{"title":"基于ERT系统和v锥流量计特征融合的气水两相流型识别","authors":"C. Tan, F. Dong","doi":"10.1109/IST.2009.5071655","DOIUrl":null,"url":null,"abstract":"Gas-water two-phase flow is of significance in industrial process and scientific research field. Its flow regime determines the flow parameters and the method of flow measurement. Precise identification of flow regime has been a popular subject for a long time. In this work, a series of experiments on gas-water two-phase flows were conducted in a 50mm diameter horizontal pipe, the flow parameters were measured with an Electrical Resistance Tomography (ERT) system and a V-cone meter. A method of feature extraction from the above two instruments is presented and the flow regime was recognized by using a Support Vector Machine (SVM) method. Additionally, the feature fusion methods are selected and compared to discuss the method of improving the flow regime recognition performance.","PeriodicalId":373922,"journal":{"name":"2009 IEEE International Workshop on Imaging Systems and Techniques","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Gas-water two-phase flow regime identification with feature fusion from an ERT system and a V-cone meter\",\"authors\":\"C. Tan, F. Dong\",\"doi\":\"10.1109/IST.2009.5071655\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gas-water two-phase flow is of significance in industrial process and scientific research field. Its flow regime determines the flow parameters and the method of flow measurement. Precise identification of flow regime has been a popular subject for a long time. In this work, a series of experiments on gas-water two-phase flows were conducted in a 50mm diameter horizontal pipe, the flow parameters were measured with an Electrical Resistance Tomography (ERT) system and a V-cone meter. A method of feature extraction from the above two instruments is presented and the flow regime was recognized by using a Support Vector Machine (SVM) method. Additionally, the feature fusion methods are selected and compared to discuss the method of improving the flow regime recognition performance.\",\"PeriodicalId\":373922,\"journal\":{\"name\":\"2009 IEEE International Workshop on Imaging Systems and Techniques\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Workshop on Imaging Systems and Techniques\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IST.2009.5071655\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Workshop on Imaging Systems and Techniques","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST.2009.5071655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gas-water two-phase flow regime identification with feature fusion from an ERT system and a V-cone meter
Gas-water two-phase flow is of significance in industrial process and scientific research field. Its flow regime determines the flow parameters and the method of flow measurement. Precise identification of flow regime has been a popular subject for a long time. In this work, a series of experiments on gas-water two-phase flows were conducted in a 50mm diameter horizontal pipe, the flow parameters were measured with an Electrical Resistance Tomography (ERT) system and a V-cone meter. A method of feature extraction from the above two instruments is presented and the flow regime was recognized by using a Support Vector Machine (SVM) method. Additionally, the feature fusion methods are selected and compared to discuss the method of improving the flow regime recognition performance.