• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.4
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• pp.80-87
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• 2004

To understand the each performance parameter correlation of flight type liquid-propellant rocket engine for KSR-III(Korea Sounding Rocket-III), the analysis of engine stand-alone combustion test results was carried out. Considering the variation of ablative material combustion chamber caused by erosion, linear regression analysis that ignores oxidizer/fuel ratio effect and two-variable 2nd-order polynomial regression analysis that considers oxidizer/fuel ratio change were performed. It can be described that linear regression analysis is simple and very practical method, and can predict the performance within 1% error inside analyzed region. And two-variable 2nd-order polynomial regression analysis can predict with very high accuracy inside region and shows that KSR-III engine's optimum oxidizer/fuel ratio for thrust(or specific impulse) is 2.22 and that for combustion chamber pressure(or characteristic velocity) is 2.17.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1227-1234
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• 2015

Traditional criteria for optimum experimental designs depend on the specifications of the model; however, there will be a dilemma when we do not have perfect knowledge about the model. Box and Draper (1959) suggested one direction to minimize bias that may occur in this situation. We will demonstrate some examples with exact solutions that provide a no-bias design for polynomial regression. The most interesting finding is that a design that requires less bias should allocate design points away from the border of the design space.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.10
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• pp.1718-1729
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• 1997

This paper describes motion errors due to acceleration and deceleration types of servo motors in NC machine tools. Motion errors are composed of two components : one is due to transient response of a servomechanism and the other comes from gain mismatching of positioning servo motors. It deals with circular interpolation to identify motion errors by using Interface card. Also in order to minimize motion errors, this study presents an effective method to optimize parameters which are connected with motion errors. The proposed method is based upon a second order polynomial regression model and it includes an orthogonal array method to make the effective results of experiments. The validity and reliability of the study were verified on a vertical machining center equipped with FANUC 0MC through a series of experiments and analysis.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.7
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• pp.931-936
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• 2009

Body weights of 862 Angora goats between birth and 36 months of age, recorded on a semiyearly basis from 1988 to 2000, were used to estimate genetic, permanent environmental and phenotypic covariance functions. These functions were estimated by fitting a random regression model with 6th order polynomial for direct additive genetic and animal permanent environmental effects and 4th and 5th order polynomial for maternal genetic and permanent environmental effects, respectively. A phenotypic covariance function was estimated by modelling overall animal and maternal effects. The results showed that the most variable coefficient was the intercept for both direct and maternal additive genetic effects. The direct additive genetic (co)variances increased with age and reached a maximum at about 30 months, whereas the maternal additive genetic (co)variances increased rapidly from birth and reached a maximum at weaning, and then decreased with age. Animal permanent environmental (co)variances increased with age from birth to 30 months with lower rate before 12 months and higher rate between 12 and 30 months. Maternal permanent environmental (co)variances changed little before 6 months but then increased slowly and reached a maximum at about 30 months. These results suggested that the contribution of maternal additive genetic and permanent environmental effects to growth variation differed from those of direct additive genetic and animal permanent environmental effects not only in expression time, but also in action magnitude. The phenotypic (co)variance estimates increased with age from birth to 36 months of age.

• YAKHAK HOEJI
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• v.57 no.1
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• pp.32-36
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• 2013

The purpose of this study was to determine the influence of assay error for improved pharmacokinetic modeling and simulation of vancomycin on the Bayesian and nonlinear least squares regression analysis in 24 Korean gastric cancer patients. Vancomycin 1.0 g was administered intravenously over 1 hr every 12 hr. Three specimens were collected at 72 hr after the first dose from all patients at the following times, at 0.5 hr before regularly scheduled infusion, at 0.5 hr and 2 hr after the end of 1 hr infusion. Serum vancomycin levels were analyzed by fluorescence polarization immunoassay technique with TDX-FLX. The standard deviation (SD) of the assay over its working range had been determined at the serum vancomycin concentrations of 0, 20, 40, 60, 80 and $120{\mu}g/ml$ in quadruplicate. The polynomial equation of vancomycin assay error was found to be SD $({\mu}g/ml)=0.0224+0.0540C+0.00173C^2$ ($R^2=0.935$). There were differences in the influence of weight with vancomycin assay error on pharmacokinetic parameters of vancomycin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynomial equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result suggests the improvement of dosage regimens for the better and safer care of patients receiving vancomycin.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.95-104
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• 2022

This study investigates the complex mechanism of work-family conflict affecting counterproductive behavior of employees based on resource conservation theory and 417 valid samples by using polynomial regression and response surface analysis. Counterproductive work behavior refers to any intentional behavior of an individual that has potential harm to the legitimate interests of the organization or its stakeholders. Results show that first, work-to-family conflict (WFC) and family-to-work conflict (FWC) had four matching types. Compared with "high WFC-low FWC," "low WFC-high FWC" and "low WFC-low FWC" matching conditions, the employee self-control resource depletion and counterproductive work behavior (CWB) are at their highest under "high WFC-high FWC" congruence matching condition. Second, the joint effect of WFC and FWC has a U-shaped relationship with counterproductive behavior. Compared with the "high WFC-low FWC" match state, the level of CWB in the "low WFC-high FWC" match state is higher. Third, the depletion of self-control resources played a mediating role in the effect of WFC on counterproductive behavior. Fourth, emotional intelligence moderated the relationship between the congruence of WFC and FWC and self-control resource depletion. Emotional intelligence was higher, and the positive relationship between the congruence of WFC and FWC and self-control resource depletion was weaker.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Animal Bioscience
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• v.37 no.5
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• pp.817-825
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• 2024

Objective: The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young bulls. Methods: A total of 45 recently weaned males, aged 325.8±28.0 days and weighing 219.9±38.05 kg, were evaluated. The animals were kept on Brachiaria brizantha pastures, received multiple supplementations, and were managed under uniform conditions for 294 days, with evaluations conducted every 56 days. The average growth trajectory was adjusted using ordinary polynomials, Legendre polynomials, and quadratic B-splines. The coefficient of determination, mean absolute deviation, mean square error, the value of the restricted likelihood function, Akaike information criteria, and consistent Akaike information criteria were applied to assess the quality of the fits. For the study of allometric growth, the power model was applied. Results: Ordinary polynomial and Legendre polynomial models of the fifth order provided the best fits. B-splines yielded the best fits in comparing models with the same number of parameters. Based on the restricted likelihood function, Akaike's information criterion, and consistent Akaike's information criterion, the B-splines model with six intervals described the growth trajectory of evaluated animals more smoothly and consistently. In the study of allometric growth, the evaluated traits exhibited negative heterogeneity (b<1) relative to the animals' weight (p<0.01), indicating the precocity of Guzerat cattle for weight gain on pasture. Conclusion: Complementary studies of growth trajectory and allometry can help identify when an animal's weight changes and thus assist in decision-making regarding management practices, nutritional requirements, and genetic selection strategies to optimize growth and animal performance.

• Journal of Power System Engineering
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• v.19 no.2
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• pp.70-77
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• 2015

Nowadays, car manufacturers applied many high strength steels such AHSS or UHSS to car bodies for weight lightening. Therefore, a variety of applied steel sheet to car bodies increased and the needs of simulation to evaluate weldability also increased in order to reduce the cost and time. In this study, resistance spot welding simulations for DP 780 Steel with 1.0 and 1.4 mm thickness were conducted with respect to lobe curve. 2 regression models to estimate tensile shear strength were suggested and they were second order polynomial regression model and optimized second order regression model. The performance of these models was evaluated in terms of the coefficient of determinant and average error rate.