{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T21:23:35Z","timestamp":1783373015415,"version":"3.54.6"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T00:00:00Z","timestamp":1692748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Planning Project of Xiamen City","award":["3502Z20191021"],"award-info":[{"award-number":["3502Z20191021"]}]},{"name":"Science and Technology Planning Project of Xiamen City","award":["2022H0044"],"award-info":[{"award-number":["2022H0044"]}]},{"name":"Science and Technology Planning Project of Xiamen City","award":["XDA23030203"],"award-info":[{"award-number":["XDA23030203"]}]},{"name":"Science and Technology Planning Project of Xiamen City","award":["3502Z20191021"],"award-info":[{"award-number":["3502Z20191021"]}]},{"name":"Science and Technology Planning Project of Xiamen City","award":["2022H0044"],"award-info":[{"award-number":["2022H0044"]}]},{"name":"Science and Technology Planning Project of Xiamen City","award":["XDA23030203"],"award-info":[{"award-number":["XDA23030203"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["3502Z20191021"],"award-info":[{"award-number":["3502Z20191021"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2022H0044"],"award-info":[{"award-number":["2022H0044"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23030203"],"award-info":[{"award-number":["XDA23030203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23177347","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T08:20:30Z","timestamp":1692778830000},"page":"7347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Machine-Learning-Based Calibration of Temperature Sensors"],"prefix":"10.3390","volume":"23","author":[{"given":"Ce","family":"Liu","sequence":"first","affiliation":[{"name":"College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China"},{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Big Data & Software Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haowei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,23]]},"reference":[{"key":"ref_1","first-page":"118","article-title":"Development of Intelligent Sensible Temperature Sensor","volume":"41","author":"Wang","year":"2022","journal-title":"Tech. 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