IEEE Journal of Oceanic Engineering最新文献_第8页

Multilabel Recognition Method for Ship-Radiated Noise Signals Based on Multidomain Information Fusion With Deep Equilibrium Models 基于深度平衡模型的多域信息融合舰船辐射噪声信号多标签识别方法

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-08 DOI: 10.1109/JOE.2025.3545239

Yichen Duan;Xiaohong Shen;Haiyan Wang;Yongsheng Yan

{"title":"Multilabel Recognition Method for Ship-Radiated Noise Signals Based on Multidomain Information Fusion With Deep Equilibrium Models","authors":"Yichen Duan;Xiaohong Shen;Haiyan Wang;Yongsheng Yan","doi":"10.1109/JOE.2025.3545239","DOIUrl":"https://doi.org/10.1109/JOE.2025.3545239","url":null,"abstract":"The recognition of ship-radiated noise signals is currently the crucial means of perceiving ships. Numerous methods for the recognition of ship-radiated noise signals have been developed based on deep learning techniques. Prior studies on ship-radiated noise signal recognition have assumed a single-target scenario. In this article, we establish a multilabel recognition scenario for ship-radiated noise signals. We propose a multilabel recognition framework for ship-radiated noise signals with multidomain information fusion. Initially, we adopt two fundamental backbone network structures to extract preliminary features from both time-domain and time–frequency domain signal data. Subsequently, we construct a feature enhancement and fusion module based on the theory of deep balanced networks. This module enables information interaction from low-level to high-level between the time-domain and time-frequency domain information of ship-radiated noise signals. We introduce a transformer-based feature enhancement approach and a gated fusion feature update network structure. We also design a fusion strategy for secondary fusion updates and interdomain representations to obtain stable enhanced fusion feature representations. Finally, a linear classifier is employed to determine the categories of the mixed signals. We simulate multilabel data for ship-radiated noise signals using the publicly available Deepship data set. Experimental results demonstrate satisfactory recognition performance.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1760-1771"},"PeriodicalIF":3.8,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MLMFFNet: Multilevel Mixed Feature Fusion Network for Real-Time Forward-Looking Sonar Image Segmentation MLMFFNet：用于实时前视声纳图像分割的多级混合特征融合网络

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-06 DOI: 10.1109/JOE.2025.3529132

Haotian Zheng;Yushan Sun;Hao Xu;Liwen Zhang;Yatong Han;Shuguang Cui;Zhen Li

{"title":"MLMFFNet: Multilevel Mixed Feature Fusion Network for Real-Time Forward-Looking Sonar Image Segmentation","authors":"Haotian Zheng;Yushan Sun;Hao Xu;Liwen Zhang;Yatong Han;Shuguang Cui;Zhen Li","doi":"10.1109/JOE.2025.3529132","DOIUrl":"https://doi.org/10.1109/JOE.2025.3529132","url":null,"abstract":"Forward-looking sonar is a critical tool for underwater target detection, and segmentation is an essential component of forward-looking sonar image processing. Accurate segmentation of sonar images is vital, but the complexity of the underwater environment introduces challenges, such as low resolution, significant noise, and blurred target edges. These factors make real-time, precise segmentation particularly difficult. To address these challenges, we propose a novel real-time segmentation network, the multilevel mixed feature fusion network (MLMFFNet), specifically designed for forward-looking sonar images. Our approach leverages a unique MFF module and a multiscale MFF module to extract both local and contextual information using deep convolutional networks, dilated convolutions, and partial convolution combinations for effective information integration. Additionally, we incorporate a context connection module to enhance feature fusion by utilizing high-level contextual information. To further improve accuracy, we introduce three weighted loss functions designed to address imbalanced sample distributions and blurred boundaries. Experimental evaluations on two distinct forward-looking sonar data sets demonstrate that MLMFFNet significantly outperforms many state-of-the-art general and sonar-specific semantic segmentation networks, delivering superior segmentation accuracy while maintaining real-time performance.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1356-1369"},"PeriodicalIF":3.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot Underwater Acoustic Target Recognition Using Domain Adaptation and Knowledge Distillation 基于领域自适应和知识精馏的水声小目标识别

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-06 DOI: 10.1109/JOE.2025.3532036

Xiaodong Cui;Zhuofan He;Yangtao Xue;Peican Zhu;Jing Han;Xuelong Li

{"title":"Few-Shot Underwater Acoustic Target Recognition Using Domain Adaptation and Knowledge Distillation","authors":"Xiaodong Cui;Zhuofan He;Yangtao Xue;Peican Zhu;Jing Han;Xuelong Li","doi":"10.1109/JOE.2025.3532036","DOIUrl":"https://doi.org/10.1109/JOE.2025.3532036","url":null,"abstract":"The complex dynamics of the marine environment pose substantial challenges for underwater acoustic target recognition (UATR) systems, especially when there are limited training samples. However, existing image-based few-shot learning methods might not be applicable, mainly because they fail to capture the temporal and spectral features from acoustic targets and lack the competent domain adaptation ability due to the inefficient usage of base samples. In this article, we develop a novel Domain Adaptation-based Attentional Time–Frequency few-shot recognition method (DAATF) for underwater acoustic targets. The DAATF explicitly utilizes a self-attention-based feature extractor to capture the time–frequency structural dependencies and constructs an autoencoder-based domain adapter to improve the cross-domain knowledge transfer through reusing the base dataset. In addition, a knowledge distillation module is designed to enable the model to reserve the general feature extraction ability of the pretrained network to avoid overfitting. Extensive experiments are conducted to assess prediction accuracy, noise robustness, and cross-domain adaptation. The obtained results validate that the DAATF can achieve outstanding performance, demonstrating its great potential for practical UATR applications. Furthermore, we provide free and open access to the DanShip data set.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"637-653"},"PeriodicalIF":3.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ROV Localization Using Ballasted Umbilical Equipped With IMUs 利用装有imu的压载脐带缆进行ROV定位

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-05 DOI: 10.1109/JOE.2024.3467448

Juliette Drupt;Christophe Viel;Claire Dune;Vincent Hugel

引用次数: 0

The Adaptive Sampling of Marine Robots in Ocean Observation: An Overview 海洋观测中海洋机器人的自适应采样研究综述

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-04 DOI: 10.1109/JOE.2025.3529087

Xiaojuan Ma;Yanhui Wang

引用次数: 0

Chaotic Initialization Particle Filter AUV Cluster Position Calibration Algorithm Based on Intragroup Distance Measurement Under Large Initial Position Error 大初始位置误差下基于群内距离测量的混沌初始化粒子滤波AUV聚类位置标定算法

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-03 DOI: 10.1109/JOE.2024.3516096

Qingyu Zhang;Jin Fu;Nan Zou;Bin Qi;Yuan Hang Fan

{"title":"Chaotic Initialization Particle Filter AUV Cluster Position Calibration Algorithm Based on Intragroup Distance Measurement Under Large Initial Position Error","authors":"Qingyu Zhang;Jin Fu;Nan Zou;Bin Qi;Yuan Hang Fan","doi":"10.1109/JOE.2024.3516096","DOIUrl":"https://doi.org/10.1109/JOE.2024.3516096","url":null,"abstract":"With the increasing diversity and complexity of maritime mission requirements, the technology of collaborative multiautonomous underwater vehicles (AUVs) has garnered widespread attention. In this domain, the positional calibration technology of AUV clusters is an integral aspect that cannot be overlooked. Traditional leader–follower AUV cluster positional calibration models and algorithms have utilized information from either a single leader AUV or multiple leader AUVs in conjunction with a single follower AUV. However, with the expansion of the scale of follower AUVs, the availability of follower–follower AUV information increases. Consequently, this article develops a novel AUV cluster positional calibration model that leverages both the distance information between leader and follower AUVs, and the follower–follower AUV distance information. The observability of this model is analyzed, and building upon this, a chaos-initialized particle filter algorithm for AUV cluster positional calibration is proposed. Finally, experiments are conducted to compare the performance of the algorithm presented in this article with the particle filtering algorithm under different initial error conditions. The results demonstrate that the proposed algorithm exhibits stable convergence speed and calibration error at low initial errors. At high initial errors, it achieves faster convergence, lower calibration error within a finite time, and enhanced stability.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1140-1152"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Floating Offshore Wind Turbine Optimized Control for Power Regulation With Experimental Validation 浮式海上风电机组功率调节优化控制与实验验证

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-03 DOI: 10.1109/JOE.2024.3520365

Seydali Ferahtia;Azeddine Houari;Mohamed Machmoum;Mohammad Rasool Mojallizadeh;Mourad Ait-Ahmed;Félicien Bonnefoy

{"title":"Floating Offshore Wind Turbine Optimized Control for Power Regulation With Experimental Validation","authors":"Seydali Ferahtia;Azeddine Houari;Mohamed Machmoum;Mohammad Rasool Mojallizadeh;Mourad Ait-Ahmed;Félicien Bonnefoy","doi":"10.1109/JOE.2024.3520365","DOIUrl":"https://doi.org/10.1109/JOE.2024.3520365","url":null,"abstract":"This article proposes a new strategy for blade pitch control to regulate power production while alleviating the negative effects of the structural motions of floating offshore wind turbines (FOWTs). FOWTs frequently experience significant fluctuations in rotor speed when wind speed is above its rated value in the presence of significant wave heights. This condition reduces the power quality while amplifying the fatigue loads, which can result in damage to the generator. To address this problem, designers frequently use simplified models to design controllers, such as the gain-scheduled proportional integral (GSPI) controller. These models can demonstrate the nonlinear coupling of the platform motions and the rotor speed. However, their performance is limited due to the chosen linearization points. This article proposes an optimal design method based on metaheuristic algorithms. These algorithms treat the system as a black box, allowing for control parameter tuning considering all degrees of freedom, such as those provided by OpenFAST. The Red Tailed Hawk (RTH) Algorithm is used to create an optimized GSPI controller (RTH-GSPI) that maintains power while minimizing platform motion. Consequently, the performance is significantly enhanced. Numerical simulations using co-simulation between MATLAB and OpenFAST, along with experimental validation using an FOWT prototype, have verified the suggested technique's efficiency.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1231-1243"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Full Magnetometer and Gyroscope Bias Estimation Using Angular Rates: Theory and Experimental Evaluation of a Factor Graph-Based Approach 使用角速率的全磁力计和陀螺仪偏差估计：基于因子图的方法的理论和实验评估

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-03 DOI: 10.1109/JOE.2024.3523701

Sebastián Rodríguez-Martínez;Giancarlo Troni

{"title":"Full Magnetometer and Gyroscope Bias Estimation Using Angular Rates: Theory and Experimental Evaluation of a Factor Graph-Based Approach","authors":"Sebastián Rodríguez-Martínez;Giancarlo Troni","doi":"10.1109/JOE.2024.3523701","DOIUrl":"https://doi.org/10.1109/JOE.2024.3523701","url":null,"abstract":"Despite their widespread use in determining system attitude, micro-electro-mechanical systems attitude and heading reference system are limited by sensor measurement biases. This article introduces a method called Magnetometer and Gyroscope Calibration (MAGYC), leveraging three-axis angular rate measurements from an angular rate gyroscope to estimate both the hard- and soft-iron biases of magnetometers as well as the bias of gyroscopes. We present two implementation methods of this approach based on batch and online incremental factor graphs. Our method imposes fewer restrictions on instrument movements required for calibration, eliminates the need for knowledge of the local magnetic field magnitude or instrument's attitude, and facilitates integration into factor graph algorithms for smoothing and mapping frameworks. We validate the proposed methods through numerical simulations and in-field experimental evaluations with a sensor onboard an underwater vehicle. By implementing the proposed method in field data of a seafloor mapping dive, the dead-reckoned-based position estimation error of the underwater vehicle was reduced from 10% to 0.5% of the distance traveled.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1606-1615"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908715","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HG2former: HSV-Gamma Guided Transformers for Efficient Underwater Image Enhancement HG2former：用于高效水下图像增强的HSV-Gamma引导变压器

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-03-03 DOI: 10.1109/JOE.2024.3525150

Yuhao Qing;Liquan Shen;Zhijun Fang;Yueying Wang

{"title":"HG2former: HSV-Gamma Guided Transformers for Efficient Underwater Image Enhancement","authors":"Yuhao Qing;Liquan Shen;Zhijun Fang;Yueying Wang","doi":"10.1109/JOE.2024.3525150","DOIUrl":"https://doi.org/10.1109/JOE.2024.3525150","url":null,"abstract":"Due to optical phenomena, such as the absorption and scattering of light in underwater environments, underwater images often suffer from degradation in color, contrast, clarity, and noise. Existing deep learning-based methods for underwater image enhancement typically learn a direct mapping from low-quality to high-quality underwater images, without fully considering the mapping of local luminance, chrominance, and contrast features. In this article, we propose a transformer model guided hue, saturation, value (HSV) and gamma correction for underwater image enhancement. The HG2former combines the HSV color model and gamma correction techniques to isolate the three fundamental characteristics of color, providing rich, differentiated enhancement for both color and contrast in underwater images. In addition, nonlinear gamma correction adaptively adjusts the brightness and contrast of images, addressing issues of visibility reduction and color distortion in underwater imaging. Furthermore, we introduce a meticulously designed encoder–decoder structure, along with an improved multihead self-attention module, to capture the spatial distribution patterns of underwater images while modeling both local and long-range dependencies. Extensive experimental results on multiple data sets demonstrate that the proposed HG2former outperforms other state-of-the-art methods.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"866-878"},"PeriodicalIF":3.8,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Velocity Form Model Predictive Control of an Autonomous Underwater Vehicle 自主水下航行器速度形态模型预测控制

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-02-26 DOI: 10.1109/JOE.2024.3519680

Isah A. Jimoh;Hong Yue

{"title":"A Velocity Form Model Predictive Control of an Autonomous Underwater Vehicle","authors":"Isah A. Jimoh;Hong Yue","doi":"10.1109/JOE.2024.3519680","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519680","url":null,"abstract":"This article presents a model-predictive control (MPC) scheme to achieve 3-D trajectory tracking control and point stabilization of an autonomous underwater vehicle (AUV) subject to environmental disturbances. The AUV is modeled as a coupled nonlinear system. The control scheme is developed using a linear parameter-varying formulation of the nonlinear model in velocity form to obtain an optimization control problem with efficient online solvers and does not require model augmentation that can potentially increase computational efforts. The control strategy inherently provides offset-free control when tracking piecewise constant reference signals, ensures feasibility for trajectories containing unreachable points, and is relatively simple to implement, as parameterization of all equilibria is not required. A simple switching law is proposed for task switching between the 3-D trajectory tracking and point stabilization. The MPC is designed to ensure the closed-loop stability of the vehicle in both motion control tasks via the imposition of terminal constraints. Through simulations of the coupled nonlinear Naminow-D AUV under ocean current and wave disturbances, the effectiveness of the control strategy in trajectory tracking and point stabilization is demonstrated.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 2","pages":"1127-1139"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0