Journal of Marine Science and Engineering

Research

23 pages, 9173 KiB

Open AccessArticle

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

by Chi-Hieu Ngo, Seok-Ju Lee, Changhyun Kim, Minh-Chau Dinh and Minwon Park

J. Mar. Sci. Eng. 2024, 12(2), 326; https://doi.org/10.3390/jmse12020326 - 13 Feb 2024

Cited by 2 | Viewed by 1629

In seaports, the automatic Grab-Type Ship Unloader (GTSU) stands out for its ability to automatically load and unload materials, offering the potential for substantial productivity improvement and cost reduction. Developing a fully automatic GTSU, however, presents a unique challenge: the system must autonomously [...] Read more.

In seaports, the automatic Grab-Type Ship Unloader (GTSU) stands out for its ability to automatically load and unload materials, offering the potential for substantial productivity improvement and cost reduction. Developing a fully automatic GTSU, however, presents a unique challenge: the system must autonomously determine the position of the cargo hold and the coordinates of the working point and identify potential hazards during material loading and unloading. This paper proposes AI models designed to detect cargo holds, extract working points, and support collision risk warnings, utilizing both the LiDAR sensor and the camera in the GTSU system. The model for cargo hold detection and collision warning was developed using image data of the cargo hold and grab, employing the You Only Look Once model. Concurrently, the model responsible for extracting the coordinates of working points for the GTSU system was designed by integrating the cargo hold detection and point cloud processing models. After testing the AI models for the lab-scale GTSU, the results show that the cargo hold detection and collision warning models achieve an accuracy of approximately 96% and 90%, respectively. Additionally, the working point coordinates extracted from the sensor system show a deviation of 5–10% compared to traditional measurements. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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25 pages, 9235 KiB

Open AccessArticle

Design of Experiments Technique Applied to Artificial Neural Network Models for FPSO Mooring System Analysis

by Ehsan Nikkhah, Antonio Carlos Fernandes and Jean-David Caprace

J. Mar. Sci. Eng. 2023, 11(11), 2194; https://doi.org/10.3390/jmse11112194 - 17 Nov 2023

Cited by 2 | Viewed by 1735

Abstract

Online monitoring of mooring system response for the FPSO platform in any operational condition is so far challenging for machine learning (ML). This paper presents a new dynamic NARX ANN model for time series of mooring tension and a static MLP model for [...] Read more.

Online monitoring of mooring system response for the FPSO platform in any operational condition is so far challenging for machine learning (ML). This paper presents a new dynamic NARX ANN model for time series of mooring tension and a static MLP model for the offset chart prediction of a taut-leg moored FPSO with different working scenarios. A novel method for supervised feature selection of the dataset was applied to determine the most influential design features. Additionally, a design of experiments (DOE) technique was implemented for test matrix creation, simulation, database generation, and supervised selection characteristics in ML. The DOE analysis revealed that the mooring configuration, platform loading condition, and environmental loads alter the platform’s six-degree-of-freedom motion response patterns. These input data were used to predict the mooring tension and the offset chart of the floater. The results include the fair values of statistical error for mooring tension (R² = 0.8–0.98, E ≈ 1.3–5.7%, RMSE ≈ 6–66 kN) and platform offset (E ≈ 0.1–1 m) prediction when testing the trained models with unseen data representing new operational conditions. Faster convergence can be achieved by adding non-numeric (string) input values to dataset numeric features. Supervised feature selection of the dataset is a step forward in ML to improve prediction accuracy. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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18 pages, 34314 KiB

Open AccessArticle

Enhanced Detection Method for Small and Occluded Targets in Large-Scene Synthetic Aperture Radar Images

by Hui Zhou, Peng Chen, Yingqiu Li and Bo Wang

J. Mar. Sci. Eng. 2023, 11(11), 2081; https://doi.org/10.3390/jmse11112081 - 30 Oct 2023

Viewed by 1250

Abstract

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship [...] Read more.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multiple scenes and multiple sizes, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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15 pages, 1870 KiB

Open AccessArticle

Graph-Based Anomaly Detection of Ship Movements Using CCTV Videos

by Nakhyeon Seong, Jeongseon Kim and Sungsu Lim

J. Mar. Sci. Eng. 2023, 11(10), 1956; https://doi.org/10.3390/jmse11101956 - 10 Oct 2023

Viewed by 1390

Abstract

This paper presents a novel machine learning-based approach for detecting abnormal ship movements using CCTV videos. Our method utilizes graph-based algorithms to analyze ship trajectories and identify anomalies, with a focus on enhancing maritime safety and accident prevention. Unlike conventional AIS data-dependent methods, [...] Read more.

This paper presents a novel machine learning-based approach for detecting abnormal ship movements using CCTV videos. Our method utilizes graph-based algorithms to analyze ship trajectories and identify anomalies, with a focus on enhancing maritime safety and accident prevention. Unlike conventional AIS data-dependent methods, our approach directly detects and visualizes abnormal movements from CCTV videos, particularly in narrow coastal areas. We evaluate the proposed method using real-world CCTV video data and demonstrate its effectiveness in detecting abnormal ship movements, offering promising results in real-world scenarios. The findings of this study have important implications to improve maritime safety and prevent accidents. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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18 pages, 4553 KiB

Open AccessArticle

A Machine-Learning Approach Based on Attention Mechanism for Significant Wave Height Forecasting

by Jiao Shi, Tianyun Su, Xinfang Li, Fuwei Wang, Jingjing Cui, Zhendong Liu and Jie Wang

J. Mar. Sci. Eng. 2023, 11(9), 1821; https://doi.org/10.3390/jmse11091821 - 19 Sep 2023

Cited by 5 | Viewed by 1948

Abstract

Significant wave height (SWH) is a key parameter for monitoring the state of waves. Accurate and long-term SWH forecasting is significant to maritime shipping and coastal engineering. This study proposes a transformer model based on an attention mechanism to achieve the forecasting of [...] Read more.

Significant wave height (SWH) is a key parameter for monitoring the state of waves. Accurate and long-term SWH forecasting is significant to maritime shipping and coastal engineering. This study proposes a transformer model based on an attention mechanism to achieve the forecasting of SWHs. The transformer model can capture the contextual information and dependencies between sequences and achieves continuous time series forecasting. Wave scale classification is carried out according to the forecasting results, and the results are compared with gated recurrent unit (GRU) and long short-term memory (LSTM) machine-learning models and the key laboratory of MArine Science and NUmerical Modeling (MASNUM) numerical wave model. The results show that the machine-learning models outperform the MASNUM within 72 h, with the transformer being the best model. For continuous 12 h, 24 h, 36 h, 48 h, 72 h, and 96 h forecasting, the average mean absolute errors (MAEs) of the test sets were, respectively, 0.139 m, 0.186 m, 0.223 m, 0.254 m, 0.302 m, and 0.329 m, and the wave scale classification accuracies were, respectively, 91.1%, 99.4%, 86%, 83.3%, 78.9%, and 77.5%. The experimental results validate that the transformer model can achieve continuous and accurate SWH forecasting, as well as accurate wave scale classification and early warning of waves, providing technical support for wave monitoring. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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24 pages, 5969 KiB

Open AccessArticle

3D-EddyNet: A Novel Approach for Identifying Three-Dimensional Morphological Features of Mesoscale Eddies in the Ocean

by Pufei Feng, Zhiyi Fu, Linshu Hu, Sensen Wu, Yuanyuan Wang and Feng Zhang

J. Mar. Sci. Eng. 2023, 11(9), 1779; https://doi.org/10.3390/jmse11091779 - 11 Sep 2023

Cited by 2 | Viewed by 1549

Abstract

Mesoscale eddies are characterized by swirling currents spanning from tens to hundreds of kilometers in diameter three-dimensional attributes holds paramount significance in driving advancements in both oceanographic research and engineering applications. Nonetheless, a notable absence of models capable of adeptly harnessing the scarcity [...] Read more.

Mesoscale eddies are characterized by swirling currents spanning from tens to hundreds of kilometers in diameter three-dimensional attributes holds paramount significance in driving advancements in both oceanographic research and engineering applications. Nonetheless, a notable absence of models capable of adeptly harnessing the scarcity of high-quality annotated marine data, to efficiently discern the three-dimensional morphological attributes of mesoscale eddies, is evident. To address this limitation, this paper constructs an innovative deep-learning-based model termed 3D-EddyNet, tailored for the precise identification and visualization of mesoscale eddies. In contrast to the prevailing 2D models that remain confined to surface-level data, 3D-EddyNet takes full advantage of three-dimensional convolutions to capture the essential characteristics of eddies. It is specifically tailored for recognizing spatial features within mesoscale eddies, including parameters like position, radius, and depth. The combination of dynamic convolutions and residual networks effectively enhances the model’s performance in a synergistic manner. The model employs the PReLU activation function to tackle gradient vanishing issues and improve convergence rates. It also addresses the challenge of foreground–background imbalance through cross-entropy functions. Additionally, to fine-tune the model’s effectiveness during the training phase, techniques such as random dropblock and batch normalization are skillfully incorporated. Furthermore, we created a training dataset using HYCOM data specifically from the South China Sea region. This dataset allowed for a comprehensive analysis of the spatial-temporal distribution and three-dimensional morphology of the eddies, serving as an assessment of the model’s practical effectiveness. The culmination of this analysis reveals an impressive 20% enhancement over 3D-UNet in detection accuracy, coupled with expedited convergence speed. Notably, the results obtained through our detection using empirical data align closely with those obtained by other scholars. The mesoscale eddies within this specific region unveil a discernible northeast-to-southwest distribution pattern, categorized into three principal morphological classifications: bowl-shaped, olive-shaped, and nearly cylindrical, with the bowl-shaped eddies prominently dominating. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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26 pages, 14293 KiB

Open AccessArticle

Real-Time Detection of Slug Flow in Subsea Pipelines by Embedding a Yolo Object Detection Algorithm into Jetson Nano

by Weiliang Qiao, Hongtongyang Guo, Enze Huang, Xin Su, Wenhua Li and Haiquan Chen

J. Mar. Sci. Eng. 2023, 11(9), 1658; https://doi.org/10.3390/jmse11091658 - 24 Aug 2023

Cited by 1 | Viewed by 2108

Abstract

In the multiple-phase pipelines in terms of the subsea oil and gas industry, the occurrence of slug flow would cause damage to the pipelines and related equipment. Therefore, it is very necessary to develop a real-time and high-precision slug flow identification technology. In [...] Read more.

In the multiple-phase pipelines in terms of the subsea oil and gas industry, the occurrence of slug flow would cause damage to the pipelines and related equipment. Therefore, it is very necessary to develop a real-time and high-precision slug flow identification technology. In this study, the Yolo object detection algorithm and embedded deployment are applied initially to slug flow identification. The annotated slug flow images are used to train seven models in Yolov5 and Yolov3. The high-precision detection of the gas slug and dense bubbles in the slug flow image in the vertical pipe is realized, and the issue that the gas slug cannot be fully detected due to being blocked by dense bubbles is solved. After model performance analysis, Yolov5n is verified to have the strongest comprehensive detection performance, during which, mAP_0.5 is 93.5%, mAP_0.5:0.95 is 65.1%, and comprehensive mAP (cmAP) is 67.94%; meanwhile, the volume of parameters and Flops are only 1,761,871 and 4.1 G. Then, the applicability of Yolov5n under different environmental conditions, such as different brightness and adding random obstructions, is analyzed. Finally, the trained Yolov5n is deployed to the Jetson Nano embedded device (NVIDIA, Santa Clara, CA, USA), and TensorRT is used to accelerate the inference process of the model. The inference speed of the slug flow image is about five times of the original, and the FPS has increased from 16.7 to 83.3. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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13 pages, 2833 KiB

Open AccessArticle

El Niño Index Prediction Based on Deep Learning with STL Decomposition

by Ningmeng Chen, Cheng Su, Sensen Wu and Yuanyuan Wang

J. Mar. Sci. Eng. 2023, 11(8), 1529; https://doi.org/10.3390/jmse11081529 - 31 Jul 2023

Cited by 1 | Viewed by 1870

Abstract

ENSO is an important climate phenomenon that often causes widespread climate anomalies and triggers various meteorological disasters. Accurately predicting the ENSO variation trend is of great significance for global ecosystems and socio-economic aspects. In scientific practice, researchers predominantly employ associated indices, such as [...] Read more.

ENSO is an important climate phenomenon that often causes widespread climate anomalies and triggers various meteorological disasters. Accurately predicting the ENSO variation trend is of great significance for global ecosystems and socio-economic aspects. In scientific practice, researchers predominantly employ associated indices, such as Niño 3.4, to quantitatively characterize the onset, intensity, duration, and type of ENSO events. In this study, we propose the STL-TCN model, which combines seasonal-trend decomposition using locally weighted scatterplot smoothing (LOESS) (STL) and temporal convolutional networks (TCN). This method uses STL to decompose the original time series into trend, seasonal, and residual components. Each subsequence is then individually predicted by different TCN models for multi-step forecasting, and the predictions from all models are combined to obtain the final result. During the verification period from 1992 to 2022, the STL-TCN model effectively captures index features and improves the accuracy of multi-step forecasting. In historical event simulation experiments, the model demonstrates advantages in capturing the trend and peak intensity of ENSO events. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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13 pages, 18314 KiB

Open AccessArticle

Multiscale Feature Fusion for Hyperspectral Marine Oil Spill Image Segmentation

by Guorong Chen, Jiaming Huang, Tingting Wen, Chongling Du, Yuting Lin and Yanbing Xiao

J. Mar. Sci. Eng. 2023, 11(7), 1265; https://doi.org/10.3390/jmse11071265 - 21 Jun 2023

Cited by 1 | Viewed by 1335

Abstract

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil [...] Read more.

Oil spills have always been a threat to the marine ecological environment; thus, it is important to identify and divide oil spill areas on the ocean surface into segments after an oil spill accident occurs to protect the marine ecological environment. However, oil spill area segmentation using ordinary optical images is greatly interfered with by the absorption of light by the deep sea and the distribution of algal organisms on the ocean surface, and it is difficult to improve segmentation accuracy. To address the above problems, a hyperspectral ocean oil spill image segmentation model with multiscale feature fusion (MFFHOSS-Net) is proposed. Specifically, the oil spill segmentation dataset was created using hyperspectral image data from NASA for the Gulf of Mexico oil spill, small-size images after the waveband filtering of the hyperspectral images were generated and the oil spill images were annotated. The model makes full use of having different layers with different characteristics by fusing feature maps of different scales. In addition, an attention mechanism was used to effectively fuse these features to improve the oil spill region segmentation accuracy. A case study, ablation experiments and model evaluation were also carried out in this work. Compared with other models, our proposed method achieved good results according to various evaluation metrics. Full article

(This article belongs to the Special Issue The Application of Artificial Intelligence and Machine Learning in a Marine Context)

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