A Study on Improving Pressure Sensor Calibration Based on Multiple Calibration Points and Auto Target Setting

• 발행기관: 한국인공지능학회
• 저자명: Jonghyun OH Jae-Yong HWANG Tumenbat TENGIS Woo-Seong JUNG
• 간행물 정보: 『인공지능연구』Vol.12 No. 4, 35~42쪽, 전체 8쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

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°C to 60°C. 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.

목차

1. Introduction

2. Literature Review

3. Research Methods

4. Results and Discussion

5. Conclusions

References

키워드

Pressure sensors Calibration Auto target Points LDO Linear Regression