结合EEMD和柔性小波阈值函数的加权信号去噪方法

J. Sensors Pub Date : 2022-08-24 DOI:10.1155/2022/5314532
Dan Liu, Xiang Liao, Shuo Ouyang, Chaoshun Li
{"title":"结合EEMD和柔性小波阈值函数的加权信号去噪方法","authors":"Dan Liu, Xiang Liao, Shuo Ouyang, Chaoshun Li","doi":"10.1155/2022/5314532","DOIUrl":null,"url":null,"abstract":"This paper proposes a method of combining ensemble empirical mode decomposition (EEMD) with a novel flexible wavelet threshold function to reduce the random error in the weighing result that caused by the nonstationary and nonlinear noise in quality characteristic parameter measurement equipment. The original signal is first processed by EEMD, and then, all intrinsic mode functions (IMFs) are processed by a novel flexible threshold function proposed by this paper. Finally, the denoised signal is obtained by adding reconstructed IMFs and residual. Through theoretical analysis, the proposed threshold function can retain more useful information and have continuity at the segmentation point. Moreover, the addition of adjustable parameters makes it more adaptable. Its advantages are verified by comparing the denoised results with other threshold functions in the simulation model. In weighing experiment, the validity of the novel flexible threshold function in weighing signal denoising is verified, and the effectiveness of the proposed method is also quantitatively confirmed by comparing the random errors of the original signal and the denoised signal.","PeriodicalId":14776,"journal":{"name":"J. Sensors","volume":"194 1","pages":"1-17"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach\",\"authors\":\"Dan Liu, Xiang Liao, Shuo Ouyang, Chaoshun Li\",\"doi\":\"10.1155/2022/5314532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method of combining ensemble empirical mode decomposition (EEMD) with a novel flexible wavelet threshold function to reduce the random error in the weighing result that caused by the nonstationary and nonlinear noise in quality characteristic parameter measurement equipment. The original signal is first processed by EEMD, and then, all intrinsic mode functions (IMFs) are processed by a novel flexible threshold function proposed by this paper. Finally, the denoised signal is obtained by adding reconstructed IMFs and residual. Through theoretical analysis, the proposed threshold function can retain more useful information and have continuity at the segmentation point. Moreover, the addition of adjustable parameters makes it more adaptable. Its advantages are verified by comparing the denoised results with other threshold functions in the simulation model. In weighing experiment, the validity of the novel flexible threshold function in weighing signal denoising is verified, and the effectiveness of the proposed method is also quantitatively confirmed by comparing the random errors of the original signal and the denoised signal.\",\"PeriodicalId\":14776,\"journal\":{\"name\":\"J. Sensors\",\"volume\":\"194 1\",\"pages\":\"1-17\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/5314532\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2022/5314532","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

本文提出了一种将集成经验模态分解(EEMD)与一种新颖的柔性小波阈值函数相结合的方法,以减小质量特性参数测量设备中非平稳和非线性噪声对称重结果造成的随机误差。首先对原始信号进行EEMD处理,然后对所有的本征模态函数(IMFs)进行柔性阈值函数处理。最后,将重构后的imf和残差相加,得到去噪后的信号。通过理论分析,所提出的阈值函数能够保留更多的有用信息,并且在分割点处具有连续性。此外,可调参数的加入使其更具适应性。通过与仿真模型中其他阈值函数去噪结果的比较,验证了该方法的优越性。在称重实验中,验证了柔性阈值函数对称重信号去噪的有效性,并通过对比原信号和去噪后信号的随机误差,定量地验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach
This paper proposes a method of combining ensemble empirical mode decomposition (EEMD) with a novel flexible wavelet threshold function to reduce the random error in the weighing result that caused by the nonstationary and nonlinear noise in quality characteristic parameter measurement equipment. The original signal is first processed by EEMD, and then, all intrinsic mode functions (IMFs) are processed by a novel flexible threshold function proposed by this paper. Finally, the denoised signal is obtained by adding reconstructed IMFs and residual. Through theoretical analysis, the proposed threshold function can retain more useful information and have continuity at the segmentation point. Moreover, the addition of adjustable parameters makes it more adaptable. Its advantages are verified by comparing the denoised results with other threshold functions in the simulation model. In weighing experiment, the validity of the novel flexible threshold function in weighing signal denoising is verified, and the effectiveness of the proposed method is also quantitatively confirmed by comparing the random errors of the original signal and the denoised signal.
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