Ocean Engineering最新文献_第2页

Effect of drag-reducing additives on particle collection performance in hydraulic collectors 减阻添加剂对液压集尘器颗粒收集性能的影响

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.123061

Xian Zhang , Qingqing Lu , Xuguang Chen , Chaoqun Liu , Shenpeng Tian , Xixi Liu

{"title":"Effect of drag-reducing additives on particle collection performance in hydraulic collectors","authors":"Xian Zhang , Qingqing Lu , Xuguang Chen , Chaoqun Liu , Shenpeng Tian , Xixi Liu","doi":"10.1016/j.oceaneng.2025.123061","DOIUrl":"10.1016/j.oceaneng.2025.123061","url":null,"abstract":"<div><div>In hydraulic collectors, the properties of the jet fluid play a crucial role in both operational efficiency and nodule collection performance. To explore the relationship between fluid characteristics and the transport behavior of nodule particles, this study introduces a surfactant as a drag-reducing additives (DRA) into the jet fluid and investigates its effects on particle detachment and collection performance. The results show that under the same jet conditions, adding a DRA significantly reduces nodule collection time, and a DRA concentration of 1000 mg/L is a suitable choice based on overall considerations. Under the coupled effect of jet velocity and travel velocity, DRA enhances jet energy to improve nodule detachment efficiency and effectively reduces nodule aggregation caused by vortices, facilitating collection. In addition, the addition of DRA to the jet does not significantly promote the suspension of sediment particles. Finally, a neural network model optimized by a genetic algorithm is employed to further predict the nodule collection performance under DRA jetting conditions, providing a scientific basis for efficient and intelligent deep-sea mining.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 123061"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227651","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

Enhancing the reliability of marine pipeline transportation systems: A flow safety monitoring method for sand-carrying churn flows via multi-migration collision behavioral responses 提高海上管道运输系统的可靠性：基于多迁移碰撞行为响应的携沙搅拌流流动安全监测方法

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122942

Kai Wang , Jiaqi Tian , Peng Cai , Zhiyuan Wang , Ziang Chang , Jiaqi Lu , Zibiao Wang , Yi Lv , Botao Gou , Yunpeng He

{"title":"Enhancing the reliability of marine pipeline transportation systems: A flow safety monitoring method for sand-carrying churn flows via multi-migration collision behavioral responses","authors":"Kai Wang , Jiaqi Tian , Peng Cai , Zhiyuan Wang , Ziang Chang , Jiaqi Lu , Zibiao Wang , Yi Lv , Botao Gou , Yunpeng He","doi":"10.1016/j.oceaneng.2025.122942","DOIUrl":"10.1016/j.oceaneng.2025.122942","url":null,"abstract":"<div><div>Marine pipeline transportation systems frequently encounter sand-carrying churn flows, wherein persistent sand particle-wall collisions lead to structural degradation of pipelines. This paper proposes a flow safety monitoring method for sand-carrying churn flows based on multi-migration collision behavior responses. Based on the Robust Empirical Mode Decomposition (REMD) algorithm, this study first establishes a multi-frequency scale vibration response characterization method of sand particles for sand-carrying churn flow. Then, a lightweight deep learning architecture based on Depthwise Separable Convolution (DSC) is constructed, achieving an average recognition accuracy of 87.17 % for sand features with contents ranging from 0g to 20g (in 5g increments) across three distinct datasets. Furthermore, the Bidirectional Long Short-Term Memory (BiLSTM) module and Self-Adaptive Temporal Transformer (SATT) module into the DSC framework, thereby enhancing bidirectional full-sequence time-delay feature extraction capability and adaptive weight-matching capacity for holistic particle characteristic information. The DSC-BiLSTM-SATT recognition model improves the average recognition accuracy by 8 %, achieving a final accuracy of 95.17 %. The model shows excellent generalization capability even on low signal-to-noise ratio (SNR) datasets, and the average recognition accuracy for three low SNR datasets reaches 89.73 %. The framework with high accuracy significantly contributes to improve the flow safety and reliability of marine pipeline transportation systems.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122942"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227650","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 behavior-informed adaptive algorithm for hierarchical compression of ship trajectories 基于行为的船舶轨迹分层压缩自适应算法

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122999

Hongfeng Chen, Dechang Pi

{"title":"A behavior-informed adaptive algorithm for hierarchical compression of ship trajectories","authors":"Hongfeng Chen, Dechang Pi","doi":"10.1016/j.oceaneng.2025.122999","DOIUrl":"10.1016/j.oceaneng.2025.122999","url":null,"abstract":"<div><div>The Automatic Identification System (AIS) plays a pivotal role in maritime monitoring, yet its high-frequency data often cause redundancy, affecting storage and downstream analysis. Existing compression algorithms often fail to capture vessel behavior semantics, making it difficult to balance compression rate and semantic preservation in complex scenarios. To address this, we propose a behavior-informed adaptive framework for hierarchical trajectory compression. The framework integrates stay region identification, behavior-oriented segmentation, and multi-feature adaptive compression, enabling differentiated compression across various navigation phases. Stay regions are identified using motion features and spatial density. Navigational behavior patterns are constructed from course sequences, and segmentation is performed using a combination of discrete wavelet transform and entropy-based techniques. Furthermore, introduce multi-dimensional deviation factors and trajectory bending factors, while dynamically setting the compression threshold through a baseline scale adjustment mechanism. In experiments across three representative maritime regions, our method achieves an average compression rate of 78.4 % with a mean spatial error of only 57.8 m, while also maintaining low speed error (0.154 kn) and course error (26.9°). Compared with the benchmark and six advanced algorithms, it consistently delivers the best overall performance, and tests on four typical trajectories further validate its adaptability and robustness.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122999"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204123","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 trajectory prediction model integrating ship-shore speech communication for early prediction at waterway intersections 一种融合船岸语音通信的船舶轨迹预测模型，用于航道交叉口的早期预测

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122934

Yang Chen , Xucun Qi , Dong Yang , Changhai Huang , Jian Zheng

{"title":"A ship trajectory prediction model integrating ship-shore speech communication for early prediction at waterway intersections","authors":"Yang Chen , Xucun Qi , Dong Yang , Changhai Huang , Jian Zheng","doi":"10.1016/j.oceaneng.2025.122934","DOIUrl":"10.1016/j.oceaneng.2025.122934","url":null,"abstract":"<div><div>Early ship trajectory prediction improves traffic coordination but increases the risk of intent misjudgment at waterway intersections, leading to deviations between predicted and actual trajectories. To address this, we propose a ship trajectory prediction model grounded in the International Maritime Organization (IMO) framework and the rule of “intent report - ship maneuver - trajectory change” observed in real-world waterway intersections. Our method enables early intent recognition by leveraging intent information embedded in ship-shore speech communication. Within a defined spatiotemporal range, we associate communication data with observed trajectories to identify reported intentions. The extracted intent labels are integrated with encoded historical trajectory features and fed into a decoder, dynamically constraining predicted directions. This alignment with reported intent advances the prediction timeline without compromising accuracy. Empirical validation at the Wusongkou Estuary (Shanghai, China) demonstrates that our model advances the prediction timeline by 6.94–8.4 min compared to existing models, while maintaining similar accuracy. This work pioneers the integration of ship-shore speech communication into trajectory prediction, highlighting the potential of AI-driven maritime safety systems.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122934"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204125","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 direct FEA loading approach for combined failure envelope of foundations in cohesive soil 粘性土中地基复合破坏包络的直接有限元加载方法

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122962

Si-Da Wu, Zhen-Yu Yin, Maozhu Peng

{"title":"A direct FEA loading approach for combined failure envelope of foundations in cohesive soil","authors":"Si-Da Wu, Zhen-Yu Yin, Maozhu Peng","doi":"10.1016/j.oceaneng.2025.122962","DOIUrl":"10.1016/j.oceaneng.2025.122962","url":null,"abstract":"<div><div>Foundations, particularly offshore foundations, are subjected to multidirectional combined loading, making accurate failure envelopes essential for geotechnical design. Traditional methods for constructing these envelopes face significant challenges. This study presents a Direct Displacement Swipe (DDS) method that indirectly steers the displacement-space trajectory to conform to a prescribed load-space trajectory, implemented within a Finite Element Analysis (FEA) framework to improve accuracy and computational efficiency. Different from conventional swipe method, a truss element is used to simulate the rope, with one end connecting to the foundation and the other end for loading, resulting in the same direction of displacement and load. Validation across various types of foundations, including circular surface footings, suction caissons, tripod buckets, and composite pile-bucket foundations, covering a range of shallow to deep foundation categories, under diverse cohesive soil conditions, highlights its robustness. Comparative analysis shows the DDS method matches the accuracy of traditional approaches while significantly reducing computational costs. Additionally, it effectively captures both symmetrical and asymmetrical failure envelopes, where traditional methods often fall short. Therefore, the DDS method emerges as a practical, efficient, and reliable alternative for geotechnical design.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122962"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204128","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

Research on AUV underwater path planning based on preference-driven interval multi-objective optimization algorithm 基于偏好驱动区间多目标优化算法的AUV水下路径规划研究

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122923

Chengchang Tong, Yixiang Wang, Weizhe Zhang, Hongbo Wang

{"title":"Research on AUV underwater path planning based on preference-driven interval multi-objective optimization algorithm","authors":"Chengchang Tong, Yixiang Wang, Weizhe Zhang, Hongbo Wang","doi":"10.1016/j.oceaneng.2025.122923","DOIUrl":"10.1016/j.oceaneng.2025.122923","url":null,"abstract":"<div><div>This study investigates the autonomous underwater vehicle (AUV) path-planning problem in complex and uncertain marine environments. Considering various factors such as dynamic ocean currents, terrain complexity, and the uncertainty of hazardous locations, an interval number approach is employed to model ocean current parameters and uncertain hazardous areas, thereby transforming uncertainties into interval constraints and formulating an interval multiobjective optimization problem. Based on this framework, the preference interval multiobjective particle swarm optimization algorithm (P-IMO-PSO) is proposed. The proposed algorithm integrates the decisionmaker’s preference information to guide the optimization process. This approach balances navigation time, path safety, and energy consumption while improving iteration efficiency. The results of MATLAB simulation experiments validate the performance of the proposed algorithm under different ocean current models and uncertain environmental conditions. The results show that, compared with the traditional IMO-PSO algorithm, the proposed P-IMO-PSO significantly enhances path-planning efficiency by reducing the mean navigation time interval by 20.85 %, while also optimizing navigation time, mitigating randomness, and accelerating convergence In addition, the paths generated by the proposed algorithm align better with decision-maker (DM) preferences, leveraging ocean currents advantageously while ensuring safety, thereby enhancing AUV navigation efficiency. These advantages highlight the superior applicability and robustness of the proposed method in complex underwater environments.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122923"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204131","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

Optimization of a mooring system applying a deep neural network under multi-directional environmental conditions 基于深度神经网络的多方向环境下系泊系统优化

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122992

Sungjun Jung , Jae Hwan Jung , Bonguk Koo , Janghoon Seo

{"title":"Optimization of a mooring system applying a deep neural network under multi-directional environmental conditions","authors":"Sungjun Jung , Jae Hwan Jung , Bonguk Koo , Janghoon Seo","doi":"10.1016/j.oceaneng.2025.122992","DOIUrl":"10.1016/j.oceaneng.2025.122992","url":null,"abstract":"<div><div>This study investigates the optimization of a mooring system using a deep learning technique, addressing the limitation of considering only a single environmental direction. To find a robust optimal design, a Deep Neural Network (DNN) model was established to predict mooring line tension and offset of the floating structure based on variations in component parameters and multiple environmental directions. The trained DNN model was then integrated with a Non-dominated Sorting Genetic Algorithm II (NSGA-II). A representative optimal solution focused on tension reduction showed a decrease of approximately 2.7 % compared to a base case, while another representative solution focused on offset reduction achieved a decrease of approximately 17 %. Furthermore, the most economical solution reduced the mooring line weight by approximately 19 %. A comparison confirmed that a mooring system designed from a single-direction optimization violated design constraints when its responses were evaluated under other environmental directions. This study confirms the feasibility of applying a deep learning technique to the mooring system optimization process and highlights the necessity of considering multi-directional environmental conditions to find a robust optimal design, while also significantly improving computational efficiency by approximately 50 %. Future work includes analyzing non-collinear environmental conditions and applying the methodology to various mooring configurations.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122992"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204400","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

Carbon emission control considering the reward and punishment mechanism in shipping 考虑奖惩机制的航运碳排放控制

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122970

Zhisen Yang , Bing Qing Tan , Zaili Yang , Qin Luo , Jingbo Yin , Xiang T.R. Kong

{"title":"Carbon emission control considering the reward and punishment mechanism in shipping","authors":"Zhisen Yang , Bing Qing Tan , Zaili Yang , Qin Luo , Jingbo Yin , Xiang T.R. Kong","doi":"10.1016/j.oceaneng.2025.122970","DOIUrl":"10.1016/j.oceaneng.2025.122970","url":null,"abstract":"<div><div>The control and monitoring of vessel carbon emission is gradually being a crucial task in green shipping. As stated by the International Maritime Organization, incentives are encouraged to stimulate relevant practitioners to take active strategies. In this research, a tripartite evolutionary game involving three groups of main stakeholders (port authorities, ship owners, charterers) is developed, aiming at analyzing their strategy selection under the implementation of the reward and punishment mechanism (RPM). The numerical simulation indicates that port authorities could improve the low-carbon awareness of vessel-related stakeholders effectively through formulating a reasonable RPM, leading to the ideal situation that carbon emission can be controlled without the need of introducing external force in the future. Further, the influence of key parameters (i.e., additional cost, revenue increment, environmental benefit) on the strategy selection is thoroughly examined and evaluated, which provides insightful implications for the formulation of the dynamic and reasonable RPM.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122970"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204402","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

Assessing human reliability in life raft inspection and maintenance to improve onboard ship operational safety 评估救生筏检修人员的可靠性，提高船上操作安全

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.123048

Muhammet Aydin

{"title":"Assessing human reliability in life raft inspection and maintenance to improve onboard ship operational safety","authors":"Muhammet Aydin","doi":"10.1016/j.oceaneng.2025.123048","DOIUrl":"10.1016/j.oceaneng.2025.123048","url":null,"abstract":"<div><div>Ensuring operational safety is of critical importance for the protection of human life in the maritime industry. One of the most crucial links in this safety chain is the proper execution of periodic maintenance and inspection of emergency equipment, such as life rafts. This study presents a hybrid methodology to assess Human Error Probabilities (HEPs) in life raft inspection and maintenance operations on board ships. The traditional Success Likelihood Index Method (SLIM) is integrated with improved Z-numbers to more effectively model the uncertainties and subjectivity inherent in expert judgments. Through Hierarchical Task Analysis (HTA), the life raft maintenance process was decomposed into fifteen sub-tasks, and Performance Shaping Factors (PSFs) were identified for these tasks. HEP values for each sub-task were calculated based on the evaluations of a panel of nine maritime experts. The analysis results indicate that tasks such as “Log and close-out inspection in maintenance system” (HEP: 1.85E-02) and “Review service expiry dates and PSC remarks” (HEP: 8.21E-03) have the highest error probabilities. The findings of this study identify the weakest links in life raft maintenance operations, providing a concrete basis for measures in training, procedural improvements, and supervision. This methodology represents a significant step towards enhancing ship operational safety by enabling a more precise management of human-related risks in the maritime domain.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 123048"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204404","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

Ship motion forecasting under varying operating conditions via multi-feature fusion 基于多特征融合的船舶运动预测

IF 5.5 2区工程技术

Ocean Engineering Pub Date : 2025-10-03 DOI: 10.1016/j.oceaneng.2025.122958

Jiaxuan Zhang , Liang Du , Guanxin Hong , Gang Liu

{"title":"Ship motion forecasting under varying operating conditions via multi-feature fusion","authors":"Jiaxuan Zhang , Liang Du , Guanxin Hong , Gang Liu","doi":"10.1016/j.oceaneng.2025.122958","DOIUrl":"10.1016/j.oceaneng.2025.122958","url":null,"abstract":"<div><div>Ships navigating in the ocean are subject to irregular perturbation motions. Accurate forecasting of such motions significantly enhances navigation safety and onboard operational efficiency. However, traditional ship motion forecasting methods are typically designed for single, stable operating conditions and lack generalizability across varying conditions. To address this limitation, we propose Multi-Feature-Informer (MF-Informer), a motion forecasting model based on multi-feature fusion technology, applicable to most operating conditions. The model is trained and evaluated on a dataset comprising perturbation motion data of the KVLCC2 vessel under 1000 randomly distributed operating conditions in sea states 2–6. It employs band-pass spectral extraction techniques based on prior physical knowledge of ship spectral distributions when extracting frequency features. Multi-feature fusion techniques, including Cross Attention and concatenated linear projection, are employed and compared in this study. The model’s hyperparameters are optimized using the Sparrow Search Algorithm (SSA). Experimental results demonstrate that, compared to models without feature fusion, MF-Informer reduces the mean squared error (MSE) for heave, roll, and pitch forecasts by <span><math><mrow><mn>24.29</mn><mspace></mspace><mo>%</mo><mo>,</mo><mn>20.24</mn><mspace></mspace><mo>%</mo><mo>,</mo><mn>26.27</mn><mspace></mspace><mo>%</mo></mrow></math></span>, respectively. Additionally, the operating condition feature extraction module exhibits strong physical interpretability.</div></div>","PeriodicalId":19403,"journal":{"name":"Ocean Engineering","volume":"342 ","pages":"Article 122958"},"PeriodicalIF":5.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204127","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