{"title":"使用机器学习的气压传感器的纯电校准","authors":"Hadi Najar","doi":"10.1109/TRANSDUCERS.2019.8808801","DOIUrl":null,"url":null,"abstract":"This paper reports a novel, rapid and economical approach to calibrate barometric micromechanical capacitive pressure sensors via electrical measurement only using a machine learning approach. Conventional calibration approaches that use physical means are time consuming and require specialized equipment. We proposed and experimentally validated an electrical only approach through machine learning to calibrate the pressure sensor over a wide range of pressures. Using this novel approach, the experimental results show an average absolute accuracy of 1.74hPa within 600hPa to 1100hPa pressure range. The typical absolute accuracy falls well within the ±1hPa. This approach reduces the test time by more than 85%.","PeriodicalId":6672,"journal":{"name":"2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)","volume":"43 1","pages":"1977-1980"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Electrical Only Calibration of Barometric Pressure Sensors Using Machine Learning\",\"authors\":\"Hadi Najar\",\"doi\":\"10.1109/TRANSDUCERS.2019.8808801\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper reports a novel, rapid and economical approach to calibrate barometric micromechanical capacitive pressure sensors via electrical measurement only using a machine learning approach. Conventional calibration approaches that use physical means are time consuming and require specialized equipment. We proposed and experimentally validated an electrical only approach through machine learning to calibrate the pressure sensor over a wide range of pressures. Using this novel approach, the experimental results show an average absolute accuracy of 1.74hPa within 600hPa to 1100hPa pressure range. The typical absolute accuracy falls well within the ±1hPa. This approach reduces the test time by more than 85%.\",\"PeriodicalId\":6672,\"journal\":{\"name\":\"2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)\",\"volume\":\"43 1\",\"pages\":\"1977-1980\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TRANSDUCERS.2019.8808801\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TRANSDUCERS.2019.8808801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electrical Only Calibration of Barometric Pressure Sensors Using Machine Learning
This paper reports a novel, rapid and economical approach to calibrate barometric micromechanical capacitive pressure sensors via electrical measurement only using a machine learning approach. Conventional calibration approaches that use physical means are time consuming and require specialized equipment. We proposed and experimentally validated an electrical only approach through machine learning to calibrate the pressure sensor over a wide range of pressures. Using this novel approach, the experimental results show an average absolute accuracy of 1.74hPa within 600hPa to 1100hPa pressure range. The typical absolute accuracy falls well within the ±1hPa. This approach reduces the test time by more than 85%.