{"title":"期末项目简报-物理特征工程、经典机器学习和深度学习模型的组合评估,用于大规模同步数据","authors":"M. Bariya","doi":"10.2172/1837767","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":211077,"journal":{"name":"North American Synchrophasor Initiative (NASPI) Working Group Meeting, Fall 2021, Virtual","volume":"237 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Final Project Briefing - Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale\",\"authors\":\"M. Bariya\",\"doi\":\"10.2172/1837767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":211077,\"journal\":{\"name\":\"North American Synchrophasor Initiative (NASPI) Working Group Meeting, Fall 2021, Virtual\",\"volume\":\"237 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"North American Synchrophasor Initiative (NASPI) Working Group Meeting, Fall 2021, Virtual\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2172/1837767\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"North American Synchrophasor Initiative (NASPI) Working Group Meeting, Fall 2021, Virtual","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2172/1837767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Final Project Briefing - Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale