{"title":"Fast Empirical Mode Decomposition Based on Gaussian Noises","authors":"Risheng Wang, Jianjun Zhou, Jie Chen, Yanjie Wang","doi":"10.1109/MCSI.2016.059","DOIUrl":null,"url":null,"abstract":"Mode-mixing, boundary effects and necessary extrema lacking and etc. are the main problems involved in empirical mode decomposition (EMD). The paper presents an improved empirical mode decomposition based on assisted signals: Gaussian noises. Firstly, the given 1D Gaussian noise and its negative counterpart are added to the original respectively to construct the two s to be decomposed. Secondly, the decomposed IMFs from the two signals are added together to get the IMFs, in which the added noises are canceled out with less mode-mixing and boundary effects. Lastly, the efficiency and performance of the method are given through theoretical analysis and experiments.","PeriodicalId":421998,"journal":{"name":"2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCSI.2016.059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Mode-mixing, boundary effects and necessary extrema lacking and etc. are the main problems involved in empirical mode decomposition (EMD). The paper presents an improved empirical mode decomposition based on assisted signals: Gaussian noises. Firstly, the given 1D Gaussian noise and its negative counterpart are added to the original respectively to construct the two s to be decomposed. Secondly, the decomposed IMFs from the two signals are added together to get the IMFs, in which the added noises are canceled out with less mode-mixing and boundary effects. Lastly, the efficiency and performance of the method are given through theoretical analysis and experiments.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
