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Theories and Techniques in Intelligent Digital Twins in Marine Science...
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Theories and Techniques in Intelligent Digital Twins in Marine Science and Engineering

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3777

Special Issue Editors

Prof. Dr. Junyu Dong

E-Mail Website
Guest Editor
The College of Information Science and Engineering, Ocean University of China, Qingdao, China
Interests: ocean data science; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hui Yu

E-Mail Website
Guest Editor
Faculty of Creative and Cultural Industries, School of Creative Technologies, University of Portsmouth, Portsmouth PO1 2DJ, UK
Interests: ocean intelligent computing; digital vision technology; human-machine interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Shu Zhang

E-Mail Website
Guest Editor
Faculty of Information Science and Engineering, Ocean University of China, Qingdao, China
Interests: underwater 3d vision; rov interaction; computer vision; deep learning
Dr. Hongjie Ma

E-Mail Website
Guest Editor
School of Energy and Electronic Engineering, University of Portsmouth, Portsmouth PO1 2DJ, UK
Interests: electronic control for marine engineering; artificial intelligence-based diagnosis and optimization

Special Issue Information

Dear Colleagues,

Research on creating virtual copies from the real world has been pursued in many areas from science studies, process simulations, and event prediction to industrial engineering and product manufacturing, among others. It is clear that work from the domains of marine science and engineering will be dramatically augmented by the utilizations of this concept, which is commonly called the digital twin (DT). This Special Issue provides opportunities for exchanging the theories and techniques in intelligence that can help to create accurate and precise digital twins, which will enhance studies in both marine science and engineering.

There are many scenarios where people will find it difficult to study and interact directly with real-world marine entities due to various physical limitations. Digital twins can easily bridge the gap between humans and the physical world across spatial and temporal dimensions. For example, with smart digital twins, researchers can understand processes in physical oceanography intuitively, and conduct an event prediction or anomaly detection efficiently. To create such intelligent digital twins, advanced theories and techniques should be explored in areas such as intelligent marine big data acquisition and processing, hardware and algorithms for intelligent data computing, and multi-modal enhanced data visualization, among others. This Special Issue will aim at consolidating views in recent trends and major challenges for those topics. We present a platform and forum to disseminate state-of-the-art research and trends in this context and help to exchange new thoughts and perspectives that can further push the progress of constructing a future smart marine world.

The Special Issue topics of interest include but are not limited to:

  • Vision-based 3D modelling for marine applications;
  • Multi-sensory based underwater 3D perception;
  • Deep learning in data computing;
  • Point-cloud manipulation and processing;
  • Ubiquitous intelligence and computing for marine science;
  • Digital twin in oceanology;
  • Multi-modal data visualization;
  • Deep learning-based data understanding.

Prof. Dr. Junyu Dong
Prof. Dr. Hui Yu
Dr. Shu Zhang
Dr. Hongjie Ma
Guest Editors

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

  • digital twin
  • 3D reconstruction
  • intelligent computing
  • ocean data
  • underwater intelligence
  • multi-modal data
  • deep learning
  • visual computing

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Published Papers (3 papers)

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Research

18 pages, 8849 KiB  
Article
Research on Model Reduction of AUV Underwater Support Platform Based on Digital Twin
by Daohua Lu, Yichen Ning, Jia Wang, Kaijie Du and Cancan Song
J. Mar. Sci. Eng. 2024, 12(9), 1673; https://doi.org/10.3390/jmse12091673 - 19 Sep 2024
Viewed by 629
Abstract
Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In [...] Read more.
Digital twin technology, as a data-driven and model-driven innovation means, plays a crucial role in the process of digital transformation and intelligent upgrading of the marine industry, helping the industry to move towards a new stage of more intelligent and efficient development. In order to solve the defects of the Autonomous Underwater Vehicle (AUV) underwater support platform structure deformation field, digital twin technology and model reduction technology are applied to an AUV underwater support platform, and a five-dimensional digital twin model of the AUV underwater support platform is studied, including five dimensions: physical world, digital world, twin data center, service application, and data connection. The digital twin of the subsea support platform is established by using the digital twin modeling technology. The POD method is used to calculate the deformation field matrix of the support structure of the subsea support platform under the 0–5 sea state, and the corresponding eigenvalues and eigenvectors are obtained. By intercepting the eigenvectors corresponding to the eigenvalues of the high energy proportion, the low-order equation is constructed, and the reduced-order model under each sea state can be quickly solved. The experimental results show that the model reduction technology can greatly shorten the model solving time, and the calculated results are highly consistent with the simulation results of the finite element full-order model, which can realize the rapid analysis of the deformation response of the subsea support platform structure, and provide a theoretical basis and technical support for the subsequent simulation, state evaluation, visual monitoring, and predictive maintenance. Full article
(This article belongs to the Special Issue Theories and Techniques in Intelligent Digital Twins in Marine Science and Engineering)
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10 pages, 722 KiB  
Article
WaterSAM: Adapting SAM for Underwater Object Segmentation
by Yang Hong, Xiaowei Zhou, Ruzhuang Hua, Qingxuan Lv and Junyu Dong
J. Mar. Sci. Eng. 2024, 12(9), 1616; https://doi.org/10.3390/jmse12091616 - 11 Sep 2024
Viewed by 774
Abstract
Object segmentation, a key type of image segmentation, focuses on detecting and delineating individual objects within an image, essential for applications like robotic vision and augmented reality. Despite advancements in deep learning improving object segmentation, underwater object segmentation remains challenging due to unique [...] Read more.
Object segmentation, a key type of image segmentation, focuses on detecting and delineating individual objects within an image, essential for applications like robotic vision and augmented reality. Despite advancements in deep learning improving object segmentation, underwater object segmentation remains challenging due to unique underwater complexities such as turbulence diffusion, light absorption, noise, low contrast, uneven illumination, and intricate backgrounds. The scarcity of underwater datasets further complicates these challenges. The Segment Anything Model (SAM) has shown potential in addressing these issues, but its adaptation for underwater environments, AquaSAM, requires fine-tuning all parameters, demanding more labeled data and high computational costs. In this paper, we propose WaterSAM, an adapted model for underwater object segmentation. Inspired by Low-Rank Adaptation (LoRA), WaterSAM incorporates trainable rank decomposition matrices into the Transformer’s layers, specifically enhancing the image encoder. This approach significantly reduces the number of trainable parameters to 6.7% of SAM’s parameters, lowering computational costs. We validated WaterSAM on three underwater image datasets: COD10K, SUIM, and UIIS. Results demonstrate that WaterSAM significantly outperforms pre-trained SAM in underwater segmentation tasks, contributing to advancements in marine biology, underwater archaeology, and environmental monitoring. Full article
(This article belongs to the Special Issue Theories and Techniques in Intelligent Digital Twins in Marine Science and Engineering)
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18 pages, 15894 KiB  
Article
Real-Time Wave Simulation of Large-Scale Open Sea Based on Self-Adaptive Filtering and Screen Space Level of Detail
by Xi Duan, Jian Liu and Xinjie Wang
J. Mar. Sci. Eng. 2024, 12(4), 572; https://doi.org/10.3390/jmse12040572 - 28 Mar 2024
Viewed by 1504
Abstract
The real-time simulation technology of large-scale open sea surfaces has been of great importance in fields such as computer graphics, ocean engineering, and national security. However, existing technologies typically have performance requirements or platform limitations, and the two are often impossible to balance. [...] Read more.
The real-time simulation technology of large-scale open sea surfaces has been of great importance in fields such as computer graphics, ocean engineering, and national security. However, existing technologies typically have performance requirements or platform limitations, and the two are often impossible to balance. Based on the camera-view-based screen space level of detail strategy and virtual camera pose adaptive filtering strategy proposed in this article, we have developed a fast and comprehensive solution for rendering large-scale open sea surfaces. This solution is designed to work without the need for special hardware extensions, making it easy to deploy across various platforms. Additionally, it enhances the degrees of freedom of virtual camera movement. After conducting performance tests under various camera poses, our filtering strategy was found to be effective. Notably, the time cost of simulation using 60 waves at the height of 6 m above sea level was only 0.184 ms. In addition, we conducted comparative experiments with four state-of-the-art algorithms currently in use, and our solution outperformed the others with the best performance and suboptimal visual effects. These results demonstrate the superiority of our approach in terms of both efficiency and effectiveness. Full article
(This article belongs to the Special Issue Theories and Techniques in Intelligent Digital Twins in Marine Science and Engineering)
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