2.942(5) ${\AA}$ from the O(8) atom of the carbonyl group and 2.901 ${\AA}$ from, another O(2)(1/2-x, -1/2＋y, z). The shortest contact between the molecule is 3.193 ${\AA}$ from the O(4) to the C(3)(1/2＋x, 1/2-y,-z).

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.115-128
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• 2019

In this study, a probabilistic model for the estimation of yearly workable wave condition period for offshore operations is developed. In doing so, we first hindcast the significant wave heights and peak periods off the Ulsan every hour from 2003.1.1 to 2017.12.31 based on the meteorological data by JMA (Japan Meterological Agency) and NOAA (National Oceanic and Atmospheric Administration), and SWAN. Then, we proceed to derive the long term significant wave height distribution from the simulated time series using a least square method. It was shown that the agreements are more remarkable in the distribution in line with the Modified Glukhovskiy Distribution than in the three parameters Weibull distribution which has been preferred in the literature. In an effort to develop a more comprehensive probabilistic model for the estimation of yearly workable wave condition period for offshore operations, wave height distribution over the 15 years with individual waves occurring within the unit simulation period (1 hour) being fully taken into account is also derived based on the Borgman Convolution Integral. It is shown that the coefficients of the Modified Glukhovskiy distribution are $A_p=15.92$, $H_p=4.374m$, ${\kappa}_p=1.824$, and the yearly workable wave condition period for offshore work is estimated to be 319 days when a threshold wave height for offshore work is $H_S=1.5m$. In search of a way to validate the probabilistic model derived in this study, we also carry out the wave by wave analysis of the entire time series of numerically simulated significant wave heights over the 15 years to collect every duration periods of waves the height of which are surpassing the threshold height which has been reported to be $H_S=1.5m$ in the field practice in South Korea. It turns out that the average duration period is 45.5 days from 2003 to 2017, which is very close to 46 days from the probabilistic model derived in this study.

• Korean Journal of Agricultural Science
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• v.8 no.1
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• pp.72-81
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• 1981

This study was conducted to determine the differences in the growth and milk production performance of Holstein Crossbreds (Korean Native Cattle(♀)${\times}$Holstein(♂)) and Korean Native Cattle produced at the Livestock Experiment Station of the Office of Rural Developement from 1973 to 1978. The number of heifers and cows used in this experiment were 15 head of Korean Native Cattle and 11 head pf Holstein Crossbreds Cattle. Body weight and body measurements were taken at birth, 6, 12, 24 and 36 months of age, however, body measurements were not taken at birth. Milk production was checked from the 11 th day to 180th day after calving. The data was analyzed using the least square procedure in order to estimate the effect of the mating group, year of birth, calving season and parity. The results obtained from this study were as follows: 1. The body weights of the Holstein Crossbreds were heavier than the body weights of purebred Korean Native Cattle. The body weight of the Holstein Crossbreds averaged 28.09kg, 146.64kg, 254.48kg, 392.04kg and 454.46kg at birth, 6, 12, 24 and 36 months of age, respectively. However, the body weights of purebreds Korean Native Cattle averaged 22.45kg, 132.82kg, 220.68kg, 363.54kg and 365.54kg at the same ages. 2. The year of birth affected on body weight at each point during the growing stage, except birth, heifers born in the spring and autumn were heavier than the others, but calving season did not affect on body weight during the growing stage except at birth and 6 months. 3. Parity showed significant differences on body weight in the growing stage. Calves from the 5th parity had a tendency to be heavier than the other calves. 4. The year of birth, calving season and parity at calving had no affect on the change of body measurements, but the wither height, hip height, chest depth, chest girth and hip width were significantly greater in the Holstein Crossbreds at 24 months of age. 5. Mating groups had a significant affect on milk production during the growing stage. Year of birth and calving season did not affect milk production, but parity was significant from 11 days after calving. 6. The least-squares means used to determine the daily milk production were 3.60 and 8.26kg/day for Korean Native Cattle and the Holstein Crossbreds, respectively.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.