• Journal of the Korean Chemical Society
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• v.56 no.4
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• pp.406-415
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• 2012

The corrosion inhibition of copper-nickel alloy by Ethylenediamine (EDA) and Diethylenetriamine (DETA) in 1.5M HCl has been investigated by weight loss technique at different temperatures. Maximum value of inhibitor efficiency was 75% at $35^{\circ}C$ and 0.2 M inhibitor concentration EDA, while the lower value was 4% at $35^{\circ}C$ and 0.01 M inhibitor concentration DETA. Two mathematical models were used to represent the corrosion rate data, second order polynomial model and exponential model respectively. Nonlinear regression analysis showed that the first model was better than the second model with high correlation coefficient. The reactivity of studied inhibitors was analyzed through theoretical calculations based on density functional theory (DFT). The results showed that the reactive sites were located on the nitrogen (N1, N2 and N4) atoms.

• Journal of Environmental Science International
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• v.26 no.6
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• pp.719-728
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• 2017

In order to investigate the adsorption characteristics for Sr ion using the Na-X zeolite synthesized from coal fly ash, batch tests and response surface analyses were carried out. The adsorption kinetic data for Sr ions, using Na-X zeolite, fitted well with the pseudo-second-order model. The uptake of Sr ions followed the Langmuir isotherm model, with a maximum adsorption capacity of 196.46 mg/g. Thermodynamic studies were conducted at different reaction temperatures, with the results indicating that Sr ion adsorption by Na-X zeolite was an endothermic (${\Delta}H^o$>0) and spontaneous (${\Delta}G^o$<0) process. Using the response surface methodology of the Box-Behnken method, initial Sr ion concentration ($X_1$), initial temperature ($X_2$), and initial pH ($X_3$) were selected as the independent variables, while the adsorption of Sr ions by Na-X zeolite was selected as the dependent variable. The experimental data fitted well with a second-order polynomial equation by multiple regression analysis. The value of the determination coefficient ($R^2=0.9937$) and the adjusted determination coefficient (adjusted $R^2=0.9823$) was close to 1, indicating high significance of the model. Statistical results showed the order of Sr removal based on experimental factors to be initial pH > initial concentration > temperature.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• YAKHAK HOEJI
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• v.20 no.1
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• pp.63-69
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• 1976

Prednisolone tablet product design problem was structured as constrained optimization problem and subsequently solved by multiple regression analysis and Lagrangian method of optimixation. Prednisolone was the drug chosen and anhydrous lactose and corn starch were the adjuvants. The effect of anhydrous lactose and corn starch concentrations on tablet hardness, volume, disintegration time and in vitro release rate was studied. The concentrations of anhydrous lactose and corn starch used in this experiment were 30-60 percent and 5-30 percent, respectively. A full second-order (quadratic) model with all possible two-factor interactions was employed. To obtain the values of anhydrous lactose and corn starch which miniumize the in vitro : release time (t$_{60%}$) subject to the constraint on tablet hardness, disintegration time and volume, we solved the Lagrange function. Multiple correlation coefficients for the regression models were correlated at less than 0.05 level and it was found that the optimum concentrations of anhydrous lactose and corn starch were 45 percent and 21 percent, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• Asia pacific journal of information systems
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• v.11 no.3
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• pp.43-62
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• 2001

The purpose of this research is to develop a webcasting site evaluation model, to estimate the relative importance of factors affecting the performance of webcasting sites, and to derive the CSFs of the sites. Evaluation model consists of 6 first-level evaluation factors and 23 second-level evaluation factors. This study compares the evaluation scores of website with those of the customer satisfaction. We inspect seven hypotheses. Hypothesis)(H1) tests correlation of the evaluation scores of website and those of the customer satisfaction. Hypothesis2(H2)$\sim$Hypothesis7(H7) tests if 6 first-level factors(planning, program & form, design, technology, interface, content) have an impact on customer satisfaction. In order to test the hypotheses, correlation analysis and regression analysis are performed. As a result of empirical tests, all Hypotheses$(H1){\sim}H7)$ are accepted and its implications are discussed.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.328-332
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• 1993

This study presents an algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is applied for the weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined form the step input. The performance of the algorithm was tested on a check weigher. Discussions were extended to the development of noise reduction techniques and to the lagged introduction of objects on the moving plate. It turns out that the algorithm shows several desirable features suitable for microcomputer assisted real-time signal processing, which are high precision and stability in noisy environment.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.136-142
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• 1994

This study presents an algorithm and related techniques which could satisfy the important properties of check weighers and conveyor scales. The algorithm of Recursive Least Squares Regression is described for te weighing system simulated as a dynamic model of the second order. Using the model and the algorithm, model parameters and then the mass being weighed can be determined from the step input. The performance of the algorithm is illustrated in digital simulation. Discussions are extended to the development of fast converging algorithm. It turns out that the algorithm shows several desirable features suitable for microcomputer assisted real-time signal processing, which are high precision and stability in noisy environment.

• Water for future
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• v.14 no.4
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• pp.19-26
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• 1981

The auto-and cross-correlation function, power spectrum, coherence function and Markov model are applied to investigate the statistical characteristics of discharge and each factor of water quality and the interrelation-ship between the variation of discharge and water quality factors. The analysis of discharge, dissolved oxygen and electric conductivity, which were only obtainable data at the Indogyo gagining station in the downstream of the Han River, clearly showed that they hace distinct period of 12 months and three different periods of 6, 4 and 3 months weaker than the former. The cross-correlation between the discharge and water quality(DO, COND) is rather weak and the crosscorrelation function has its peak at lag one. It is considered therefrom that the variation of discharge behaves on water quality facotrs with one day's difference. In the examination of linear regression model for the serial generation and predictive measures, discharge series is fit to first and second order Markov model and DO, COND to first order Markov model.