Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
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A Study on Improving Pressure Sensor Calibration Based on Multiple Calibration Points and Auto Target Setting

  • Jonghyun OH (Department of Information & Communication Engineering, Daejeon University) ;
  • Jae-Yong HWANG (Department of Information & Communication Engineering, Daejeon University) ;
  • Tumenbat TENGIS (Department of Information & Communication Engineering, Daejeon University) ;
  • Woo-Seong JUNG (Department of Information & Communication Engineering, Daejeon University)
  • Received : 2024.10.14
  • Accepted : 2024.12.05
  • Published : 2024.12.30
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Abstract

Pressure sensors are essential equipment for precise measurements in industrial and research fields, requiring calibration and target value setting for each sample to ensure high accuracy. This study proposes an automated target value prediction method based on a polynomial regression model to enhance pressure sensor accuracy and evaluates its effectiveness. Experiments were conducted over a pressure range of 0 to 45 bar and a temperature range of -5℃ to 60℃. By expanding the calibration points from the previous two to four, linearity error was improved from 0.380% to 0.116%. In the conventional method, theoretical output values were manually calculated based on LDO voltage, and target values were set accordingly. However, this study employed a method that uses Polynomial Features (degree=2) transformation followed by a Linear Regression model to automatically predict target values. This approach allowed samples to more precisely follow the target voltage. This study demonstrates that an automated target value setting with multiple calibration points can contribute to improving the accuracy of pressure sensor measurements.

Keywords

Acknowledgement

This research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2021RIS-004)

References

  1. Chen, H.-Y., & Chen, C. (2019). Determination of Optimal Measurement Points for Calibration Equations-Examples by RH Sensors. Sensors, 19(1213). https://doi.org/10.3390/s19051213
  2. Dikbas, F. (2024). Forecasting extreme precipitations by using polynomial regression. Idojaras - Quarterly Journal of the Hungarian Meteorological Service, 128(3), 379. https://doi.org/10.28974/idojaras.2024.3.1
  3. Kwak, H., Jung, Y., Jeon, I., & Lee, C. (2024). Method for Estimating Fruit Load of Apple Trees Using YOLOv5 and Polynomial Regression Models. Journal of the Institute of Electronics Engineers of Korea, 28(2), 28-35.
  4. Park, D., Jin, S., Lee, G., & Choi, D.-H. (2013). Material Arrangement Optimization for Automotive BIW Considering a Large Number of Design Variables. Transactions of KSAE, 21(3), 15-23. https://doi.org/10.13106/KODISA.Journals.2013.vol21.no3.11.
  5. Park, S.-H. (2016). A study on the development of materials utilizing the least square method for the convergent teaching. Korea University of Foreign Studies, Graduate School of Education, Mathematics Education Major, Korea.
  6. Tengis, T., Hwang, J.-Y., Kim, Y.-M., & Jung, W.-S. (2024). A study on improving pressure sensor calibration with multiple calibration points. Proceedings of the 13th ICAEIC-2024.
  7. Park, S.-M., Kim, Y.-E., & Lim, J.-S. (2024). A Study on Improving Precision in Calibration of Vehicle Camera Sensors. Korean Society of Mechanical Engineers Spring and Fall Academic Conference, Incheon.
  8. Zou, M., Xu, Y., Jin, J., Chu, M., & Huang, W. (2023). Accurate Nonlinearity and Temperature Compensation Method for Piezoresistive Pressure Sensors Based on Data Generation. Sensors, 23(6167). https://doi.org/10.3390/s23136167