• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.1
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• pp.61-68
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• 2005

In this study, it was proposed that an equation for predicting consolidation settlement on soft clay ground, which separate total settlement into primary and secondary consolidation settlement equation. The consolidation settlements by the proposed equation and by the measured settlements from laboratory model test were compared and verified for its application. It was appeared that the proposed equation from the laboratory model test approach to be more realistic comparing to !the result of Terzaghi's equation. From the above application, it was concluded that the final settlement prediction by. the Hyperbolic, Asaoka methods is needed to the initial settlement but the proposed equation could be much applicable in the lacking condition of measured data of the initial period.

• Bulletin of the Korean Mathematical Society
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• v.40 no.4
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• pp.677-683
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• 2003

In multi-allelic model $X\;=\;(x_1,\;x_2,\;\cdots\;,\;x_d),\;M_f(t)\;=\;f(p(t))\;-\;{\int_0}^t\;Lf(p(t))ds$ is a P-martingale for diffusion operator L under the certain conditions. In this note, we examine the stochastic differential equation for model X and find the properties using stochastic differential equation.

• Bulletin of the Korean Chemical Society
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• v.34 no.11
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• pp.3425-3428
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• 2013

We solve the Klein-Gordon equation with the Morse empirical potential energy model. The bound state energy equation has been obtained in terms of the supersymmetric shape invariance approach. The relativistic vibrational transition frequencies for the $X^1{\sum}^+$ state of ScI molecule have been computed by using the Morse potential model. The calculated relativistic vibrational transition frequencies are in good agreement with the experimental RKR values.

• Bulletin of the Korean Mathematical Society
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• v.56 no.5
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• pp.1129-1141
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• 2019

We propose a stochastic delay financial model which describes influences driven by historical events. The underlying is modeled by stochastic delay differential equation (SDDE), and the delay effect is modeled by a stopping time in coefficient functions. While this model makes good economical sense, it is difficult to mathematically deal with this. Therefore, we circumvent this model with similar delay effects but mathematically more tractable, which is by the backward time integration. We derive the option pricing equation and provide the option price and the perfect hedging portfolio.

• Ocean and Polar Research
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• v.29 no.1
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• pp.9-31
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• 2007

Most seismic sea waves in the East Sea originate from earthquakes occurring near the Japanese west coast. While the waves propagate in the East Sea, they are deformed by refraction, diffraction and scattering. Though the Boussinesq equation is most applicable for such wave phenomena, it was not used in numerical modelling of seismic sea waves in the East Sea. To examine characteristics of seismic sea waves in the East Sea, numerical models based on the Boussinesq equation are established and used to simulate recent tsunamis. By considering Ursell parameter and Kajiura parameter, it is proved that Boussinesq equation is a proper equation for seismic sea waves in the East Sea. Two models based on the Boussinesq equation and linear wave equation are executed with the same initial conditions and grid size ($1min{\times}1min$), and the results are compared in various respects. The Boussinesq equation model produced better results than the linear model in respect to wave propagation and concentration of wave energy. It is also certified that the Boussinesq equation model can be used for operational purpose if it is optimized. Another Boussinesq equation model whose grid size is $40sec{\times}30sec$ is set up to simulate the 1983 and 1993 tsunamis. As the result of simulation, new propagation charts of 2 seismic sea waves focused on the Korean east coast are proposed. Even though the 1983 and 1993 tsunamis started at different areas, the propagation paths near the Korean east coast are similar and they can be distinguished into 4 paths. Among these, total energy and propagating time of the waves passing over North Korea Plateau(NKP) and South Korea Plateau(SKP) determine wave height at the Korean east coast. In case of the 1993 tsunami, the wave passing over NKP has more energy than the wave over SKP. In case of the 1983 tsunami, the huge energy of the wave passing over SKP brought about great maximum wave heights at Mukho and Imwon. The Boussinesq equation model established in this study is more useful for simulation of seismic sea waves near the Korean east coast than it is the Japanese coast. To improve understanding of seismic sea waves in shallow water, a coastal area model based on the Boussinesq equation is also required.

• Animal Bioscience
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• v.34 no.10
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• pp.1623-1631
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• 2021

Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW^0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R² = 0.39) than the NRC equation (R² = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

• 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.

• Fire Science and Engineering
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• v.17 no.2
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• pp.50-55
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• 2003

The lower flash points for the binary system, n-butanol+n-decane, were measured by Pensky-Martens closed cup tester. The experimental results showed the minimum in the flash point versus composition curve. The experimental data were compared with the values calculated by the reduced model under an ideal solution assumption and the flash point-prediction models based on the Van Laar and Wilson equations. The predictive curve based upon the reduced model deviated form the experimental data for this system. The experimental results were in good agreement with the predictive curves, which use the Van Laar and Wilson equations to estimate activity coefficients. However, the predictive curve of the flash point prediction model based on the Willson equation described the experimentally-derived data more effectively than that of the flash point prediction model based on the Van Laar equation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.11
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• pp.57-64
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• 2005

Large Eddy Simulation (LES) of turbulent premixed combustion flow is performed by using the dynamic subgrid scale model based on -equation describing the flame front propagation. After introducing the LES governing equations with dynamic subgrid scale (DSGS) model newly introduced into the -equation, the turbulent premixed combustion flow over backward facing step is analyzed to validate present formulation. The calculated results can predict the velocity and temperature of the combustion flow in good agreement with the experiment data.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.171-177
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• 2005

This paper describes the method of calculating coordinate using Forward Kinematics and expresses the recursive equation as the numerical formula using a homogeneous coordinate for creating workspace. This paper proposes an algorithm for the workspace of human model using the recursive equation and the joint limit angle of human model, and describes the results of workspace of the human model as computer graphics.