• Bulletin of the Korean Chemical Society
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• v.34 no.12
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• pp.3709-3714
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• 2013

Based on the Debye-H$\ddot{u}$ckel equation, a semi-empirical equation for activity coefficients was derived through empirical and theoretical trial and error efforts. The obtained equation included two parameters: the proportional factor and the effective radius of an ionic sphere. These parameters were used in the empirical and regression parameter fitting of the calculated values to the experimental results. The activity coefficients calculated from the equation agreed with the data. Transforming to a semi-empirical form, the equation was expressed with one parameter, the ion radius. The ion radius, ${\alpha}$, was divided into three parameters, ${\alpha}_{cation}$, ${\alpha}_{anion}$ and ${\delta}_{cation}$, representing parameters for the cation, anion and combination, respectively. The advantage of this equation is the ability to propose a semi-empirical equation that can easily determine the activity coefficient with just one parameter, so the equation is expected to be used more widely in actual industry applications.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.2
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• pp.106-111
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• 2001

The applicability of near infrared reflectance spectroscopy(NIRS) was tested to determine the protein and oil contents in ground soybean [Glycine max (L.) Merr.] seeds. A total of 189 soybean calibration samples and 103 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of protein, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing and 1 point second smoothing) math treatment condition with SNV-D (Standard Normal Variate and Detrend) scatter correction method and entire spectrum by using MPLS (Modified Partial Least Squares) regression. In the case of oil, the best equation was obtained at 1, 4, 4, 1 condition with SNV-D scatter correction method and near infrared (1100-2500nm) region by using MPLS regression. Validation of these NIRS equations showed very low bias (protein:-0.016%, oil : -0.011 %) and standard error of prediction (SEP, protein: 0.437%, oil: 0.377%) and very high coefficient of determination ($R^2$, protein: 0.985, oil : 0.965). Therefore, these NIRS equation seems reliable for determining the protein and oil content, and NIRS method could be used as a mass screening method of soybean seed.

• Journal of the Korean Geotechnical Society
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• v.27 no.9
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• pp.55-66
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• 2011

The compulsory replacement method for soft ground treatment is simple but excellent in economic feasibility. However, the accurate replacement depth is not easy to properly predicted since an theoretical algorithm has not presently been established so far. In this research a prediction equation is proposed in a new form based on the liquid limit and natural moisture content rather than on the bearing capacity of the soft soil layer. The equation is based on the monitoring as well as the confirmatory boring at the site. In addition, the equation has been derived from the data obtained from the analysis of the characteristics of silt/clay of Sihwa region. The final prediction equation has been drawn by applying the regression analysis method.

• Journal of the Korean Society of Tobacco Science
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• v.28 no.1
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• pp.43-50
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• 2006

The objective of the present study is to induct the regression equation for the hardness prediction of cellulose acetate filter which was manufactured by the domestic cellulose acetate tow manufacturer. As a result of our study, the hardness of filter was increased with increasing the plasticizer content and packing density as major factors affecting to the filter hardness. As a result which was obtained by the three dimensional response surface methodology in STATISTIC A program, the hardness prediction value well fitted with experiment result on the high plasticizer content. To make up for the this equation, the new modified fraction of solid factors which was contained the mono denier factor was introduced to the hardness prediction equation, and this third regression equation which was sufficient for the wide plasticizer content, was obtained by the three dimensional response surface methodology in STATISTICA. This results indicated that the third regression equation which was obtained this study was applicable for the hardness prediction of cellulose acetate filter which was manufactured by the domestic cellulose acetate tow manufacturer.

• Journal of Environmental Science International
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• v.21 no.10
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• pp.1235-1244
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• 2012

The objective of this study is to evaluate Flow-Pollutant load delivery ratio equations developed from rural watershed on main subwatersheds within Juam Lake. Two regression equations for BOD and three equations for T-P were evaluated on Bosung cheon, Dongbok cheon, Songgwang cheon, Naenam cheon, and Sinpyeon cheon. The results show that estimation of BOD delivery ratio using flow-delivery equation is reliable when relative composition of discharge load of pollutant sources of a watershed is similar to those of watershed where the equation developed. On the other hand, application of regression equation for T-P was feasible when the landuse pattern and relative composition of discharge load of pollutant sources of a watershed is similar to those of watershed where the equation developed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.101-111
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• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

• Preventive Nutrition and Food Science
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• v.14 no.4
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• pp.310-315
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• 2009

The partitioning behavior of the five printing ink solvents in nine lab-made cookies with various sugar and water content at 25${^{\circ}C}$ was studied to find out the presence and effects of interaction between the two ingredients on partitioning behavior in cookies. Solvents were ethyl acetate, hexane, isopropanol, methyl ethyl ketone and hexane. It was observed that the partition coefficient (the solvent concentration in food compared to that in air, Kp) decreased as sugar increased in all case and increased as water content increased for all compounds except toluene. Statistical analysis by the F-test method was used to determine the significance of sugar-water interactions, as well as other single factors on partitioning behavior of each solvent. Sugar content alone had no significant effects, but the crystallinity of sugar, as changed by water content, affected the partitioning behavior of the five solvents significantly. Parameter estimation for each significant factor by SAS program yielded a regression equation, which was used to predict the partitioning behavior in the finished cookie. Kp values from the regression equation could be determined more precisely by applying a correction term for the interaction between sugar and water to the Kp values of each ingredient after baking.

• Journal of Environmental Health Sciences
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• v.7 no.1
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• pp.9-20
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• 1981

This study was performed to determine the correlationship between temperature and overall removals of BOD, SS and to demonstrate the effect of temperature on treatment performance. These data for a period from February 1, 1977 to January 31, 1980 were obtained from the Cheong-Gye Cheon Sewage Treatment plant. The results of correlation and stepwise multiple regression analysis were as follows. 1) Secondary effluent BOD and SS showed negative correlationship with water temperature, with correlation coefficient of -0.1710, and -0.1654 respectively. 2) Correlation coefficient of BOD, SS removal rate and water temperature were 0.1823 and 0.0429 respectively. 3) Regresion equation for estimate of BOD removal rate was as follows $\widehat{Y}_1$ (BOD removal rate)=63.9994+0.5442X(water temperature). And BOD removal rate showed non significant change according to the water temperature. 4) Regression equation for estimate of SS removal rate was as follows $\widehat{Y}_2$ (SS removal rate)=61.6881+0.1514X(Water temperature). And SS removal rate showed non significant change according to the water temperature. 5) According to the Stepwise Multiple Regression analysis, water temperature ranked second order in the BOD removal rate estimation and the equation was as follows $\widehat{Y}_1$ (BOD removal rate)=69.7398+0.2665 $X_1$ (Primary effluent BOD)+0.3562 $X_2$ (Water temperature)-0.0122 $X_3(Flow)+4413.271X_4$ (Organic Loading).

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.2
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• pp.116-124
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• 1989

Based on the theory of runoff equation proposed by SCS, the actual storage capacity(Sa) as a modified retention paramater was introduced to estimate the effective rainfall for the daily streamfiow analysis. During a storm, the actual storage capacity is limited by either soil water storage or infiltration rate as precipitation increases. Therefore, it was assumed that Sa is dependent on the baseflow before storm runoff(Qb) corresponding to soil water storage and the total amount of precipitation(P) corresponding to infiltration rate of a watershed. Effective rainfalls (Direct run-offs) estimate4 from SCS equation using Sa were compared with observed effective rainfalls at 10 watersheds in Geum river watershed boundary. 1. Regression equation for Sa was supposed Sa=Co+C$_1$XP+C$_2$X Qb Regression coefficients were highly significant at the level of 0. 01 and R$^2$ were 0.57 to 0.73. 2. The adjustment of coefficient of initial abstraction was made according to the storm size. It was adjusted to 025 for 30mm or less, 0.23 for 30 to 80mm, 0.20 for 80 to 200mm, and 0.1 for 200mm or more. 3. Regression equations between estimated and observed effective rainfall showed that slopes were 0.857 to 1.029 and R$^2$ were 0.779 to 0.989,

• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.415-419
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• 2013

To mitigate forest fire damage, it is needed to concentrate suppression resources on the fire having a high probability to become large in the initial stage. The objective of this study is to develop the large fire judgement model which can estimate large fire possibility index between the fire size and the related factors such as weather, terrain, and fuel. The results of logistic regression equation indicated that temperature, wind speed, continuous drought days, slope variance, forest area were related to the large fire possibility positively but elevation has negative relationship. This model may help decision-making about size of suppression resources, local residents evacuation and suppression priority.