• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.3
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• pp.573-582
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• 1994

Blast vibration equations proposed previously are investigated. Special attention is given to the blast vibration equation which shows the best fitting to the geologic condition of Korea. The fittness of proposed blast vibration equation is analyzed and examined using many field data measured in Korea. The prediction of blast vibration equation using field data was performed by linear regression analysis. Moreover, after the prediction of each blast vibration equation, vibration velocity is recalculated on the basis of scaled distance at each equation. Reliability of regressioned blast vibration equation is observed by comparing predicted and measured velocity, which is divided into small-scale blasting of city and large-scale blasting of quarry. Based on this study, the best fitting equation to the Korean geologic condition is ROOT SCALING & CUBE ROOT SCALING proposed by USBM(United Nations Bureau of Mines). Also representative blast vibration equations depending on the different kinds of rock mass are proposed using measured and existing field data.

• Journal of the Korean Mathematical Society
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• v.38 no.3
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• pp.657-667
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• 2001

We propose a statistical interpolation approximate solution for a nonlinear stochastic integral equation of a stock price process. The proposed method has the order O(h$^2$) of local error under the weaker conditions of $\mu$ and $\sigma$ than those of Milstein' scheme.

• Proceedings of the Korea Concrete Institute Conference
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• 1993.04a
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• pp.124-129
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• 1993

In this study, a simple and accurate model equation for prediction of shear strength of reinforce concrete beams without web, reinforcement is proposed based on basic shear transfer mechanism and modified Bazant's size effect law. The proposed equation includes the effects of concrete strength, longitudinal steel ratio, shear span to depth ratio and effective depth. Comparisons with published experimental data indicate that the proposed equation estimates properly the effects of these factors. Among many equations, ACI code equation, Zsutty's equation and Bazant's equation are selected for comparison. As the result, the accuracy of the proposed equation is better than that of any other equations.

• Food Science of Animal Resources
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• v.38 no.5
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• pp.1109-1119
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• 2018

In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation - developed to estimate LMP in whole carcasses based on six variables - was characterized by a coefficient of determination ($R^2_v$) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited $R^2_v$ values${\geq}0.8$ (0.73 for loin parts) with low RMSEV values. However, lower accuracy ($R^2_v=0.67$) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.

• Food Science of Animal Resources
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• v.28 no.5
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• pp.604-609
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• 2008

Analyses were conducted to estimate carcass component of yield grade factors by sex and live weight class and to develop the prediction equation of retail product weight by sex in Korean native cattle (Hanwoo). Data from 42,113 Hanwoo carcasses were used to estimate the traits of yield grade factor and an additional 1,066 carcasses were used to develop the prediction equation of retail meat weight. The average of fasting weight of cow, bull and steer were 529 kg, 596 kg, and 634 kg respectively. Carcass weight (CW), backfat thickness (BFT), loineye area (REA), Index score of wholesale meat and yield grade were significantly (p<0.01) affected by sex and live weight. The lean meat percentage, fat percentage and bone percentage based on the weight of cold carcasses were significantly different (p<0.05) between sex groups. The equation of predicting the retail meat product from this study could be expressed as a multiple regression $Y=-4.18+0.63{\times}CW\;(kg)-0.17{\times}BFT\;(cm)+0.16{\times}REA\;(cm^2)$, $R^2=0.93$. Among the independent factors, the BFT was the highest contributor to the prediction equation. Using the equation from this study should allow for rapid, precise and cost-effective assessment of the retail product in Hanwoo beef carcasses.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.548-557
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• 2021

Background: Due to the emergence of new diseases such as COVID-19, an increasing number of people are struggling with stress and depression. Interest is growing in forest-based recreation for physical and mental relief. Objectives: A prediction model equation using meteorological factors and data was developed to predict the quantities of medicinal substances generated in forests (monoterpenes) in real-time. Methods: The concentration of phytoncide and meteorological factors in the forests near Chuncheon in South Korea were measured for nearly two years. Meteorological factors affecting the observation data were acquired through a multiple regression analysis. A model equation was developed by applying a linear regression equation with the main factors. Results: The linear regression analysis revealed a high explanatory power for the coefficients of determination of temperature and humidity in the coniferous forest (R²=0.7028 and R²=0.5859). With a temperature increase of 1℃, the phytoncide concentration increased by 31.7 ng/Sm³. A humidity increase of 1% led to an increase in the coniferous forest by 21.9 ng/Sm³. In the deciduous forest, the coefficients of determination of temperature and humidity had approximately 60% explanatory power (R²=0.6611 and R²=0.5893). A temperature increase of 1℃ led to an increase of approximately 9.6 ng/Sm³, and 1% humidity resulted in a change of approximately 6.9 ng/Sm³. A prediction model equation was suggested based on such meteorological factors and related equations that showed a 30% error with statistical verification. Conclusions: Follow-up research is required to reduce the prediction error. In addition, phytoncide data for each region can be acquired by applying actual regional phytoncide data and the prediction technique proposed in this study.

• Korean Journal of Poultry Science
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• v.47 no.2
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• pp.107-114
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• 2020

This study was conducted to investigate the variation in nutrient composition of oilseed meals and to develop prediction equations for amino acid concentrations. Energy and nutrient contents were determined in a total of 1,380 feed ingredient samples including copra byproducts, corn distillers, dried grains with solubles, palm kernel byproducts, and soybean meal. The ingredient samples were imported to the Republic of Korea between 2006 and 2015. Data were analyzed using the MIXED procedure of SAS. The regression procedure of SAS was used to generate the prediction equation for the lysine concentration using the crude protein (CP) concentration as an independent variable. The concentrations of moisture, gross energy, CP, ether extract, crude fiber, ash, calcium, phosphorus, lysine, methionine, cysteine, and threonine in tested oilseed meals differed (P<0.05) depending on producing countries. The prediction equations for amino acid concentrations (% as-is basis) in the oilseed meals are: lysine = -1.08 + 0.080 × CP (root mean square error = 0.244, R² = 0.924, and P<0.001); threonine = -0.297 + 0.044 × CP (root mean square error = 0.099, R² = 0.958, and P<0.001). In conclusion, energy and nutrient compositions vary in the oilseed meals depending on the producing countries. Moreover, the crude protein concentration can be used as a suitable independent variable for estimating lysine and threonine concentrations in the oilseed meals.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1786-1795
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• 1997

In order to perform leak-before-break design of nuclear piping systems and integrity evaluation of reactor vessels, full stress-strain curves and fracture resistance (J-R) curves are required. However it is time-consuming and expensive to obtain J-R curves experimentally. The objective of this paper is to modify two J-R curve prediction methods previously proposed by the authors and to propose an additional J-R curve prediction method for nuclear piping materials. In the first method which is based on the elastic-plastic finite element analysis, a blunting region handling procedure is added to the existing method. In the second method which is based on the empirical equation, a revised general equation is proposed to apply to both carbon steel and stainless steel. Finally, in the third method, both full stress-strain curve and finite element analysis results are used for J-R curve prediction. A good agreement between the predicted results based on the proposed methods and the experimental ones is obtained.

• Journal of Welding and Joining
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• v.24 no.1
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• pp.77-87
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• 2006

Considering ferrite grain size in the base metal, the prediction model for $A_{c3}$ temperature and prior austenite grain size at just above $A_{c3}$ temperature was proposed. In order to predict $A_{c3}$ temperature, the Avrami equation was modified with the variation of ferrite grain size, and its kinetic parameters were measured from non-isothermal data during continuous heating. From calculation using a proposed model, $A_{c3}$ temperatures increased with increasing ferrite grain size and heating rate. Meanwhile, by converting the phase transformation kinetic model that predicts the ferrite grain size from austenite grain size during cooling, a prediction model for prior austenite grain size at just above the $A_{c3}$ temperature during heating was developed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.5
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• pp.1349-1355
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• 1990

By examining the fatigue deformation properties of 12% Cr rotor steel which has been proved to have high fatigue and creep rupture strength around 600deg. C, authors reviewed major fatigue life prediction models such as Manson, Langer and Morrow equations, and following results were obtained. (1) A simple life prediction model for 12% Cr rotor steel was obtained as follows : DELTA..epsilon.$_{t}$ =2.18+.sigma.$_{u}$ /E+ $N^{-0.065}$+ $e^{0.6}$ $N^{-0.025}$ This equation shows that fatigue life, N, can be easily determined when total strain range, DELTA..epsilon.$_{t}$ and ultimate tensile strength, .sigma.$_{u}$ are known by simple tension test on the given test conditions. (2) Life prediction equation with equivalent maximum stress, DELTA..sigma./2, corresponding maximum strain in one cycle at room temperature is as follows: DELTA..sigma./w=-7.01logN+96.69+96.69