非球形颗粒的大小和形状估计使用机器学习

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}
引用次数: 0

摘要

对燃气轮机吸入的颗粒进行实时测量将有助于准确监测发动机的健康状况并确保安全运行。然而,典型的光学方法假设粒子是球形的,而大多数摄取的粒子不是球形的。我们提出了一种机器学习模型的新应用,该模型将散射和消光观测作为输入,并估计非球形颗粒的形状(通过纵横比)和大小。给出了多分类和回归层的总体方法,以及使用模拟输入的三个测试用例的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信