The Simple Linear Regression Model The Ordinary Least Squares Method

1977 ◽  
pp. 48-68
Author(s):  
A. Koutsoyiannis
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Ordinary Least Squares Method

Automatic Measurement of Stator Resistance for Permanent Magnet Synchronous Motors Based on Simple Linear Regression Model

2013 ◽  
Vol 732-733 ◽  
pp. 1142-1146
Author(s):  
Feng Li ◽  
Chao Ying Xia
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Permanent Magnet ◽  
Linear Regression Model ◽  
Automatic Measurement ◽  
Ordinary Least Squares ◽  
Experimental Result ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Stator Resistance

In the field-oriented vector control system of permanent magnet synchronous motor (PMSM), the performance is directly dependent on the accuracy of motor parameters. Because of nonlinear factors of the inverter, the difficulty in accurate compensation of voltage error and low accuracy of measurement are problems which exist in stator resistance identification approach based on traditional DC voltmeter-ammeter method. In this paper, the ordinary least squares method based on simple linear regression model is used to finish the automatic measurement of stator resistance. It is verified that the method has higher measurement accuracy according to the experimental result. Furthermore, nonlinear factors of the inverter are not needed to be taken into account using the method, which has comparatively strong practicality in engineering.

scholarly journals PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Concentration Estimation ◽  
Single Feature ◽  
The Difference ◽  
Pm2.5 Concentration

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

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