Chi Young Moon, C. Edwards, Alka Panda, G. Byun, K. Lowe
{"title":"非球形颗粒的大小和形状估计使用机器学习","authors":"Chi Young Moon, C. Edwards, Alka Panda, G. Byun, K. Lowe","doi":"10.1109/RAPID49481.2020.9195671","DOIUrl":null,"url":null,"abstract":"A real time measurement of particles being ingested by gas turbines would prove useful for accurately monitoring engine health and ensuring safe operations. However, typical optical methods assume spherical particles, which most ingested particles are not. We present a novel application of machine learning models that takes scattering and extinction observations as inputs and estimates non-spherical particle shape (via aspect ratio) and size. The overall method of multiple classification and regression layers, as well as the results from three test cases using simulated inputs are presented.","PeriodicalId":220244,"journal":{"name":"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)","volume":"389 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-spherical particle size and shape estimation using machine learning\",\"authors\":\"Chi Young Moon, C. Edwards, Alka Panda, G. Byun, K. Lowe\",\"doi\":\"10.1109/RAPID49481.2020.9195671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A real time measurement of particles being ingested by gas turbines would prove useful for accurately monitoring engine health and ensuring safe operations. However, typical optical methods assume spherical particles, which most ingested particles are not. We present a novel application of machine learning models that takes scattering and extinction observations as inputs and estimates non-spherical particle shape (via aspect ratio) and size. The overall method of multiple classification and regression layers, as well as the results from three test cases using simulated inputs are presented.\",\"PeriodicalId\":220244,\"journal\":{\"name\":\"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)\",\"volume\":\"389 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAPID49481.2020.9195671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAPID49481.2020.9195671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-spherical particle size and shape estimation using machine learning
A real time measurement of particles being ingested by gas turbines would prove useful for accurately monitoring engine health and ensuring safe operations. However, typical optical methods assume spherical particles, which most ingested particles are not. We present a novel application of machine learning models that takes scattering and extinction observations as inputs and estimates non-spherical particle shape (via aspect ratio) and size. The overall method of multiple classification and regression layers, as well as the results from three test cases using simulated inputs are presented.