Evidence map›Paper›PMID 39987193›Full record

ArticleScientific reports2025

ICEEMDAN-RPE-AITD algorithm for magnetic field signals of magnetic targets.

Binjie Lu, Zongji Li, Xiaobing Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Binjie LuNaval University of Engineering, Wuhan, 430033, Hubei, China.
Zongji LiNaval University of Engineering, Wuhan, 430033, Hubei, China.
Xiaobing ZhangNaval University of Engineering, Wuhan, 430033, Hubei, China. professorzxb@163.com.

Funding

National Natural Science Foundation of China 12304535
6 · The paper itself

Abstract

Magnetic targets magnetic field signals contain rich target feature information, but geomagnetic background noise, sensor noise and so on will cause serious interference to the targets magnetic field signals. To solve the denoising problem of magnetic targets magnetic field signals, a denoising algorithm based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), reverse permutation entropy (RPE) and adaptive interval threshold denoising (AITD), named ICEEMDAN-RPE-AITD algorithm, is proposed. The algorithm firstly adopts ICEEMDAN to decompose the signal and get the intrinsic mode functions (IMFs), then calculates the RPE value of IMFs and sets dual thresholds to classify signal IMFs (signal-dominant), noisy IMFs (mixed signal and noise), noise IMFs (noise-dominant), and finally carries out AITD on noisy IMFs, and combined the signal IMFs with the processed noisy IMFs to reconstruct the denoised signals. The denoising test is conducted using the ship model magnetic field signals, and the effectiveness of the algorithm is verified by taking the signal-to-noise ratio and the root mean square error as the evaluation indexes. Finally, the effectiveness of the algorithm is verified by using the measured magnetic field signal test of the ship, taking the noise intensity and correlation dimension as the evaluation indexes and combining with the power spectral density analysis.

Indexed as

Adaptive interval threshold denoisingImproved complete ensemble empirical mode decomposition with adaptive noiseMagnetic field signalsMagnetic targetsReverse permutation entropy

Identifiers

PMID39987193
PMCPMC11846947

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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.