{"title":"使用逻辑回归模型的高压灭菌器灭菌过程阶段估计","authors":"L. Ángel, J. Viola, M. Vega, R. Restrepo","doi":"10.1109/STSIVA.2016.7743337","DOIUrl":null,"url":null,"abstract":"This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.","PeriodicalId":373420,"journal":{"name":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Sterilization process stages estimation for an autoclave using logistic regression models\",\"authors\":\"L. Ángel, J. Viola, M. Vega, R. Restrepo\",\"doi\":\"10.1109/STSIVA.2016.7743337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.\",\"PeriodicalId\":373420,\"journal\":{\"name\":\"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STSIVA.2016.7743337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STSIVA.2016.7743337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sterilization process stages estimation for an autoclave using logistic regression models
This paper presents a methodology for an autoclave sterilization process stages estimation using logistic regression models. The Autoclave sterilization process has four stages Pre-Vacuum, Rising Temperature, Sterilizing and Vacuum-Drying, which are classified employing the one vs all algorithm. The logistic regression model employed as variables the Autoclave absolute temperature and pressure. Data from 35 sterilization process were employed to find the logistic regression coefficients. As performance indexes, the precision, coverage and harmonic mean were employed. Results shown that the classification algorithm reached an efficiency of 81% to estimate the sterilization process stages.