Evidence map›Paper›PMID 29278380›Full record

ArticleSensors (Basel, Switzerland)2017

Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.

Yuxing Li, Yaan Li, Xiao Chen, Jing Yu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. A new hybrid prediction model of COVID-19 daily new case data.Engineering applications of artificial intelligence · 2023
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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yuxing LiSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710000, China. liyuxing@mail.nwpu.edu.cn.
Yaan LiSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710000, China. liyaan@nwpu.edu.cn.
Xiao ChenSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710000, China. chenxiao@mail.nwpu.edu.cn.ORCID 0000-0001-6501-5922
Jing YuSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710000, China. yujing@nwpu.edu.cn.ORCID 0000-0002-3285-6490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships.

Indexed as

correlation coefficient (CC)denoisingsecondary variational mode decomposition (2VMD)ship-radiated noise (SN)variational mode decomposition (VMD)

Identifiers

PMID29278380
PMCPMC5795591

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.