Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.9
Journals
JMSE
Special Issues
Data Processing Method for Observing Marine Environment and Underwater Targets
share announcement

Data Processing Method for Observing Marine Environment and Underwater Targets

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Environmental Science".

Deadline for manuscript submissions: closed (10 November 2023) | Viewed by 2661

Special Issue Editor

Dr. Yuxing Li

E-Mail Website
Guest Editor
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: fractal dimension; underwater signal processing; sensor signal processing; denoising; feature extraction; fault diagnosis; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine data, especially environmental data and underwater target data, have immeasurable value, and exploring their enormous value will be crucial for protecting the marine environment and exploring and developing marine resources. With the continuous application of new technologies such as satellite remote sensing observation and navigation observation, a wealth of environmental and underwater target data has been collected. Effective data processing for observing the marine environment and underwater targets is beneficial for marine disaster prevention and mitigation, ecological environmental protection, marine target detection and tracking, and emergency rescue.

In the past few decades, there have been successful methods applied to the processing of marine environment and underwater target data, including mode decomposition methods, nonlinear dynamic analysis methods, and machine learning methods. Mode decomposition methods can be used to decompose complex marine data, exploring the differences between data from the mode perspective; nonlinear dynamic analysis methods can provide features that characterize the complexity of marine data; and machine learning methods can be used for data classification and identification. For example, by decomposing marine environment data such as temperature and humidity into several modes through mode decomposition, extracting the nonlinear dynamics feature of the modes, and then classifying them using machine learning methods, future changes in marine temperature and humidity can be predicted, which is beneficial for predicting climate change, studying marine ecosystems, and protecting the marine environment. Meanwhile, similar methods can be used to process underwater target data to help us understand the distribution of marine species, protect rare and endangered species, and explore deep-sea resources. However, the above methods not only have some shortcomings in themselves, such as the parameter selection problem in mode decomposition and the overfitting problem in machine learning, but also do not consider the data target characteristics under different marine conditions. Therefore, we should further explore advanced data processing methods to provide richer information about the marine environment and underwater targets for human perception and prediction of the marine world.

This research topic is devoted to studying different aspects of advanced marine data processing methods for observing the marine environment and underwater targets, from basic theory to application. Researchers from the global academic field and industry are encouraged to submit high-quality, unpublished, original research articles and review articles in a wide range of fields related to data processing for the marine environment and underwater targets. Potential topics include but are not limited to the following:

  1. Machine learning in marine acidification prediction;
  2. Signal processing for marine temperature data analysis;
  3. Feature extraction for marine soil moisture data;
  4. Noise reduction of underwater ship target data;
  5. Classification and recognition of various marine organisms data;
  6. Analysis of underwater sonar data by mode decomposition method;
  7. Application of the nonlinear dynamic analysis method in marine environment data research.

Dr. Yuxing Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data processing
  • marine environment
  • underwater target
  • machine learning
  • mode decomposition
  • nonlinear dynamic analysis

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 2618 KiB  
Article
Scaled Conjugate Gradient Neural Intelligence for Motion Parameters Prediction of Markov Chain Underwater Maneuvering Target
by Wasiq Ali, Habib Hussain Zuberi, Xin Qing, Abdulaziz Miyajan, Amar Jaffar and Ayman Alharbi
J. Mar. Sci. Eng. 2024, 12(2), 240; https://doi.org/10.3390/jmse12020240 - 29 Jan 2024
Viewed by 614
Abstract
This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater [...] Read more.
This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF). Full article
(This article belongs to the Special Issue Data Processing Method for Observing Marine Environment and Underwater Targets)
►▼ Show Figures

Figure 1

18 pages, 1030 KiB  
Article
Model for Underwater Acoustic Target Recognition with Attention Mechanism Based on Residual Concatenate
by Zhe Chen, Guohao Xie, Mingsong Chen and Hongbing Qiu
J. Mar. Sci. Eng. 2024, 12(1), 24; https://doi.org/10.3390/jmse12010024 (registering DOI) - 20 Dec 2023
Viewed by 750
Abstract
Underwater acoustic target recognition remains a formidable challenge in underwater acoustic signal processing. Current target recognition approaches within underwater acoustic frameworks predominantly rely on acoustic image target recognition models. However, this method grapples with two primary setbacks; the pronounced frequency similarity within acoustic [...] Read more.
Underwater acoustic target recognition remains a formidable challenge in underwater acoustic signal processing. Current target recognition approaches within underwater acoustic frameworks predominantly rely on acoustic image target recognition models. However, this method grapples with two primary setbacks; the pronounced frequency similarity within acoustic images often leads to the loss of critical target data during the feature extraction phase, and the inherent data imbalance within the underwater acoustic target dataset predisposes models to overfitting. In response to these challenges, this research introduces an underwater acoustic target recognition model named Attention Mechanism Residual Concatenate Network (ARescat). This model integrates residual concatenate networks combined with Squeeze-Excitation (SE) attention mechanisms. The entire process culminates with joint supervision employing Focal Loss for precise feature classification. In our study, we conducted recognition experiments using the ShipsEar database and compared the performance of the ARescat model with the classic ResNet18 model under identical feature extraction conditions. The findings reveal that the ARescat model, with a similar quantity of model parameters as ResNet18, achieves a 2.8% higher recognition accuracy, reaching an impressive 95.8%. This enhancement is particularly notable when comparing various models and feature extraction methods, underscoring the ARescat model’s superior proficiency in underwater acoustic target recognition. Full article
(This article belongs to the Special Issue Data Processing Method for Observing Marine Environment and Underwater Targets)
►▼ Show Figures

Figure 1

25 pages, 11736 KiB  
Article
A Novel Denoising Method for Ship-Radiated Noise
by Yuxing Li, Chunli Zhang and Yuhan Zhou
J. Mar. Sci. Eng. 2023, 11(9), 1730; https://doi.org/10.3390/jmse11091730 - 01 Sep 2023
Cited by 5 | Viewed by 902
Abstract
Ship-radiated noise (SN) is one of the most critical signals in the complex marine environment; however, it is inevitably contaminated by the marine environment’s noise as well as noise from other equipment. Thus, the feature extraction and identification of SN becomes very arduous. [...] Read more.
Ship-radiated noise (SN) is one of the most critical signals in the complex marine environment; however, it is inevitably contaminated by the marine environment’s noise as well as noise from other equipment. Thus, the feature extraction and identification of SN becomes very arduous. This paper proposes a denoising method for SN based on successive variational mode decomposition (SVMD), the dual-threshold analysis based on fuzzy dispersion entropy (FuDE) and wavelet packet denoising (WPD), termed SVMD-FuDE-WPD. First, SVMD adaptively decomposes SN into certain intrinsic mode functions (IMFs), which can solve the parameter selection problem of variational mode decomposition (VMD) and suppress the mode mixing of empirical mode decomposition (EMD). After that, the FuDE-based dual-threshold analysis is used to accurately classify IMFs into signal IMFs, noise–signal IMFs and noise IMFs. Finally, the denoised signal could be obtained by reconstructing the signal IMFs and noise–signal IMFs that were denoised using WPD. The classical simulation experiments demonstrate the effectiveness of the proposed denoising method, which performs better than the other four existing denoising methods. And the measured SN experiments show that the attractor trajectories of the proposed method are smoother and more regular, which verifies the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Data Processing Method for Observing Marine Environment and Underwater Targets)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop