• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.8
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• pp.82-90
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• 2019

We propose an equivalent model of a sintered metal mesh filter calculated by Ergun's equation and polynomial regression for the CFD analysis of breakaway devices at a hydrogen fueling station. CFD analysis of filters that cause high pressure loss is essential because breakaway devices in high-pressure hydrogen conditions require low pressure loss. A differential pressure experiment with a filter was performed in a low-pressure air condition considering similarities. An equivalent model was developed by deriving the resistance value by the polynomial regression using the experimental results. The results of CFD analysis using the equivalent model show that there was almost no error in the operating condition of the breakaway device compared to the experimental results. Through this work, we believe that the proposed equivalent model of a filter can be applied to the analysis of breakaway devices in hydrogen fueling stations. We will study how to optimize the shape and position of the filter in breakaway devices using the developed equivalent model.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.257-266
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• 2022

Inundation damage is increasing every year due to localized heavy rain and an increase of rainfall exceeding the design frequency. Accordingly, the importance of hydraulic structures for flood control and defense is also increasing. The hydraulic structures are designed according to its purpose and performance, and the amount of flood is an important calculation factor. However, in Korea, design rainfall is used as input data for hydrological analysis for the design of hydraulic structures due to the lack of sufficient data and the lack of reliability of observation data. Accurate probability rainfall and its temporal distribution are important factors to estimate the design rainfall. In practice, the regression equation of temporal distribution for the design rainfall is calculated using the cumulative rainfall percentage of Huff's quartile method. In addition, the 6^th order polynomial regression equation which shows high overall accuracy, is uniformly used. In this study, the optimized regression equation of temporal distribution is derived using the variable selection method according to the principle of parsimony in statistical modeling. The derived regression equation of temporal distribution is verified through the significance test. As a result of this study, it is most appropriate to derive the regression equation of temporal distribution using the stepwise selection method, which has the advantages of both forward selection and backward elimination.

• 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.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2977-2979
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• 2000

A Fuzzy Hybrid-Multilayer Perceptron (FH-MLP) Structure is proposed in this paper. proposed FH-MLP is not a fixed architecture. that is to say. the number of layers and the number of nodes in each layer of FH-MLP can be generated to adapt to the changing environment. FH-MLP consists of two parts. one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules. and its fuzzy system operates with Gaussian or Triangular membership functions in premise part and constants or regression polynomial equation in consequence part. the other is polynomial nodes which several types of high-order polynomial such as linear. quadratic. and cubic form are used and is connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method. time series data for gas furnace process has been applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.701-704
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• 2014

This Paper proposed the analysis and prediction method of beverage sales. We assumed weather had a relationship with beverage sales. We got the output as sales amount from a temperature and humidity of weather as input by using polynomial equation. We had modelling as quadric function with input and output data. In order to verify the effectiveness of proposed method, the sales data were collected over a 4 months during February 2014. The results showed that the proposed method can estimate sales data.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• Journal of the Korean Society of Clothing and Textiles
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• v.14 no.2
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• pp.152-163
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• 1990

The purpose of this study is to experimentally analyze the relationship between structural characteristics of cotton fabrics and their cool-and-warm felling in order to develop more comfortable fabrics. Comfort in textile products has been emphasized as consumers preferred performance to fashion of clothing. Thermal comfort of clothing is a basic parameter of the comfort sensation which is usually represented by the cool-and-warm feeling felt by human skin. Cloo-and-warm feeling is perceived by the heat flux which transfers heat energy stored in an object to skin. We feel warm (cool) if the temperature of nerve extremity in skin ascends (descends). As cool-and-warm feeling determines the comfort sensation of clothing, it is important to develop new comfort fabrics. Although considerable works have been made on the body, clothing, and environment, there has been no research study on the structural characteristics of fabrics and their cool and warm feeling. Cool-and-warm feeling is closely related to the transient heat transfer property. This research study used the cotton fabrics manufactured in Korea as sample and measured $q_{max}$ value with thermal property measuring instrument (Thermo-Labo II type). $q_{max}$ values estimated by polynomial regression equation were compared with those observed in this study. This study also identified the structural parameters of cotton fabrics for a specific range of $q_{max}$ values. The findings of this study can be summarized as follows: 1) As the thickness, porosity and air permeability of cotton fabrics increase, $q_{max}$ value decreases. 2) As the fabric count and over factor of cotton fabrics increase, $q_{max}$ value also increases. 3) $q_{max}$ values have been estimated by simple and polynomial regression equations developed in this study. Regression curves which have been plotted by polynomial regression equations also provided with the range of structural parameters for a specific range of $q_{max}$ values of cotton fabrics. This study would be significant in that it has identified the structural Parameters for the cool-and-warm feeling of cotton fabric at $65\%$ relative humidity.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.75-81
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• 1999

This paper presents in the rrethod power demand estimated of consuming facility algorithm using orthogonal polynomial regression rmdel. Estimation rmdel presented can use mathematical rrethod consists. of extrapolation and correlation rrethod, Computation tirre and capacity of presented rmdel was rmre economic than multiple regression rrodel because low-order equation can use in the high-order equation without sorre correction, and vice-versa. Therefore this rmthed can be very usefulness rmthed in the power demand estimation Fourth-order rrodel was very good armng this rrodel that was coJTJp)Sed the estimation rmdel of second, third and fourth-order. Power demand estimated result of consuming facility using correlation rrethod was good in the percentage error of about 2[%1 Also It was to verify efficiency and awroPJiation the estimated rmdel that estimation percentage error was about 1[%] in the oower demand estimated result of 1997.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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• pp.401-411
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• 2009

In this paper, we correct pulse wave velocity(PWV) with heart-rate and derive regression equations to estimate intima-media thickness(IMT). Widely used methods for diagnosis of arteriosclerosis are IMT and PWV. Arterial wall stiffness determines the degree of energy absorbed by the elastic aorta and its recoil in diastole but there is not correlation between sclerosis and IMT in an existing study. In this study, we will correct PWV with heart-rate and get regression equation to estimate IMT using heart-rate correction index(HCI). We executed experiments for this study. Made up question of physical condition and measured electrocardiogram(ECG), photoplethysmogram (PPG) of finger-tip and toe-tip and ultrasound image of carotid artery. Calculated PWV and IMT using ECG, PPG and ultrasound image. We found that every p-value between PWV and IMT is not significant(<0.05). But p-value between IMT and HCI which is a corrected PWV using heart-rate is significant(>0.01). We use HCI and various measured parameter for estimating regression equation and apply backward estimation to select parameters for regression analysis. Result of backward estimation, found that only HCI is possible to derive proper regression equation of IMT. Relationship between PWV and IMT is the second order. Result of regression equation of E-H PWV is $R^2$=0.735, adj $R^2$=0.711. This is the best correlation value. We calculate error of its analysis for verification of earlobe PWV regression equation. Its result is RMSEP=0.0328, MAPE(%) = 4.7622. Like this regression analysis, we know that HCI is useful parameter and relationship between PWV, HCI and IMT. In addition, we are able to suggest possibility which is that we can get different parameter of prediction throughout just one measurement.

• Journal of Food Hygiene and Safety
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• v.26 no.2
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• pp.120-124
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• 2011

Predictive mathematical models were developed for predicting the kinetics of growth of Listeria monocytogenes in smoked salmon, which is the popular ready-to-eat foods in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At these storage temperature, the primary growth curve fit well ($r^2$=0.989~0.996) to a Gompertz equation to obtain specific growth rate (SGR) and lag time (LT). The Polynomial model for natural logarithm transformation of the SGR and LT as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). Results indicate L. monocytogenes growth was affected by temperature mainly, and SGR model equation is $365.3-31.94^*Temperature+0.6661^*Temperature^{\wedge^2}$ and LT model equation is $0.1162-0.01674^*Temperature+0.0009303^*Temperature{\wedge^2}$. As storage temperature decreased $30^{\circ}C$ to $4^{\circ}C$, SGR decreased and LT increased respectively. Polynomial model was identified as appropriate secondary model for SGR and LT on the basis of most statistical indices such as bias factor (1.01 by SGR, 1.55 by LT) and accuracy factor (1.03 by SGR, 1.58 by LT).