• 전기학회논문지
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• 제67권3호
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• pp.460-466
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• 2018

For the efficient energy consumption in an urban railway station, it is necessary to know the patterns of electrical loads for each usage in detail. The electrical loads in an urban railway station have different characteristics from other normal electrical load, such as the peak load timing during a day. The lighting, HVAC, communication, and commercial loads make up large amount of electrical load for equipment in an urban railway station, and each of them has the unique specificity. These loads for each usage were estimated without measuring device by the polynomial regression method with big data such as total amount of electrical load and weather data. In the simulation with real data, the optimal polynomial regression model was third order polynomial regression model with 9 or 10 independent variables.

• 융합정보논문지
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• 제9권6호
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• pp.1-6
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• 2019

본 연구에서는 다항식 회귀분석(Polynomial regression analysis) 방법을 이용하여 비선형 특성을 갖는 전자저울의 질량 추정 모델 개발이 이루어 졌다. 전자저울에 사용되는 로드셀의 출력 단자 전압을 기준 질량 추를 사용하여 직접 측정하였고 이 데이터를 이용하여 MS Office 엑셀의 행렬식 계산과 데이터 추세선 분석 기능을 이용하여 다항식 회귀모델을 구하였다. 5kg까지 측정 가능한 로드셀 전자저울을 사용하여 100g단위로 질량을 측정하였고 다항식 회귀분석(Multiple regression analysis) 모델을 구하였으며, 단순(1차), 2차, 3차 다항식 회귀분석에 대한 오차를 구하였다. 각 모델에 대한 회귀 방정식의 적합도 분석을 위해 결정계수(Coefficient of determination)를 제시하여 추정 질량과 측정 데이터와의 상관관계를 나타내었다. 본 연구에서 제안하는 3차 다항식 모델을 이용하여 추정 값의 표준편차가 10g, 결정계수 1.0으로 상당히 정확한 모델을 얻었다. 본 연구에 사용된 선형 회귀 분석 이론을 바탕으로 최근 인공지능 분야에서 많이 사용되고 있는 로지스틱 회귀 분석(Logistic regression analysis)을 활용하여 기상예측, 신약개발, 경제지표 분석 등의 분야에 대한 다양한 연구를 수행할 수 있을 것으로 생각된다.

• Journal of the Korean Statistical Society
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• 제9권1호
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• pp.61-77
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• 1980

In response surface experiments, a polynomial model is often used to fit the response surface by the method of least squares. However, if the vectors of predictor variables are multicollinear, least squares estimates of the regression parameters have a high probability of being unsatisfactory. Hoerland Kennard have demonstrated that these undesirable effects of multicollinearity can be reduced by using "ridge" estimates in place of the least squares estimates. Ridge regrssion theory in literature has been mainly concerned with selection of k for the first order polynomial regression model and the precision of $\hat{\beta}(k)$, the ridge estimator of regression parameters. The problem considered in this paper is that of selecting k of ridge regression for a given polynomial regression model with an arbitrary order. A criterion is proposed for selection of k in the context of integrated mean square error of fitted responses, and illustrated with an example. Also, a type of admissibility condition is established and proved for the propose criterion.criterion.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• 핵의학기술
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• 제20권2호
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• pp.69-74
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• 2016

Insulin 83건을 검사 대상으로, 장비 WIZARD(PerkinElmer, USA)와 DREAM- G-10(Shinjin, KOREA)의 Graph Algorithm 중에 표준곡선과 신뢰성이 가장 높은 Excel의 추세선은 다항식 추세선이다. 다음으로 다항식 추세선식을 이용하여 표준농도 1개씩을 제외하여 표준농도의 회귀식에 미치는 영향을 비교한 결과 최고농도($315{\mu}IU/m{\ell}$)의 평균값이 표준물질 6개로 실시한 표준 회귀식을 이용한 평균값과 비교하여 49%나 평균값이 저하되었다. 단 낮은 농도에서는 영향이 미비하였다. 마지막으로 WIZARD의 Point to Point형식과 DREAM G-10의 Point to Point형식이 적합성이 높고, DREAM G-10(Point to Point)와 Excel 다항식 추세선이 적합성이 높으며, Excel 다항식 추세선과 DREAM G-10(2'nd order Polynomial)의 적합성이 높다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.800-802
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• 2000

In this paper, we propose the Self-Organizing Networks(SON) based on competitive Fuzzy Polynomial Neuron(FPN) for the optimal design of nonlinear process system. The SON architectures consist of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as FPN which includes either the simplified or regression Polynomial fuzzy inference rules. The proposed SON is a network resulting from the fusion of the Polynomial Neural Networks(PNN) and a fuzzy inference system. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as liner, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. Chaotic time series data used to evaluate the performance of our proposed model.

• 한국해양공학회지
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• 제26권2호
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• 방사성폐기물학회지
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• 제19권4호
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• pp.447-457
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• 2021

Graphite Isotope Ratio Method (GIRM) can be used to estimate plutonium production in a graphite-moderated reactor. This study presents verification results for the GIRM combined with a 3-D polynomial regression function to estimate cumulative plutonium production in a graphite-moderated reactor. Using the 3-D Monte-Carlo method, verification was done by comparing the cumulative plutonium production with the GIRM. The GIRM can estimate plutonium production for specific sampling points using a function that is based on an isotope ratio of impurity elements. In this study, the ¹⁰B/¹¹B isotope ratio was chosen and calculated for sampling points. Then, 3-D polynomial regression was used to derive a function that represents a whole core cumulative plutonium production map. To verify the accuracy of the GIRM with polynomial regression, the reference value of plutonium production was calculated using a Monte-Carlo code, MCS, up to 4250 days of depletion. Moreover, the amount of plutonium produced in certain axial layers and fuel pins at 1250, 2250, and 3250 days of depletion was obtained and used for additional verification. As a result, the difference in the total cumulative plutonium production based on the MCS and GIRM results was found below 3.1% with regard to the root mean square (RMS) error.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권9호
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• pp.430-438
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• 2001

In this study, we introduce the adaptive Polynomial Neuro-Fuzzy Networks(PNFN) architecture generated from the fusion of fuzzy inference system and PNN algorithm. The PNFN dwells on the ideas of fuzzy rule-based computing and neural networks. Fuzzy inference system is applied in the 1st layer of PNFN and PNN algorithm is employed in the 2nd layer or higher. From these the multilayer structure of the PNFN is constructed. In order words, in the Fuzzy Inference System(FIS) used in the nodes of the 1st layer of PNFN, either the simplified or regression polynomial inference method is utilized. And as the premise part of the rules, both triangular and Gaussian like membership function are studied. In the 2nd layer or higher, PNN based on GMDH and regression polynomial is generated in a dynamic way, unlike in the case of the popular multilayer perceptron structure. That is, the PNN is an analytic technique for identifying nonlinear relationships between system's inputs and outputs and is a flexible network structure constructed through the successive generation of layers from nodes represented in partial descriptions of I/O relatio of data. The experiment part of the study involves representative time series such as Box-Jenkins gas furnace data used across various neurofuzzy systems and a comparative analysis is included as well.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.695-705
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• 2003

In this article, we propose a regression and correlation analysis via dynamic graphs and implement them in Java Web Start. For the polynomial relations between dependent and independent variables, dynamic graphics are implemented for both polynomial regression and spline estimates for an instant model selection. The results include basic statistics. They are available both as a web-based service and an application.