{"title":"PSO-based autocalibration for differential pressure level sensor","authors":"P. Esmaili, P. Esmaili, F. Cavedo, M. Norgia","doi":"10.1109/ICAIoT53762.2021.00013","DOIUrl":null,"url":null,"abstract":"To achieve desired level of accuracy in piezoresistive pressure sensors based on silicon, calibration should be performed frequently. In this paper, an Intelligent auto-calibration approach is proposed to update characterization curve in differential pressure-based level sensor. This intelligent method is based on particle swarm optimization method. To achieve optimum results, different factors such as self-knowledge and social knowledge coefficients in addition to inertia weight have been considered in this intelligent auto-calibration method. The compensation process is the last part of the system. It leads to achieve the up bounded measurement error becomes limited to 0.25 mm.","PeriodicalId":344613,"journal":{"name":"2021 International Conference on Artificial Intelligence of Things (ICAIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Artificial Intelligence of Things (ICAIoT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIoT53762.2021.00013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
To achieve desired level of accuracy in piezoresistive pressure sensors based on silicon, calibration should be performed frequently. In this paper, an Intelligent auto-calibration approach is proposed to update characterization curve in differential pressure-based level sensor. This intelligent method is based on particle swarm optimization method. To achieve optimum results, different factors such as self-knowledge and social knowledge coefficients in addition to inertia weight have been considered in this intelligent auto-calibration method. The compensation process is the last part of the system. It leads to achieve the up bounded measurement error becomes limited to 0.25 mm.