Evidence map›Paper›PMID 35052048›Full record

ArticleEntropy (Basel, Switzerland)2021

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy.

Yuxing Li, Peiyuan Gao, Bingzhao Tang, Yingmin Yi, Jianjun Zhang

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  9. Entropy and Information Theory in Acoustics.Entropy (Basel, Switzerland) · 2022
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  10. Article
  11. Slope Entropy Characterisation: The Role of theEntropy (Basel, Switzerland) · 2022
    Article
  12. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yuxing LiSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.ORCID 0000-0001-5035-223X
Peiyuan GaoSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Bingzhao TangSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Yingmin YiSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Jianjun ZhangSchool of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.

Indexed as

dispersion entropyfeature extractionfluctuation dispersion entropypermutation entropyreverse dispersion entropyship-radiated noise signalslope entropy

Identifiers

PMID35052048
PMCPMC8774539

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Registered trials

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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.