{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:36:15Z","timestamp":1765546575914,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701046","41674107","41874119","41574064"],"award-info":[{"award-number":["61701046","41674107","41874119","41574064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.<\/jats:p>","DOI":"10.3390\/s20123373","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T12:16:57Z","timestamp":1592223417000},"page":"3373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition"],"prefix":"10.3390","volume":"20","author":[{"given":"Kai","family":"Chen","sequence":"first","affiliation":[{"name":"National Demonstration Center for Experimental Electrical &amp; Electronic Education, Yangtze University, Jingzhou 434023, China"},{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Kai","family":"Xie","sequence":"additional","affiliation":[{"name":"National Demonstration Center for Experimental Electrical &amp; Electronic Education, Yangtze University, Jingzhou 434023, China"},{"name":"School of Electronic and Information, Yangtze University, Jingzhou 434023, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7339-3130","authenticated-orcid":false,"given":"Chang","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Xin-Gong","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education, Wuhan 430100, China"}],"role":[{"role":"author","vocab":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.cpc.2012.08.008","article-title":"SeismicWaveTool: Continuous and discrete wavelet analysis and filtering for multichannel seismic data","volume":"184","year":"2013","journal-title":"Comput. 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