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

IoT센서로 수집된 균질 시간 데이터를 이용한 기계학습 기반의 품질관리 및 데이터 보정

Kim, Hye-Jin;Lee, Hyeon Soo;Choi, Byung Jin;Kim, Yong-Hyuk

  • Received : 2019.01.28
  • Accepted : 2019.04.20
  • Published : 2019.04.28


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.


Convergence;Machine Learning;Quality Control;Data Correction;Weather Data


Supported by : Ministry of Land, Infrastructure and Transport