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http://dx.doi.org/10.15207/JKCS.2019.10.4.017

Machine Learning-based Quality Control and Error Correction Using Homogeneous Temporal Data Collected by IoT Sensors  

Kim, Hye-Jin (Dept. Computer Science, Kwangwoon University)
Lee, Hyeon Soo (R&D Center, JUBIX Co., Ltd.)
Choi, Byung Jin (R&D Center, JUBIX Co., Ltd.)
Kim, Yong-Hyuk (Dept. Computer Science, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.4, 2019 , pp. 17-23 More about this Journal
Abstract
In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.
Keywords
Convergence; Machine Learning; Quality Control; Data Correction; Weather Data;
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Times Cited By KSCI : 1  (Citation Analysis)
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