Journal of Convergence for Information Technology (융합정보논문지)

Convergence Society for SMB (중소기업융합학회)
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A Study of the Nonlinear Characteristics Improvement for a Electronic Scale using Multiple Regression Analysis

다항식 회귀분석을 이용한 전자저울의 비선형 특성 개선 연구

  • Received : 2019.05.02
  • Accepted : 2019.06.20
  • Published : 2019.06.28
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Abstract

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.

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.

Keywords

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Fig. 1. Simple(1st) Regression Analysis

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Fig. 2. Polynomial Regression Analysis

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Fig. 3. Load cell with strain gauge and Wheatstone bridge circuit

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Fig. 4. Arduino module and load cell with Amplifier

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Fig. 5. Matrix coefficient Calculation for Simple(1storder) linear regression model using measured mass data

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Fig. 6. Calculation for Simple(1st order) linear regression model using excel curve fitting

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Fig. 7. Matrix coefficient Calculation for 3rd order polynomial regression model using measured mass data

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Fig. 8. Calculation for 3rd order polynomial regression model using excel curve fitting

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Fig. 9. Errors for each polynomial regression model

Table 1. Measured results using standard weight

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