Machine-Learning-Based Calibration of Temperature Sensors
Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/17/7347 |
| _version_ | 1827727859338182656 |
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| author | Ce Liu Chunyuan Zhao Yubo Wang Haowei Wang |
| author_facet | Ce Liu Chunyuan Zhao Yubo Wang Haowei Wang |
| author_sort | Ce Liu |
| collection | DOAJ |
| description | Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research. |
| first_indexed | 2024-03-10T23:13:58Z |
| format | Article |
| id | doaj.art-fa0b317c1fec4e13a777241ceddf4073 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:58Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fa0b317c1fec4e13a777241ceddf40732023-11-19T08:48:38ZengMDPI AGSensors1424-82202023-08-012317734710.3390/s23177347Machine-Learning-Based Calibration of Temperature SensorsCe Liu0Chunyuan Zhao1Yubo Wang2Haowei Wang3College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaKey Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaSchool of Big Data & Software Engineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, ChinaTemperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.https://www.mdpi.com/1424-8220/23/17/7347temperature sensorcalibrationartificial neural network (ANN)accuracystabilitylinear regression |
| spellingShingle | Ce Liu Chunyuan Zhao Yubo Wang Haowei Wang Machine-Learning-Based Calibration of Temperature Sensors Sensors temperature sensor calibration artificial neural network (ANN) accuracy stability linear regression |
| title | Machine-Learning-Based Calibration of Temperature Sensors |
| title_full | Machine-Learning-Based Calibration of Temperature Sensors |
| title_fullStr | Machine-Learning-Based Calibration of Temperature Sensors |
| title_full_unstemmed | Machine-Learning-Based Calibration of Temperature Sensors |
| title_short | Machine-Learning-Based Calibration of Temperature Sensors |
| title_sort | machine learning based calibration of temperature sensors |
| topic | temperature sensor calibration artificial neural network (ANN) accuracy stability linear regression |
| url | https://www.mdpi.com/1424-8220/23/17/7347 |
| work_keys_str_mv | AT celiu machinelearningbasedcalibrationoftemperaturesensors AT chunyuanzhao machinelearningbasedcalibrationoftemperaturesensors AT yubowang machinelearningbasedcalibrationoftemperaturesensors AT haoweiwang machinelearningbasedcalibrationoftemperaturesensors |