IEEE Journal of Oceanic Engineering最新文献_第2页

Call for Papers: Special Issue on the IEEE UT2027 Symposium 论文征集：IEEE UT2027专题研讨会特刊

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-07-15 DOI: 10.1109/JOE.2026.3711922

引用次数: 0

Robust Pitch Control for Floating Offshore Wind Turbines Using LLF-Based Aerodynamic Modeling and Nonsingular Terminal Sliding Mode With Prescribed Performance 基于llf气动建模和规定性能的非奇异末端滑模的浮式海上风力发电机鲁棒螺距控制

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-03-18 DOI: 10.1109/JOE.2026.3679232

Hao Chen;Zijian Luo;Youming Cai;Jingang Han;Nadia Aït-Ahmed;Mohamed Benbouzid

{"title":"Robust Pitch Control for Floating Offshore Wind Turbines Using LLF-Based Aerodynamic Modeling and Nonsingular Terminal Sliding Mode With Prescribed Performance","authors":"Hao Chen;Zijian Luo;Youming Cai;Jingang Han;Nadia Aït-Ahmed;Mohamed Benbouzid","doi":"10.1109/JOE.2026.3679232","DOIUrl":"https://doi.org/10.1109/JOE.2026.3679232","url":null,"abstract":"Floating offshore wind turbines exhibit complex and highly nonlinear dynamics, making the development of accurate mathematical models particularly challenging. Under substantial external disturbances, conventional control strategies often fall short in delivering fast and precise responses. To address the difficulties associated with modeling aerodynamic characteristics, this study employs extensive simulation data from the standalone AeroDyn module to construct a representative aerodynamic response surface. A locally linear fitting technique is adopted to approximate this surface, resulting in an accurate aerodynamic model. To further mitigate the influence of modeling errors and improve system robustness, a novel pitch control scheme is developed by integrating prescribed performance control (PPC) with nonsingular terminal sliding mode (NTSM) control, referred to as the NTSM-PPC controller. This approach not only enhances tracking accuracy but also circumvents the singularity problems inherent in traditional sliding mode controllers. The effectiveness of the proposed method is validated through cosimulations on MATLAB/Simulink and OpenFAST platforms. Simulation results confirm that the NTSM-PPC controller significantly enhances output power stability and suppresses fluctuations. Specifically, the controller’s root mean square value of 5.00001236 MW is closest to the rated power, with a power variation coefficient of only 0.01%, outperforming the gain scheduled proportional integral (GSPI) (0.57%) and other controllers such as <inline-formula><tex-math>${{bm{C}}_{bm{p}}^{rm{exp}}}$</tex-math></inline-formula>-SMC, <inline-formula><tex-math>${{{hat{bm{C}}}}_{bm{p}}}$</tex-math></inline-formula>-SMC, and <inline-formula><tex-math>${{{hat{bm{C}}}}_{bm{p}}}$</tex-math></inline-formula>-PPC-SMC.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2008-2023"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451839","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 Two-Stage Underwater Image Enhancement Network With Cross-Domain Multiple Priors 一种跨域多先验的两级水下图像增强网络

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-03 DOI: 10.1109/JOE.2026.3683796

Zijian Liu;Xingmei Wang;Tianzi Zhang

{"title":"A Two-Stage Underwater Image Enhancement Network With Cross-Domain Multiple Priors","authors":"Zijian Liu;Xingmei Wang;Tianzi Zhang","doi":"10.1109/JOE.2026.3683796","DOIUrl":"https://doi.org/10.1109/JOE.2026.3683796","url":null,"abstract":"Underwater images in marine environments often suffer from severe visual degradation due to light absorption and scattering. Existing deep learning-based underwater image enhancement (UIE) methods typically rely on limited underwater data sets to learn nonlinear mappings, which constrains their ability to generalize across diverse degradation patterns. Empirically, we find that structural boundaries and texture features from natural images can serve as informative priors for UIE. Motivated by this, we propose a novel two-stage UIE network with cross-domain multiple priors (CMUIE) to improve underwater image quality. First, to address multisource degradation, we design a pretraining-finetuning pipeline that transfers generic enhancement knowledge via blind degradation modeling. Second, to preserve semantic structures, we construct a structure-aware denoising module guided by semantic priors, directing the network's focus to salient regions. Finally, to restore high-frequency textures, we develop a one-step diffusion reconstruction module that leverages a diffusion prior to enhance fine-grained details. Extensive experiments show that CMUIE achieves state-of-the-art performance in both image denoising and super-resolution tasks, exhibiting superior generalization capabilities and practical applicability.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2495-2515"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451864","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

Underwater Blast Noise Measurements in Very Shallow Water 浅水水下爆炸噪声测量

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-18 DOI: 10.1109/JOE.2026.3693581

Jorge E. Quijano

{"title":"Underwater Blast Noise Measurements in Very Shallow Water","authors":"Jorge E. Quijano","doi":"10.1109/JOE.2026.3693581","DOIUrl":"https://doi.org/10.1109/JOE.2026.3693581","url":null,"abstract":"This article presents measurements and a model-based analysis of the acoustic noise generated by underwater charges (of weights 0.78–13.28 kg TNT equivalent) detonated in very shallow water (≤4-m depth) within a bay with a limited line-of-sight acoustic path toward open and deeper waters. Four bottom-mounted and one mid-water acoustic recorders at ranges 70–1000 m (water depths 4–60 m) were used to measure blast signatures of 48 detonations. Measurements at close range allowed for characterization of the frequency-dependent performance of a bubble curtain mitigation system, and verification of the agreement between the measured data and a theoretical model of an underwater explosion. Measurements at longer ranges provided empirical curves of the peak (PK) level and sound exposure level (SEL) as a function of range, which are more representative for acoustically enclosed locations than current models for confined detonations found in the literature. The measurements indicate strong attenuation outside the bay of the high-frequency oscillations characteristic of the blast shockwave, likely due to the effect of bathymetry features. Modeling of blast noise using the parabolic equation method successfully represented the PK and SEL characteristics of the blasts, but also highlighted the need to refine the seabed geoacoustic model and the bathymetry of the area.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2421-2429"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451888","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

Experimental Model-Free Trim Control of a Planing Hull With Interceptor 带截流器的平面船体纵倾控制实验

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-03 DOI: 10.1109/JOE.2026.3683607

Caner Sancak;Omer Sinan Sahin

{"title":"Experimental Model-Free Trim Control of a Planing Hull With Interceptor","authors":"Caner Sancak;Omer Sinan Sahin","doi":"10.1109/JOE.2026.3683607","DOIUrl":"https://doi.org/10.1109/JOE.2026.3683607","url":null,"abstract":"This study presents the design, development, and real-sea validation of an automatically controlled interceptor system for dynamic trim control in planing hulls. The interceptor mechanism is integrated with an ultra-local-model-based, real-time model-free control strategy, enabling trim-angle regulation without an explicit hydrodynamic plant model. By continuously compensating for nonlinearities and modeling uncertainties through online estimation, the proposed approach is well suited to practical marine operation. First, the system was installed on a planing boat to investigate the influence of interceptor plate height on trim dynamics; then, the control system was evaluated under Beaufort 0 and Beaufort 1 conditions for trim-angle regulation. The experimental control results demonstrate effective trim setpoint regulation across interceptor plate heights, while maintaining mean trim behavior around the desired reference despite wave-induced disturbances. Performance improvements were observed in cruise operation relative to a baseline case with interceptor control disabled, including attainable cruising speed when operating near-favorable trim settings. A comparative study with a conventional proportional–integral controller is also reported, along with additional trials using different model-free controller parameters, to highlight differences in tracking behavior and parameter sensitivity. The presented results support the feasibility of model-free interceptor-based trim regulation in real-sea experiments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1965-1976"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451897","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

Real-Time Path Planning for Multiple Autonomous Underwater Vehicles in Dense Obstacle Fields: The DA-IFDS Approach 多自主水下航行器在密集障碍物场中的实时路径规划：DA-IFDS方法

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-04-24 DOI: 10.1109/JOE.2026.3674024

Xizhe Chen;Shihong Yin;Yujing Li;Zhengrong Xiang

{"title":"Real-Time Path Planning for Multiple Autonomous Underwater Vehicles in Dense Obstacle Fields: The DA-IFDS Approach","authors":"Xizhe Chen;Shihong Yin;Yujing Li;Zhengrong Xiang","doi":"10.1109/JOE.2026.3674024","DOIUrl":"https://doi.org/10.1109/JOE.2026.3674024","url":null,"abstract":"Multiple autonomous underwater vehicles (AUVs) face dense static and dynamic obstacle threats during path planning in complex and dynamic subsea environments. Therefore, each AUV must achieve safe, reliable, and real-time path planning under limited local observations. This article proposes a 3-D path planning method for multiple AUVs, named the dynamically adaptive-interfered fluid dynamical system (DA-IFDS) algorithm. It employs a two-layer real-time decision-making framework: the high-level decision-making module outputs step-by-step parameters based on proximal policy optimization (PPO), while the low-level module determines the velocity direction of each step according to these parameters. Such a hierarchical design improves the learning efficiency and adaptability of multiple AUVs. This algorithm models obstacles as disturbances in the flow field and incorporates these parameters into the action space. It employs bidirectional gated recurrent units to process variable-length observation sequences, effectively addressing the observation inconsistency faced by traditional methods in dynamic obstacle scenarios. In addition, it designs a diversified reward function. Simulation results demonstrate that the proposed DA-IFDS algorithm outperforms soft actor-critic-IFDS, deep deterministic policy gradient-IFDS, and conventional PPO algorithms in terms of success rate, obstacle-avoidance performance, and planning efficiency, validating its effectiveness and superiority in real-time path planning for multiple AUVs in dense static and dynamic obstacle scenarios.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1787-1803"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451900","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

Structural Health Monitoring for Floating Offshore Wind Turbines Based on the Hilbert–Huang Transform and Random Forest Classification 基于Hilbert-Huang变换和随机森林分类的海上浮式风力机结构健康监测

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-08 DOI: 10.1109/JOE.2026.3683216

Yinde Qiao;Zefan Lin;Chenziyu Sun;Xizheng Zhao;Jinbin Liang

{"title":"Structural Health Monitoring for Floating Offshore Wind Turbines Based on the Hilbert–Huang Transform and Random Forest Classification","authors":"Yinde Qiao;Zefan Lin;Chenziyu Sun;Xizheng Zhao;Jinbin Liang","doi":"10.1109/JOE.2026.3683216","DOIUrl":"https://doi.org/10.1109/JOE.2026.3683216","url":null,"abstract":"Floating offshore wind turbines (FOWTs) work in the deep sea, where severe environmental conditions threaten their structural safety. Structural health monitoring (SHM) is crucial for the safe operation of FOWTs. Currently, the SHM of FOWTs faces the problem of a lack of engineering data and effective methods. In this study, structural damage data are generated by fully coupled simulations of a FOWT to simulate blade damage and mooring line breakage. To handle nonlinear and oscillating FOWT blade acceleration and platform pitch motion, the Hilbert–Huang transform–based method is used to extract time-varying frequency domain features. Multiple random forest (RF) models are trained and tested for different types of SHM, i.e., monitoring for blade damage and mooring line breakage. The results indicate that the RF model to identify mooring line breakage by platform pitch angle presented the best accuracy. The pitch angle signal is better than the blade acceleration data for recognizing damage from the platform. The results indicate that the established method is effective for the SHM of FOWTs.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"1993-2007"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451901","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 Passive Underwater Acoustic Target Recognition Method Using DiMobNet and ROI 基于DiMobNet和ROI的被动水声目标识别方法

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-05 DOI: 10.1109/JOE.2026.3687787

Hassan Akbarian;Mohammad Hossein Sedaaghi

{"title":"A Passive Underwater Acoustic Target Recognition Method Using DiMobNet and ROI","authors":"Hassan Akbarian;Mohammad Hossein Sedaaghi","doi":"10.1109/JOE.2026.3687787","DOIUrl":"https://doi.org/10.1109/JOE.2026.3687787","url":null,"abstract":"Underwater acoustic target recognition (UATR) has emerged as an essential technique for monitoring the marine environment and classifying vessels. Although increasing the number of neural network layers or neurons can improve classification accuracy under certain conditions, it often leads to higher computational complexity and potential overfitting in real-world applications. The underwater acoustic background frequently obscures ship-radiated noise, requiring advanced deep learning approaches capable of extracting salient and informative acoustic features. To address this challenge, this article proposes a novel network, DiMobNet, which employs 1-D and 2-D dilated convolution operations to extract meaningful features from regions-of-interest (ROI) in spectrogram images. The network adopts a multibranch architecture that integrates dilated convolutions to effectively capture the underlying characteristics of ship-radiated noise in time–frequency representations. The proposed method combines multiple preprocessing stages and dilated convolutional blocks with a global weighting mechanism derived from time–frequency features to enhance discriminative feature selection and classification performance. Experimental results demonstrate that DiMobNet achieves an overall accuracy of 98.86% on the ShipsEar database. Additionally, the proposed ROI extraction approach was validated under varying signal-to-noise ratio conditions (−10 to +10 dB), demonstrating stable detection performance even in high-noise environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2329-2344"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451970","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

Robust Multiping Forward-Looking Sonar Coherent Detection 鲁棒多频前视声纳相干探测

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-16 DOI: 10.1109/JOE.2026.3693862

Olivier Lerda;Guillaume Ginolhac;Ammar Mian;Jean-Philippe Ovarlez;Guillaume Matte;Didier Charlot

{"title":"Robust Multiping Forward-Looking Sonar Coherent Detection","authors":"Olivier Lerda;Guillaume Ginolhac;Ammar Mian;Jean-Philippe Ovarlez;Guillaume Matte;Didier Charlot","doi":"10.1109/JOE.2026.3693862","DOIUrl":"https://doi.org/10.1109/JOE.2026.3693862","url":null,"abstract":"We tackle the problem of adaptive multiping detection of quasi-static point-like targets by moving forward-looking sonar based on the principles of coherent imaging. Data are first repositioned using an amplitude and phase coregistration procedure, enabling correct compensation of sonar displacements induced by the platform's movements. The result is a data stack similar to the space time adaptive processing case, in which the target observations are brought back into coherence, and the covariance matrix of the clutter naturally has an underlying structure. We then show that a rank-1 multiping detector incorporating a structured estimator of the covariance matrix performs significantly better than a conventional single-ping detector. However, while rank-1 multiping detection works well for ideally phase-aligned signals, this is not always the case in practical applications. Subspace detection, particularized to phase uncertainties, then leads to a new detection test robust to the target phase shift and whose performance still outperforms conventional approaches. Numerous simulations and analyses of experimental sonar data are discussed and illustrated.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2258-2273"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452039","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

Physics-Informed Ensemble Learning for Detection of Malfunctioning Ocean Bottom Pressure Gauges: A Case Study on the S-Net Network 基于物理信息的集成学习在海底压力表故障检测中的应用——以S-Net网络为例

IF 5.6 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2026-07-01 Epub Date: 2026-06-03 DOI: 10.1109/JOE.2026.3686725

Oleg V. Ponomarev;Nikita V. Belyakov;Sergey V. Kolesov;Andrei M. Volgarev

{"title":"Physics-Informed Ensemble Learning for Detection of Malfunctioning Ocean Bottom Pressure Gauges: A Case Study on the S-Net Network","authors":"Oleg V. Ponomarev;Nikita V. Belyakov;Sergey V. Kolesov;Andrei M. Volgarev","doi":"10.1109/JOE.2026.3686725","DOIUrl":"https://doi.org/10.1109/JOE.2026.3686725","url":null,"abstract":"In this study, we analyze bottom pressure time series from 139 operational stations of the S-net network to detect potential anomalies. As of 12 March 2025, 15 stations were identified as malfunctioning: S1N09, S1N13, S1N21, S2N06, S2N18, S2N20, S2N25, S3N02, S3N04, S3N22, S4N10, S4N24, S5N10, S5N12, and S5N16. Notably, the failure of station S2N25 was captured in real time during observations. The analysis revealed that increased depth is statistically associated with a higher risk of sensor malfunction (<inline-formula><tex-math>$p < 0.05$</tex-math></inline-formula>). To enable automatic identification of malfunctioning pressure gauges, we introduce an ensemble learning model that combines three complementary reconstruction techniques—Seasonal Auto Regressive Integrated Moving Average forecasting, physics-based tidal prediction using the TPXO model, and interstation similarity analysis—evaluated with two error metrics (mean squared error and DTW). Their outputs are integrated by a lightweight random-forest meta-classifier, yielding an interpretable and data-efficient hybrid model. The model aggregates prior knowledge of the physical characteristics of the signal with machine learning to classify stations as either operational or malfunctioning. The average MCC-score on the test set reached 0.90 with a standard deviation of 0.06. Because the proposed model is fully interpretable and computationally inexpensive, it is suitable for real-time deployment within existing tsunami warning infrastructures.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"51 3","pages":"2147-2158"},"PeriodicalIF":5.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452040","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