{"title":"基于高斯噪声的快速经验模态分解","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":"{\"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}","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}
Fast Empirical Mode Decomposition Based on Gaussian Noises
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.
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