Calibration of Environmental Sensor Data Using a Linear Regression Technique

Gyu-Sik Kim; Chungyong Kim

Title:
Calibration of Environmental Sensor Data Using a Linear Regression Technique

Authors:
Chungyong Kim | Gyu-Sik Kim
DOI:
https://doi.org/10.31142/ijtsrd7060

Cite This Article :

Chungyong Kim | Gyu-Sik Kim "Calibration of Environmental Sensor Data Using a Linear Regression Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1, December 2017, pp.715-719, URL: https://www.ijtsrd.com/papers/ijtsrd7060.pdf

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Abstract :
The linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. A linear regression analysis method is used to improve the performance of the instruments using light scattering method. The data measured by these instruments have to be converted to actual PM10 concentrations using some factors. These findings propose that the instruments using light scattering method can be used to measure and control the PM10 concentrations of the underground subway stations. In this study, the reliability of the instrument for PM measurement using light scattering method was improved with the help of a linear regression analysis technique to continuously measure the PM10 concentrations in the subway stations. In addition, the linear regression analysis technique was also applied to calibrate a radon counter with the help of an expensive radon measuring apparatus.

Keywords :
PM10, linear regression analysis, subway station, radon counter, radon measuring apparatus

Publication Details:

Unique Identification Number : IJTSRD7060

Published In : Volume-2 | Issue-1, December 2017

Page Number(s) : 715-719

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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