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

Artificial Intelligence-Aided Design for Unmanned Underwater Vehicles: A Multiple Activation Function Network-Based Hull Resistance Prediction 无人潜航器人工智能辅助设计：基于多激活函数网络的船体阻力预测

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-28 DOI: 10.1109/JOE.2025.3531926

Yu Ao;Huiling Duan;Shaofan Li

{"title":"Artificial Intelligence-Aided Design for Unmanned Underwater Vehicles: A Multiple Activation Function Network-Based Hull Resistance Prediction","authors":"Yu Ao;Huiling Duan;Shaofan Li","doi":"10.1109/JOE.2025.3531926","DOIUrl":"https://doi.org/10.1109/JOE.2025.3531926","url":null,"abstract":"Unmanned underwater vehicles (UUVs) require low-resistance hull designs to enhance their operational range and mission duration. However, the design process of UUV hulls is often multidisciplinary, sequential, and iterative, making it necessary to realize accurate and prompt resistance prediction. In order to address this challenge, this article presents a data-driven deep learning algorithm to provide a real-time prediction surrogate model for the hull resistance of UUVs. Specifically, we first collected UUV simulation data under different hull shapes with different hydrodynamic conditions. By introducing the multiple activation function network topology, we develop a deep learning algorithm that can predict resistance accurately in real-time while balancing training speed and prediction accuracy. We demonstrate that the developed deep learning algorithm can provide information about the UUV's performance by inputting hull shape and hydrodynamic condition without tedious meshing and calculation processes with an average error of less than 1.2% and a coefficient of determination of 0.9996. Finally, the UUV resistance prediction application scenarios of the constructed deep learning algorithm presented. We believe the algorithm construction and application process shown in this paper can make an invaluable contribution to artificial intelligence-aided design in underwater vehicles.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2050-2062"},"PeriodicalIF":3.8,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646464","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 Ship Radiated Noise Recognition Method Applicable to Incomplete Training Data Sets 一种适用于不完全训练数据集的船舶辐射噪声识别方法

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-23 DOI: 10.1109/JOE.2024.3519744

Shaoxiang Guo;Guolong Liang;Nan Zou;Longhao Qiu;Yu Hao;Yan Wang

{"title":"A Ship Radiated Noise Recognition Method Applicable to Incomplete Training Data Sets","authors":"Shaoxiang Guo;Guolong Liang;Nan Zou;Longhao Qiu;Yu Hao;Yan Wang","doi":"10.1109/JOE.2024.3519744","DOIUrl":"https://doi.org/10.1109/JOE.2024.3519744","url":null,"abstract":"To effectively classify ship radiated noise signals under incomplete training data sets, this article proposes a ship radiated noise recognition method. This method consists of a feature selection method based on the Bhattacharyya distance measurement (FS-BD) and the fuzzy support vector machine (SVM) based on fuzzy support vector center (FSVM-fsv). In the feature extraction stage, FS-BD removes redundant dimensions based on the similarity of feature probability distribution, aiming to improve the separability of features. However, the complex underwater acoustic environment leads to heterogeneity between similar samples, which further causes the incompleteness of the learning feature set. In the classifier design, under the framework of SVM, the FSVM-fsv method uses the distance between samples and their respective fuzzy support vector centers to optimize the decision hyperplane's spatial division capability, thereby reducing the impact of outliers and noise on classifier performance. The experimental results show that the FS-BD method can significantly improve the difference and robustness of the target features under the condition of about 900 samples of single-class targets and containing multiple environments. At the same time, the performance of FSVM-fsv under incomplete training sets is better than other machine learning methods, such as SVM, FSVM based on class center, FSVM based on estimated hyperplane, FSVM based on actual hyperplane, and FSVM based on support vector center (FSVM-sv), and the average recognition accuracy reaches 84.65%. This recognition method provides a reliable solution for target recognition in unknown environments.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1798-1811"},"PeriodicalIF":3.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646059","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 Study on Dynamic Characteristics and Vibration Mitigation Strategy of Jacket Offshore Wind Turbines Under Typical Normal Operating and Mechanical Fault Conditions 典型正常运行和机械故障工况下导管套海上风电机组动态特性及减振策略试验研究

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-23 DOI: 10.1109/JOE.2025.3536021

Dongzhe Lu;Wenhua Wang;Xin Li

{"title":"Experimental Study on Dynamic Characteristics and Vibration Mitigation Strategy of Jacket Offshore Wind Turbines Under Typical Normal Operating and Mechanical Fault Conditions","authors":"Dongzhe Lu;Wenhua Wang;Xin Li","doi":"10.1109/JOE.2025.3536021","DOIUrl":"https://doi.org/10.1109/JOE.2025.3536021","url":null,"abstract":"Based on a 1/75 jacket offshore wind turbine (OWT) fully coupled test model, fault and vibration-reduction dynamic model tests of a jacket OWT were conducted under typical wind and waves. The structural responses under grid loss and blade pitch faults were compared with those under normal operating conditions, and the coupled dynamic characteristics of jacket OWTs were analyzed under normal operating and mechanical fault conditions. Subsequently, a tuned-mass damper (TMD) model was designed and manufactured to evaluate the vibration-reduction effect of the TMD on the structural response of a jacket OWT under fault conditions. The results showed that the proposed fully coupled test design method for OWTs based on aerodynamic performance and hydrostructural elastic similarities can accurately simulate the normal operating and fault conditions of OWTs. Under fault conditions, a blade pitch-to-feather control strategy significantly reduces the structural response of an OWT; however, the load on the OWT requires further attention under blade pitch faults. Moreover, when the wind speed is not lower than the rated wind speed, the TMD exhibits a good vibration-reduction effect on the structural response of the jacket OWT under normal operating and fault conditions. Meanwhile, attention should be paid to the limitation of TMD frequency detuning when the wind speed is lower than the rated wind speed.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2165-2181"},"PeriodicalIF":3.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646463","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 Underwater Localization in Three-Dimensional Environment 三维环境下的实时水下定位

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-23 DOI: 10.1109/JOE.2025.3551008

Taketo Noda;Hayato Kondo;Koetsu Nagaokaya;Takuto Someya

{"title":"Real-Time Underwater Localization in Three-Dimensional Environment","authors":"Taketo Noda;Hayato Kondo;Koetsu Nagaokaya;Takuto Someya","doi":"10.1109/JOE.2025.3551008","DOIUrl":"https://doi.org/10.1109/JOE.2025.3551008","url":null,"abstract":"Localization is a critical component to enable autonomous visual inspection of underwater structures by using autonomous underwater vehicles (AUVs). Underwater structures, such as bridge piers, are often vertically nonuniform and have 3-D features, such as inclination and structural overhangs. This article proposes a real-time localization method using a particle filter with a novel 3-D observation model. The acoustic ray casting is introduced into the observation model to estimate multiple echoes of a single fan beam from varying geometries at different depths, so as to consider the vertical beamwidth of the mechanical scanning imaging sonar. The proposed method was implemented based on parallel computation using an embedded computer equipped with a graphics processing unit to enable real-time onboard navigation for the AUV. Photogrammetric survey missions were conducted in an area with large bridge structures of the Tokyo Wan Aqua-Line Expressway using an AUV. The effectiveness of the proposed method was validated by the successful demonstration of its ability to provide precise navigation and guidance in real time for very close distance visual inspection of underwater structures.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1585-1595"},"PeriodicalIF":3.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646559","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 Fully Coupled Electromagnetic-Hydrodynamic-Rigid Body Kinetics Model for Moored-Submerged Reconfigurable Ducted Turbine Arrays 系泊-水下可重构导管式水轮机阵列的电磁-水动力-刚体全耦合动力学模型

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-23 DOI: 10.1109/JOE.2025.3545219

Xin Shan;Onur Bilgen

{"title":"A Fully Coupled Electromagnetic-Hydrodynamic-Rigid Body Kinetics Model for Moored-Submerged Reconfigurable Ducted Turbine Arrays","authors":"Xin Shan;Onur Bilgen","doi":"10.1109/JOE.2025.3545219","DOIUrl":"https://doi.org/10.1109/JOE.2025.3545219","url":null,"abstract":"Hydrokinetic energy is largely untapped since the expense to install, operate, monitor, maintain, repair, and recover hydrokinetic turbines in aquatic environments leads to a high levelized cost of energy. Multidisciplinary design methods based on coupled aerodynamics/hydrodynamics, structural dynamics, power electronics, etc., are proposed to reduce the levelized cost of energy. This article presents a multiphysics coupled lumped-parameter model composed of the following: 1) a rigid body turbine array; 2) a mooring cable; 3) ducted turbine units; and 4) generators and electrical loads. The lumped-parameter multiphysics model is analytical, preserving the physical insights behind its development. Compared to high-fidelity (e.g., finite-element or distributed parameter) approaches, the lumped-parameter model is computationally low cost, which allows for applications of parametric investigations, design optimization, model-based real-time adjustment, control, etc. Among these applications, this work demonstrates 1) model-based real-time maximum power point tracking for varying flow velocity and 2) parametric investigations and design optimization for reducing the levelized cost of energy to show the utility of the proposed model.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2146-2164"},"PeriodicalIF":3.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646065","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

Impact of Natural Turbulent Waters on Quantum Key Distribution: Temperature and Salinity Considerations 自然湍流水对量子密钥分布的影响：温度和盐度的考虑

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-21 DOI: 10.1109/JOE.2025.3551076

Yalçın Ata;Kamran Kiasaleh

{"title":"Impact of Natural Turbulent Waters on Quantum Key Distribution: Temperature and Salinity Considerations","authors":"Yalçın Ata;Kamran Kiasaleh","doi":"10.1109/JOE.2025.3551076","DOIUrl":"https://doi.org/10.1109/JOE.2025.3551076","url":null,"abstract":"Quantum key distribution (QKD) has been identified as a viable solution for secure underwater communication. This study aims to characterize the impact of natural turbulent waters on systems. The performance of QKD systems is analyzed in terms of the quantum bit error rate and the secret key rate. We expand upon previous studies which only considered large separations by including large and small separations by comparing the turbulence length scale with the distance between two observation points. To that end, we obtain the analytical expression of the wave structure function (WSF) for small separation for propagating Gaussian beams. Since the WSF describes how the amplitude and phase of an optical wave change as it travels through the varying refractive index of the turbulent medium over a given distance, the accurate characterization remains essential in terms of revealing the beam wavefront distortion. Leveraging the practical parameters of underwater environment and communication systems, we demonstrate that the average temperature, average salinity concentration, temperature-salinity gradient, temperature and energy dissipation rates of turbulent water impact the performance and security of the QKD systems to a nonnegligible extent. It is also observed that the waters having high chlorophyll concentrations, e.g., coastal waters, drastically decrease the performance of the QKD systems due to strong absorption and scattering effects. The findings and insights gained from this study may help advance secure underwater communication, which in turn will aid in establishing future QKD networks. Potential applications of this research include secure underwater communication for defense, data gathering for marine environmental monitoring, remote data transmission, and deep-sea exploration.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"2381-2393"},"PeriodicalIF":3.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646060","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

Spotlighting on Objects: Prior Knowledge-Driven Maritime Image Dehazing and Object Detection Framework 目标聚焦：先验知识驱动的海洋图像去雾和目标检测框架

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-21 DOI: 10.1109/JOE.2025.3545289

Yaozong Mo;Chaofeng Li;Wenqi Ren;Wenwu Wang

{"title":"Spotlighting on Objects: Prior Knowledge-Driven Maritime Image Dehazing and Object Detection Framework","authors":"Yaozong Mo;Chaofeng Li;Wenqi Ren;Wenwu Wang","doi":"10.1109/JOE.2025.3545289","DOIUrl":"https://doi.org/10.1109/JOE.2025.3545289","url":null,"abstract":"Maritime environments often face visibility challenges due to haze which significantly impacts detection models. However, existing maritime object detection algorithms often neglect haze conditions or the unique characteristics of the maritime environment, resulting in decreased effectiveness in hazy weather. In this article, we propose a prior knowledge-driven maritime image dehazing and object detection framework (MDD), which consists of a detection network and a restoration network. Leveraging the characteristics of the highlighted ships in the inverted dark channel prior (IDCP), the detection network incorporates a prior subnetwork to learn ship-related features, which are subsequently merged into the backbone network through an IDCP cross-attention module. During training, the restoration network is integrated to improve the clarity of the features learned by the detection network. In addition, a ship-haze enrichment strategy is implemented to emphasize ship regions in the training samples, along with a ship-aware reconstruction loss to enhance the network's ability to learn dehazed features. Moreover, we establish a maritime object recognition with haze levels (MORHL) data set to evaluate object detector performance in maritime hazy conditions. It includes 13 280 annotated images across six categories: cargo ship, container ship, fishing boat, passenger ship, island, and buoy, with haze levels categorized as light, medium, and heavy. Comprehensive experiments on the MORHL and SMD data sets demonstrate that the proposed MDD framework outperforms the state-of-the-art detectors and various combinations of dehazing and detection methods.","PeriodicalId":13191,"journal":{"name":"IEEE Journal of Oceanic Engineering","volume":"50 3","pages":"1978-1992"},"PeriodicalIF":3.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646512","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

Editorial:The JOE Adopts a New Manuscript Submission System 社论：乔采用了一个新的稿件提交系统

IF 3.8 2区工程技术

IEEE Journal of Oceanic Engineering Pub Date : 2025-04-18 DOI: 10.1109/JOE.2025.3549318

Karl von Ellenrieder