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Artificial Intelligence in Marine Science and Engineering

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

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 31176

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Prof. Dr. Fausto Pedro García Márquez

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Guest Editor

Ingneium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain
Interests: artificial intelligence; maintenance; management; advanced analytics; data science
Special Issues, Collections and Topics in MDPI journals

Dr. Mayorkinos Papaelias

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Guest Editor

School of Metallurgy and Materials, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: non-destructive evaluation; structural health monitoring; condition monitoring; machine learning; digital twins
Special Issues, Collections and Topics in MDPI journals

Dr. Simone Marini

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Guest Editor

1. Institute of Marine Sciences (ISMAR) of the National Research Council (CNR), Lerici, Italy
2. Stazione Zoologica Anton Dohrn (SZN), Naples, Italy
Interests: data science; artificial intelligence; machine learning; knowledge discovery; ocean observation; marine science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue covers research in Artificial Intelligence in Marine Science and Engineering, and shows how to apply it to many different professional areas, e.g., engineering, economic and management. Each paper of the Special Issue is contributed by a different author from across the world and covers a different area of Artificial Intelligence applied to Marine. The Special Issue connects the analytic principles with business practice and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, economic and planning abilities of management in Marine Science and Engineering. It also refers to other disciplines such as finance, marketing, behavioral economics, and risk analysis. This Special Issue is of particular interest to researchers, engineers and economists who are developing new advances in analytics but also to practitioners working on this subject.

Prof. Dr. Fausto Pedro García Márquez
Prof. Dr. Mayorkinos Papaelias
Prof. Dr. Simone Marini
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

statistical foundations
mathematical foundations
data science
analytics
analytics of complex data
analytics of domain
analytics of network
analytics of organizational
analytics of social
analytics of behaviour
system characteristics
creation and extraction
processing
representation and modelling
learning and discovery
fusion and integration
presentation and visualization of complex data
data behaviour
knowledge and intelligence
data analytics
pattern recognition
knowledge discovery
machine learning
deep analytics
deep learning
intelligent processing
behaviors and systems
computation
optimization
big data architecture
big data infrastructure
big data computing
big data matching
big data indexing
big data query processing
big data mapping
big data search
big data retrieval
renewable energy review
renewable energy surveys
renewable energy trends
renewable energy prospects
renewable energy applications
renewable energy opportunities
renewable energy applications
intelligent devices and services
solar energy
wind energy
hydro energy
tidal energy
geothermal energy
biomass energy
new renewable energy source

Published Papers (10 papers)

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Editorial

Jump to: Research

5 pages, 222 KiB

Open AccessEditorial

Artificial Intelligence in Marine Science and Engineering

by Fausto Pedro García Márquez, Mayorkinos Papaelias and Simone Marini

J. Mar. Sci. Eng. 2022, 10(6), 711; https://doi.org/10.3390/jmse10060711 - 24 May 2022

Cited by 8 | Viewed by 1895

Abstract

This Special Issue covers research in Artificial Intelligence in Marine Science and Engineering and shows how to apply it to many different professional areas, e [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

Research

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29 pages, 5864 KiB

Open AccessArticle

Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

by Chun Chen, Zhi-Hua Hu and Lei Wang

J. Mar. Sci. Eng. 2021, 9(12), 1439; https://doi.org/10.3390/jmse9121439 - 16 Dec 2021

Cited by 17 | Viewed by 4395

Abstract

In order to improve the horizontal transportation efficiency of the terminal Automated Guided Vehicles (AGVs), it is necessary to focus on coordinating the time and space synchronization operation of the loading and unloading of equipment, the transportation of equipment during the operation, and the reduction in the completion time of the task. Traditional scheduling methods limited dynamic response capabilities and were not suitable for handling dynamic terminal operating environments. Therefore, this paper discusses how to use delivery task information and AGVs spatiotemporal information to dynamically schedule AGVs, minimizes the delay time of tasks and AGVs travel time, and proposes a deep reinforcement learning algorithm framework. The framework combines the benefits of real-time response and flexibility of the Convolutional Neural Network (CNN) and the Deep Deterministic Policy Gradient (DDPG) algorithm, and can dynamically adjust AGVs scheduling strategies according to the input spatiotemporal state information. In the framework, firstly, the AGVs scheduling process is defined as a Markov decision process, which analyzes the system’s spatiotemporal state information in detail, introduces assignment heuristic rules, and rewards the reshaping mechanism in order to realize the decoupling of the model and the AGVs dynamic scheduling problem. Then, a multi-channel matrix is built to characterize space–time state information, the CNN is used to generalize and approximate the action value functions of different state information, and the DDPG algorithm is used to achieve the best AGV and container matching in the decision stage. The proposed model and algorithm frame are applied to experiments with different cases. The scheduling performance of the adaptive genetic algorithm and rolling horizon approach is compared. The results show that, compared with a single scheduling rule, the proposed algorithm improves the average performance of task completion time, task delay time, AGVs travel time and task delay rate by 15.63%, 56.16%, 16.36% and 30.22%, respectively; compared with AGA and RHPA, it reduces the tasks completion time by approximately 3.10% and 2.40%. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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12 pages, 4497 KiB

Open AccessArticle

Application of Feature Point Matching Technology to Identify Images of Free-Swimming Tuna Schools in a Purse Seine Fishery

by Qinglian Hou, Cheng Zhou, Rong Wan, Junbo Zhang and Feng Xue

J. Mar. Sci. Eng. 2021, 9(12), 1357; https://doi.org/10.3390/jmse9121357 - 01 Dec 2021

Cited by 3 | Viewed by 1769

Abstract

Tuna fish school detection provides information on the fishing decisions of purse seine fleets. Here, we present a recognition system that included fish shoal image acquisition, point extraction, point matching, and data storage. Points are a crucial characteristic for images of free-swimming tuna schools, and point algorithm analysis and point matching were studied for their applications in fish shoal recognition. The feature points were obtained by using one of the best point algorithms (scale invariant feature transform, speeded up robust features, oriented fast and rotated brief). The k-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new points, which means that new data points will be assigned a value based on how closely they match the points that exist in the database. Finally, we tested the model, and the experimental results show that the proposed method can accurately and effectively recognize tuna free-swimming schools. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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15 pages, 2501 KiB

Open AccessArticle

An Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay for Path Planning of Unmanned Surface Vehicles

by Zhengwei Zhu, Can Hu, Chenyang Zhu, Yanping Zhu and Yu Sheng

J. Mar. Sci. Eng. 2021, 9(11), 1267; https://doi.org/10.3390/jmse9111267 - 13 Nov 2021

Cited by 15 | Viewed by 3360

Abstract

Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path planning as its crucial technology has developed into a hot research direction in the field of USV research. This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the selection and calculation of the target Q value action to eliminate overestimation. The prioritized experience replay method is adopted to extract experience samples from the experience replay unit, increase the utilization rate of actual samples, and accelerate the training speed of the neural network. Then, the neural network is optimized by introducing a dueling network structure. Finally, the soft update method is used to improve the stability of the algorithm, and the dynamic

ϵ

-greedy method is used to find the optimal strategy. The experiments are first conducted in the Open AI Gym test platform to pre-validate the algorithm for two classical control problems: the Cart pole and Mountain Car problems. The impact of algorithm hyperparameters on the model performance is analyzed in detail. The algorithm is then validated in the Maze environment. The comparative analysis of simulation experiments shows that IPD3QN has a significant improvement in learning performance regarding convergence speed and convergence stability compared with DQN, D3QN, PD2QN, PDQN, PD3QN. Also, USV can plan the optimal path according to the actual navigation environment with the IPD3QN algorithm. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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23 pages, 5425 KiB

Open AccessFeature PaperArticle

Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

by Chih-Chiang Wei

J. Mar. Sci. Eng. 2021, 9(11), 1257; https://doi.org/10.3390/jmse9111257 - 12 Nov 2021

Cited by 2 | Viewed by 2071

Abstract

Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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16 pages, 5221 KiB

Open AccessArticle

A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television

by Xinglong Liu, Yicheng Li, Yong Wu, Zhiyuan Wang, Wei He and Zhixiong Li

J. Mar. Sci. Eng. 2021, 9(11), 1199; https://doi.org/10.3390/jmse9111199 - 30 Oct 2021

Cited by 4 | Viewed by 1994

Abstract

Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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27 pages, 4384 KiB

Open AccessArticle

Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

by Junjun Li, Jingyu Yang, Bowei Xu, Yongsheng Yang, Furong Wen and Haitao Song

J. Mar. Sci. Eng. 2021, 9(10), 1080; https://doi.org/10.3390/jmse9101080 - 01 Oct 2021

Cited by 12 | Viewed by 2247

Abstract

Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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14 pages, 5013 KiB

Open AccessArticle

Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

by Yan Yan and Hongyan Xing

J. Mar. Sci. Eng. 2021, 9(6), 651; https://doi.org/10.3390/jmse9060651 - 12 Jun 2021

Cited by 7 | Viewed by 2031

Abstract

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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19 pages, 7157 KiB

Open AccessArticle

Restoration of Missing Patterns on Satellite Infrared Sea Surface Temperature Images Due to Cloud Coverage Using Deep Generative Inpainting Network

by Song-Hee Kang, Youngjin Choi and Jae Young Choi

J. Mar. Sci. Eng. 2021, 9(3), 310; https://doi.org/10.3390/jmse9030310 - 11 Mar 2021

Cited by 15 | Viewed by 2435

Abstract

In this paper, we propose a novel deep generative inpainting network (GIN) trained under the framework of generative adversarial learning, which is optimized for the restoration of cloud-disturbed satellite sea surface temperature (SST) imagery. The proposed GIN architecture can achieve accurate and fast restoration results. The proposed GIN consists of rough and fine reconstruction stages to promote the details and textures of missing (clouded) regions in SST images. We also propose a nov el preprocessing strategy that replaces the land areas with the average value of daily oceanic surface temperatures for improving restoration accuracy. To learn the proposed GIN, we developed a novel approach that combines multiple loss functions well suited for improving the restoration quality over missing SST information. Our results show that the difference in temperature between restored and actual satellite image data was no larger than 0.7 °C in monthly average values, which suggests excellent resilience against the missing sea surface temperature data. The proposed GIN has a faster restoration time and is feasible for real-time ocean-related applications. Furthermore, the computational cost of restoring SST images is much lower than the popular interpolation methods. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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16 pages, 3193 KiB

Open AccessArticle

Autonomous Underwater Vehicles and Field of View in Underwater Operations

by Isaac Segovia Ramírez, Pedro José Bernalte Sánchez, Mayorkinos Papaelias and Fausto Pedro García Márquez

J. Mar. Sci. Eng. 2021, 9(3), 277; https://doi.org/10.3390/jmse9030277 - 04 Mar 2021

Cited by 17 | Viewed by 4200

Abstract

Submarine inspections and surveys require underwater vehicles to operate in deep waters efficiently, safely and reliably. Autonomous Underwater Vehicles employing advanced navigation and control systems present several advantages. Robust control algorithms and novel improvements in positioning and navigation are needed to optimize underwater operations. This paper proposes a new general formulation of this problem together with a basic approach for the management of deep underwater operations. This approach considers the field of view and the operational requirements as a fundamental input in the development of the trajectory in the autonomous guidance system. The constraints and involved variables are also defined, providing more accurate modelling compared with traditional formulations of the positioning system. Different case studies are presented based on commercial underwater cameras/sonars, analysing the influence of the main variables in the measurement process to obtain optimal resolution results. The application of this approach in autonomous underwater operations ensures suitable data acquisition processes according to the payload installed onboard. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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