• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.31-38
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• 2000

Relationships between hydrologic variables are often nonlinear. Usually the functional form of such a relationship is not known a priori. A multivariate, nonparametric regression methodology is provided here for approximating the underlying regression function using locally weighted polynomials. Locally weighted polynomials consider the approximation of the target function through a Taylor series expansion of the function in the neighborhood of the point of estimate. The utility of this nonparametric regression approach is demonstrated through an application to nonparametric short term forecasts of the biweekly Great Salt Lake volume.volume.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1776-1779
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• 2003

This research presents Reverse Engineering of a Impeller. The modeling introduced in this paper adopts polynomial regression that is utilizing approximating technique. The measured data are obtained from measuring with Coordinate Measuring Machine. This paper introduces efficient methods of Reverse Engineering using Polynomial Regression.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.376-380
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• 2002

This research presents modeling of a functional surface which is a constructed free-formed surface. The modeling introduced in this paper adopts polynomial regression that is utilizing approximating technique. The measured data are obtained from measuring with Coordinate Measuring Machine. This paper introduces efficient methods of Reverse Engineering using Polynomial Regression.

• Korean Journal of Clinical Pharmacy
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• v.19 no.1
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• pp.43-49
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• 2009

토브라마이신은 그람음성균 감염에 사용하는 아미노글리코사이드계 항생제로 이독성 및 신독성 등의 부작용과 큰 개인차로 혈중농도 모니터를 통한 투여계획이 필요한 약물이다. 본 연구에서는 16명의 위암환자에서 비선형 최소자승 회귀분석과 베이시안 분석에 의한 토브라마이신의 약물동태에 분석오차의 영향에 대하여 연구하였다. 약물투여는 토브라마이신 1-2 mg/kg을 30분에 걸쳐 8시간 간격으로 등속 주입하였으며, 혈액 채취는 정상상태에 도달되었다고 판단되는 첫 약물투여 72시간 후에, 약물 주입 5분전과 주입이 끝난 뒤 30분과 2시간에서 세차례 채취하였다. 혈청중 약물농도는 형광편광면역법으로 측정 하였다. 분석오차를 위해 0, 1, 2, 4, 8 및 12 ${\mu}g/mL$에 해당하는 토브라마이신 혈중농도(C)을 네차례 측정하여 각 혈중농도의 표준편차 (SD)을 구하였다. 토브라마이신 분석오차를 구하기 위한 다항식이 SD = 0.0224+0.0540C+0.00173C2, $R^2$ = 0.935이었다. 이 식에서 구한 SD 값으로 분석시 가중치를 주었을 때, 비선형 최소자승 회귀분석에 의한 토브라마이신의 약물동태학적 파라메타 ($V_d$, $K_{el}$, $K_{slpoe}$, $t_{1/2}$)에 유의성있는 영향을 주었으나, 베이시안 분석에 의한 토브라마이신의 약물동태학적 파라메타에는 영향이 없었다. 이 다항식으로 부터 구한 분석오차를 토브라마이신의 비선형 최소자승 회귀분석을 이용한 약물동태 연구 및 파라메타 분석에 적용하여 좀 더 정확한 투여용량을 결정할 수 있으며, 더 나아가 토브라마이신 약물동태 시뮬레이션 연구에 응용할 수 있다.

• Korean Journal of Clinical Pharmacy
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• v.18 no.1
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• pp.11-17
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• 2008

아미카신은 그람음성균 감염에 사용하는 아미노글리코사이드계 항생제로 이독성 및 신독성 등의 부작용과 큰 개인차로 혈중농도 모니터를 통한 투여계획이 필요한 약물이다. 본 연구에서는 16명의 위암환자에서 비선형최소자승 회귀분석과 베이시안 분석에 의한 아미카신의 약물동태에 분석오차의 영향을 연구하였다. 약물투여는 아미카신 7.5 mg/kg을 30분에 걸쳐 12시간 간격으로 등속 주입하였으며, 혈액 채취는 정상상태에 도달되었다고 판단되는 첫 약물투여 72시간 후에, 약물 주입 5분전과 주입이 끝난 뒤 30분과 2시간에서 세차례 채취하였다. 혈청중 약물농도는 형광편광면역법으로 측정하였다. 분석오차를 위해 0, 5, 15, 30, 60 및 $80\;{\mu}g/ml$에 해당하는 아미카신 혈중농도(C)을 네차례 측정하여 각 혈중농도의 표준편차 (SD)을 구하였다 아미카신 분석오차를 위한 다항식이 $SD=0.3017+(0.00538C)+(0.00112C^2)$, $R^2=0.974$이었다 이 식에서 구한 SD 값으로 분석시 가중치를 주었을 때, 비선형최소자승 회귀분석에 의한 아미카신의 약물동태학적 파라메타($V_d$, $K_{el}$, $K_{slpoe}$, $t_{1/2}$)에 유의성있는 영향을 주었으나, 베이시안 분석에 의한 아미카신의 약물동태학적 파라메타에는 영향이 없었다. 이 다항식에 의한 분석오차를 비선형최소자승 회귀분석에 의한 아미카신 약물동태학적 파라메타 분석시 적절히 사용하면 안전하고 효율적인 투여계획을 할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.25-34
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• 2008

By comparing two images, which are captured with the same scene at different time, we can detect a set of outliers, such as occluding objects due to moving vehicles. To reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. For an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. In this paper, an algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergence property of the estimates of the noise variance. Using a correction constant for the estimate of the noise variance is proposed. The correction enables the detection algorithm robust to the choice of thresholds for selecting outliers. Numerical analysis using both synthetic and Teal images are also shown in this paper to show the robust performance of the detection algorithm.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.386-388
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• 2013

합산 영역 테이블(Summed Area Table)을 사용하면 현재 픽셀 주변으로 임의의 사각 영역의 평균을 모든 픽셀을 읽을 필요 없이, 단 4번의 픽셀의 합과 차로 표시할 수 있다. 그러나 많은 픽셀의 값이 누적되는 경우 부동소수점 표현의 정밀도가 떨어지는 문제가 발생한다. 따라서 본 논문에서는 합산 영역 테이블의 정밀도를 향상시키기 위한 방법으로 선형 회귀분석(linear regression)을 이용한 오프셋을 사용할 것을 제안한다. 회귀분석을 통해 구축한 다항식을 통해 픽셀 그리고 채널 별로 다른 오프셋을 적용하여 정밀도를 효과적으로 향상하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.