Journal of Ocean Engineering and Science最新文献_第6页

Sea surface reconstruction from marine radar images using deep convolutional neural networks 基于深度卷积神经网络的海洋雷达图像海面重建

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2023.09.002

Mingxu Zhao , Yaokun Zheng , Zhiliang Lin

{"title":"Sea surface reconstruction from marine radar images using deep convolutional neural networks","authors":"Mingxu Zhao , Yaokun Zheng , Zhiliang Lin","doi":"10.1016/j.joes.2023.09.002","DOIUrl":"10.1016/j.joes.2023.09.002","url":null,"abstract":"<div><p>The sea surface reconstructed from radar images provides valuable information for marine operations and maritime transport. The standard reconstruction method relies on the three-dimensional fast Fourier transform (3D-FFT), which introduces empirical parameters and modulation transfer function (MTF) to correct the modulation effects that may cause errors. In light of the convolutional neural networks’ (CNN) success in computer vision tasks, this paper proposes a novel sea surface reconstruction method from marine radar images based on an end-to-end CNN model with the U-Net architecture. Synthetic radar images and sea surface elevation maps were used for training and testing. Compared to the standard reconstruction method, the CNN-based model achieved higher accuracy on the same data set, with an improved correlation coefficient between reconstructed and actual wave fields of up to 0.96-0.97, and a decreased non-dimensional root mean square error (NDRMSE) of around 0.06. The influence of training data on the deep learning model was also studied. Additionally, the impact of the significant wave height and peak period on the CNN model’s accuracy was investigated. It has been demonstrated that the accuracy will fluctuate as the wave steepness increases, but the correlation coefficient remains above 0.90, and the NDRMSE remains less than 0.11.</p></div>","PeriodicalId":48514,"journal":{"name":"Journal of Ocean Engineering and Science","volume":"8 6","pages":"Pages 647-661"},"PeriodicalIF":7.1,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S246801332300058X/pdfft?md5=157230f141b6426716a9cdd5a460c28b&pid=1-s2.0-S246801332300058X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135408844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

An optimal coupling incentive mechanism concerning insider's compliance behavior towards marine information security policy 内部人遵守海洋信息安全政策行为的最优耦合激励机制

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.05.023

Xiaolong Wang, Changlin Wang, Zaiguan Sun, Chunhui Wang

引用次数: 0

Dynamic quantitative risk assessment of LNG bunkering SIMOPs based on Bayesian network 基于贝叶斯网络的LNG加注SIMOP动态定量风险评估

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.03.004

Hongjun Fan, Hossein Enshaei, Shantha Gamini Jayasinghe

{"title":"Dynamic quantitative risk assessment of LNG bunkering SIMOPs based on Bayesian network","authors":"Hongjun Fan, Hossein Enshaei, Shantha Gamini Jayasinghe","doi":"10.1016/j.joes.2022.03.004","DOIUrl":"10.1016/j.joes.2022.03.004","url":null,"abstract":"<div><p>Liquified natural gas (LNG) bunkering simultaneous operations (SIMOPs) refers to the operations (such as cargo operations, port activities and ship maintenance) occurring around LNG bunkering. SIMOPs pose new risks to LNG bunkering, because the operations are dynamically interlocked in which the occurrence probabilities of potential consequences change at different times due to commencement or completion of specific SIMOP events. However, traditional static risk assessment approaches are not able to take the dynamic nature of these new risks into account. This article proposes a dynamic quantitative risk assessment (DQRA) methodology based on the Bayesian network (BN) to develop better understanding of dynamic risks of LNG bunkering SIMOPs. The methodology is demonstrated and evaluated through a truck-to-ship LNG bunkering case study. The results and discussion of the case study validate the utility of the proposed methodology and demonstrate that BNs are efficient in performing the probability calculations and are flexible in conducting causal diagnosis. The main innovation of this work is realizing the quantification of risks at different times, which reflects the most essential time-changing characteristics of risks associated with LNG bunkering SIMOPs.</p></div>","PeriodicalId":48514,"journal":{"name":"Journal of Ocean Engineering and Science","volume":"8 5","pages":"Pages 508-526"},"PeriodicalIF":7.1,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47013829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 6

Diversity of wave structures to the conformable fractional dynamical model 适形分数动力模型波动结构的多样性

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.04.014

U. Younas, J. Ren

{"title":"Diversity of wave structures to the conformable fractional dynamical model","authors":"U. Younas, J. Ren","doi":"10.1016/j.joes.2022.04.014","DOIUrl":"10.1016/j.joes.2022.04.014","url":null,"abstract":"<div><p>This manuscript examines the recently developed conformable three-dimensional Wazwaz–Benjamin–Bona–Mahony (3D-WBBM) equation’s dynamical behavior in terms of its spatial and temporal variables. The governing equation is stretch for the Korteweg-de-Vries equation that represents the unidirectional propagation of small amplitude long waves on the surface of hydro magnetic and acoustic waves in a channel, especially for shallow water. Solitary wave solutions of various types, such as kink and shock, as well as singleton, combined solitons, and complex solitons, are all retrieved. Additionally, solutions to hyperbolic, exponential, and trigonometric functions are obtained through the use of recently developed methods, namely the Kudryashov method (KM), the modified Kudryashov method (MKM), and the new extended direct algebraic method (NEDAM). The study conducts a comparison of our findings to well-known findings, and concludes that the solutions reached here are novel. Additionally, the earned results are sketched in different shapes to demonstrate their dynamics as a function of parameter selection. We can assert from the obtained results that the applied techniques are simple, vibrant, and quite well, and will be helpful tool for addressing more highly nonlinear issues in various of fields, especially in ocean and coastal engineering. Furthermore, our findings are first step toward understanding the structure and physical behavior of complicated structures. We anticipate that our results will be highly valuable in better understanding the waves that occur in the ocean. We feel that this work is timely and will be of interest to a wide spectrum of experts working on ocean engineering models.</p></div>","PeriodicalId":48514,"journal":{"name":"Journal of Ocean Engineering and Science","volume":"8 5","pages":"Pages 559-572"},"PeriodicalIF":7.1,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44961383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 11

Optimal Kalman-like filter for a class of nonlinear stochastic systems 一类非线性随机系统的最优类卡尔曼滤波器

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.03.002

Shulan Kong , Yawen Sun , Huanshui Zhang

引用次数: 1

New solitary waves and exact solutions for the fifth-order nonlinear wave equation using two integration techniques 用两种积分技术求解五阶非线性波动方程的新孤立波和精确解

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.02.012

Ahmed H. Arnous , Mohammad Mirzazadeh , Lanre Akinyemi , Arzu Akbulut

引用次数: 12

New soliton wave solutions of a (2 + 1)-dimensional Sawada-Kotera equation (2+1)维Sawada-Kotera方程的新孤子波解

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.03.007

Kong Debin , Hadi Rezazadeh , Najib Ullah , Javad Vahidi , Kalim U. Tariq , Lanre Akinyemi

引用次数: 8

Comparative life cycle impact assessment of offshore support vessels powered by alternative fuels for sustainable offshore wind operations using machine learning 使用机器学习对可持续海上风力发电替代燃料驱动的海上支持船的生命周期影响进行比较评估

1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2023.10.005

Shoaib Ahmed, Tie Li, Shi Yan Li, Run Chen

引用次数: 0

Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models 用模糊元启发式模型预测玻璃纤维增强聚合物棒在恶劣碱性混凝土环境中的残余抗拉强度

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.03.011

Mudassir Iqbal , Khalid Elbaz , Daxu Zhang , Lili Hu , Fazal E. Jalal

{"title":"Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models","authors":"Mudassir Iqbal , Khalid Elbaz , Daxu Zhang , Lili Hu , Fazal E. Jalal","doi":"10.1016/j.joes.2022.03.011","DOIUrl":"10.1016/j.joes.2022.03.011","url":null,"abstract":"<div><p>The long-term durability of glass fiber reinforced polymer (GFRP) bars in harsh alkaline environments is of great importance in engineering, which is reflected by the environmental reduction factor in various structural codes. The calculation of this factor requires robust models to predict the residual tensile strength of GFRP bars. Therefore, three robust metaheuristic algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM), were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system (ANFIS) in order to obtain more accurate prediction model. Various optimized models were developed to predict the tensile strength retention (TSR) of degraded GFRP rebars in typical alkaline environments (e.g., seawater sea sand concrete (SWSSC) environment in this study). The study also proposed more reliable model to predict the TSR of GFRP bars exposed to alkaline environmental conditions under accelerating laboratory aging. A total number of 715 experimental laboratory samples were collected in a form of extensive database to be trained. K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal folds. In order to analyze the efficiency of the metaheuristic algorithms, multiple statistical tests were performed. It was concluded that the ANFIS-SVM-based model is robust and accurate in predicting the TSR of conditioned GFRP bars. In the meantime, the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of GFRP bars in alkaline concrete environment. The sensitivity analysis revealed GFRP bar size, volume fraction of fibers, and pH of solution were the most influential parameters of TSR.</p></div>","PeriodicalId":48514,"journal":{"name":"Journal of Ocean Engineering and Science","volume":"8 5","pages":"Pages 546-558"},"PeriodicalIF":7.1,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48369029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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: 6

Numerical treatment of temporal-fractional porous medium model occurring in fractured media 裂隙介质中时间分数多孔介质模型的数值处理

IF 7.1 1区工程技术

Journal of Ocean Engineering and Science Pub Date : 2023-10-01 DOI: 10.1016/j.joes.2022.02.016

R. Meher , J. Kesarwani , Z. Avazzadeh , O. Nikan

引用次数: 3