• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.42-45
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• 2017

It is known that power line communication systems have more noise than general wired communication systems due to the high voltage that flows in power line cables, and the noise causes a serious performance degradation. In order to mitigate performance degradation due to such noise, this paper proposes an artificial intelligence algorithm based on polynomial regression, which detects signals in the impulse noise environment in the power line communication system. The polynomial regression method is used to predict the original transmitted signal from the impulse noise signal. Simulation results show that the signal detection performance in the impulse noise environment of the power line communication is improved through the artificial intelligence algorithm proposed in this paper.

• 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.

• Transactions of Materials Processing
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• v.26 no.1
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• pp.18-27
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• 2017

A comparative study was performed on strain-compensated Arrhenius-type constitutive models established with two regression methods: polynomial regression and regression Kriging. For measurements at high temperatures, experimental data of 70Cr3Mo steel were adopted from previous research. An Arrhenius-type constitutive model necessitates strain compensation for material constants to account for strain effect. To associate the material constants with strain, we first evaluated them at a set of discrete strains, then capitalized on surrogate modeling to represent the material constants as a function of strain. As a result, disparate flow stress models were formed via the two different regression methods. The constructed constitutive models were examined systematically against measured flow stresses by validation methods. The predicted material constants were found to be quite accurate compared to the actual material constants. However, notable mismatches between measured and predicted flow stresses were revealed by the proposed validation techniques, which carry out validation with not the entire, but a single tensile test case.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.337-339
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• 2004

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses 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. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.775-784
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• 2022

In this study, to determine the optimal order of the full-logged I-D-F polynomial equation, which is mainly used to calculate the probable rainfall over a temporal rainfall duration, the probable rainfall was calculated and the regression coefficients of the full-logged I-D-F polynomial equation was estimated. The optimal variable of the polynomial equation for each station was selected using a stepwise selection method, and statistical significance tests were performed through ANOVA. Using these results, the statistically appropriately calculated rainfall intensity equation for each station was presented. As a result of analyzing the variable selection outputs of the full-logged I-D-F polynomial equation at 9 stations in Gyeongbuk, the 1^st to 3^rd order equations at 6 stations and the incomplete 3^rd order at 1 station were determined as the optimal equations. Since the 1^st order equation is similar to the Sherman type equation and the 2^nd order one is similar to the general type equation, it was presented as a unified form of rainfall intensity equation for convenience of use by increasing the number of independent variables. Therefore, it is judged that there is no statistical problem in considering only the 3^rd order polynomial regression equation for the full-logged I-D-F.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.179-188
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• 1998

Local polynomial regression estimation is the most popular one among kernel type regression estimator. In local polynomial regression function esimation bandwidth selection is crucial problem like the kernel estimation. When the regression curve has complicated structure variable bandwidth selection will be appropriate. In this paper, we propose a variable bandwidth selection method fully data driven. We will choose the bandwdith by selecting minimising estiamted MSE which is estimated by the pilot bandwidth study via croos-validation method. Monte carlo simulation was conducted in order to show the superiority of proposed bandwidth selection method.

• 모자간호학회지
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• v.1
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• pp.34-44
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• 1991

The purposes of this study were to predict newborn birthweight by use of gestational period and fundal height and to identify growth curve of fundal height according to gestational period and growth curve of newborn birthweight according to fundal height. The subjects for the study were 802 women who delivered the normal newborn babies at Seoul National University Hospital from Sep. 1, 1981 to Aug.31, 1986. The data were collected bit chart review and analyzed nth SPSS program. The results of study were as follows : 1. The multiple regression equation ($R^2$=0.416) used for the prediction of newborn birthweight was y=(newborn birthweight, kg)=-4.421+0.075$x_1$(fundal height, cm)+0.053$x_2$(gestational period, weeks)+0.016$x_3$(abdominal girth, cm)+0.010$x_4$(maternal height, cm) 2. The growth curve of fundal height according to gestational period was obtained by polynomial regression. The regression equation was Y(fundal height, cm)=-36.78+18.58$log_ex$(gestational period, weeks) The growth curve of newborn birth weight according to fundal height was obtained by polynomial regression. The regression equation was Y(newborn birthweight, kg)=-8.09+3.27$log_ex$ (Fundal Height, cm) 3. In the following subgroups no significant difference was found in fundal height : engaged vs. nonengaged presentation, and nulliparous vs. multiparous women.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.664-674
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• 2001

In this paper, as a new category of fuzzy-neural networks architecture, we propose Fuzzy Polynomial Neural Networks (FPNN) and discuss a comprehensive design methodology related to its architecture. FPNN dwells on the ideas of fuzzy rule-based computing and neural networks. The FPNN architecture consists of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as Fuzzy Polynomial Neuron(FPN). The conclusion part of the rules, especially the regression polynomial, uses several types of high-order polynomials such as linear, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. It is worth stressing that the number of the layers and the nods in each layer of the FPNN are not predetermined, unlike in the case of the popular multilayer perceptron structure, but these are generated in a dynamic manner. With the aid of two representative time series process data, a detailed design procedure is discussed, and the stability is introduced as a measure of stability of the model for the comparative analysis of various architectures.