Article
Engineering, Marine
Yuki Obara, Ryota Nakamura
Summary: This study evaluated the prediction of significant wave height using transfer learning in long short-term memory networks. The results showed that transfer learning improved the accuracy of the prediction and transferring all layers was more effective than transferring only part of the layers.
Article
Computer Science, Interdisciplinary Applications
Yan Li, Xue Qin, Daoheng Zhu
Summary: A fusion prediction model based on MIC and LSTM is proposed to predict significant wave heights of nearshore ocean hydrographic data in Yangjiang City, China and the NDBC deep-sea buoy data. Compared with other models, the MIC-LSTM model shows smaller deviation, fewer feature variables in the public dataset, and higher screening efficiency, thereby improving the efficiency and effectiveness of nearshore significant wave height prediction.
EARTH SCIENCE INFORMATICS
(2023)
Article
Environmental Sciences
Peng Hao, Shuang Li, Yu Gao
Summary: In this study, the predictive performance of significant wave height (SWH) using recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) is comprehensively analyzed by considering different input lengths, prediction lengths, and model complexity. The results show that the input length and prediction length have an impact on the SWH prediction, but longer input length and longer prediction length do not necessarily lead to better prediction performance. Moreover, the number of layers in the model does not always determine the prediction performance.
FRONTIERS IN MARINE SCIENCE
(2023)
Article
Computer Science, Information Systems
Pritam Anand, Shantanu Jain, Harsh Savaliya
Summary: In this paper, a series of wave hybrid models for predicting significant wave height have been developed. These models incorporate signal decomposition methods, regression models, and meta-heuristic algorithms. The performance of the developed models is evaluated using different criteria, and it is found that the VMD-PSO-LDMR based model outperforms others in most cases. Furthermore, the hybrid models utilizing the VMD method demonstrate superior prediction abilities compared to other decomposition-based models. However, the LSTM model performs better than other regression models when the significant wave height signals are not decomposed apriori.
Article
Marine & Freshwater Biology
Ahmed Elsayed Elkut, Mostafa Tawfik Taha, Abu Bakr Elseddiek Abu Zed, Fahmy Mohammed Eid, Mohammed Abdallah Abdallah
Summary: This study evaluates the usage of the SWAN model forced with modified wind field along the Mediterranean Sea, showing that enhancing the wind field by 20%-25% can improve the accuracy of predicting significant wave height. However, there were no significant improvements in simulating wave period despite the enhancements. The study also highlights the high performance of the calibrated SWAN model with the modified wind field in capturing wave characteristics, but with considerations for potential overestimation of storm conditions in extreme cases.
ESTUARINE COASTAL AND SHELF SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang
Summary: The improved nonparallel support vector machine (INPSVM) proposed in this article inherits the advantages of nonparallel support vector machine (NPSVM) while also offering incomparable benefits over twin support vector machine (TSVM). INPSVM effectively eliminates noise effects and achieves higher classification accuracy for both linear and nonlinear datasets compared to other algorithms. Experimental results demonstrate the superior efficiency, accuracy, and robustness of INPSVM.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chen Ding, Tian-Yi Bao, He-Liang Huang
Summary: The study proposes a quantum-inspired classical algorithm for LS-SVM, utilizing an improved sampling technique for classification. The theoretical analysis indicates that the algorithm can achieve classification with logarithmic runtime for low-rank, low-condition number, and high-dimensional data matrices.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
M. Tanveer, A. Tiwari, R. Choudhary, M. A. Ganaie
Summary: This study proposes a novel large scale pinball twin support vector machine (LPTWSVM) to address the limitations of the twin support vector machines (TWSVMs), using a unique pinball loss function and improving model performance by eliminating matrix inversion calculation and minimizing structural risk.
Article
Engineering, Civil
Gulhan Ozdogan-Sarikoc, Mehmet Sarikoc, Mete Celik, Filiz Dadaser-Celik
Summary: Reservoirs play essential roles in water supply, flood control, irrigation, and hydroelectric production. Effective reservoir operation is crucial for these functions. This study predicts reservoir volumes using Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) in Ladik and Yedikir Reservoirs. The models' performance is analyzed, and the results show that the LSTM model performs best in both reservoirs due to its ability to adapt to temporal dynamics.
JOURNAL OF HYDROLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Matteo Avolio, Antonio Fuduli
Summary: This paper introduces a novel approach for binary multiple instance learning classification, combining the strengths of SVM and PSVM, aiming to discriminate between positive and negative instances by generating a hyperplane placed in the middle between two parallel hyperplanes.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Ergonomics
Azhar Quddus, Ali Shahidi Zandi, Laura Prest, Felix J. E. Comeau
Summary: Fatigue hinders drivers' safety and performance, and detecting drowsiness through eye movements is an attractive approach. This paper proposes using RNN to capture eye-related movements and LSTM to model the eye movements, showing significant efficacy in detecting drowsiness.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Engineering, Marine
Xiaoyu Zhang, Yongqing Li, Song Gao, Peng Ren
Summary: This study investigates the use of N-LSTM model to combine numerical predictions with wave height measurements, correcting the accuracy of wave height numerical predictions for the Bohai Sea and Xiaomaidao. By incorporating LSTM module and Gaussian approximation module, the N-LSTM achieves 10% to 20% better prediction accuracy compared to state-of-the-art machine learning methods in prediction time ranging from 3 to 72 hours.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Jinyuan Zhang, Yan Feng, Jiaxuan Zhang, Yijun Li
Summary: This study utilizes machine learning models to predict the energy level of geomagnetic storms. The LSTM model shows higher prediction accuracy in both active and quiet periods, while the EMD-LSTM model addresses the issue of prediction lag and provides better predictions for geomagnetic storms.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Mohammad Aslani, Stefan Seipel
Summary: A novel instance selection method called BPLSH is designed to address the high computational complexity of SVMs in the training phase on large datasets. Experimental results show that BPLSH outperforms other methods in terms of classification accuracy, preservation rate, and execution time.
INFORMATION SCIENCES
(2021)
Article
Environmental Sciences
Sarmad Dashti Latif
Summary: Compressive strength is a critical parameter in concrete design, and measuring it correctly can reduce time and cost. Research shows that a model predicting concrete compressive strength utilizing a detailed dataset and deep learning methods can be effective.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Water Resources
Chenglin Duan, Sheng Dong, Zhifeng Wang, Zhenkun Liao
JOURNAL OF WATER AND CLIMATE CHANGE
(2020)
Article
Engineering, Marine
Jiawen Sun, Zhe Ma, Dongxu Wang, Sheng Dong, Ting Zhou
INTERNATIONAL JOURNAL OF NAVAL ARCHITECTURE AND OCEAN ENGINEERING
(2020)
Article
Engineering, Marine
Yifan Lin, Sheng Dong, Shanshan Tao
Article
Engineering, Marine
Junnan Cui, Sheng Dong, Zhifeng Wang, Xinyu Han, Peng Lv
Article
Ecology
Sheng Dong, Yijie Gong, Zhifeng Wang
REGIONAL STUDIES IN MARINE SCIENCE
(2020)
Article
Engineering, Mechanical
Fengyuan Jiang, Sheng Dong
ENGINEERING FAILURE ANALYSIS
(2020)
Article
Engineering, Marine
Qilin Yin, Jinjin Zhai, Sheng Dong
Summary: The study found that there is a critical self-weight W(critical) in the double-layer seabed, leading to two different failure modes of the platform. In the single-layer seabed, the failure mode is the windward leg being pulled up, with H(ult) increasing as W increases.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT
(2021)
Article
Engineering, Marine
Yuliang Zhao, Sheng Dong, Fengyuan Jiang
Summary: The article proposes a methodology to assess the reliability of mooring lines under extreme environmental conditions based on artificial neural network-Bayesian network inference. After establishing a failure database, a reliability analysis of moored floating structures is conducted using Bayesian networks.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT
(2021)
Article
Engineering, Marine
Fengyuan Jiang, Sheng Dong
Summary: This study introduces a risk-based integrity model for offshore pipelines, which incorporates genetic programming surrogate modeling and finite element analysis to reduce the risk of failure from falling objects. The research emphasizes the significant influence of soil properties and pipeline wall thickness on the risk.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT
(2021)
Article
Oceanography
Yuliang Zhao, Dahui Liu, Sheng Dong
JOURNAL OF OCEAN UNIVERSITY OF CHINA
(2020)
Article
Oceanography
Chenglin Duan, Sheng Dong, Zhifeng Wang
CONTINENTAL SHELF RESEARCH
(2020)
Article
Engineering, Marine
Yuliang Zhao, Sheng Dong
Article
Engineering, Marine
Yuliang Zhao, Sheng Dong
Summary: This paper presents an extension of the alternative environmental contour approach based on inverse first-order reliability theory in a three-dimensional model. It considers short-term extreme response uncertainties and investigates long-term extreme tension under wave excitation loads. The applicability of the load assessment models is demonstrated using a case study of a platform, and the results are compared with one- and two-dimensional environmental contour-based models.
SHIPS AND OFFSHORE STRUCTURES
(2022)
Article
Engineering, Marine
Xinyu Han, Sheng Dong
Article
Geochemistry & Geophysics
Chenglin Duan, Sheng Dong, Zhifeng Wang
PURE AND APPLIED GEOPHYSICS
(2020)
Article
Engineering, Marine
Alba Ricondo, Laura Cagigal, Beatriz Perez-Diaz, Fernando J. Mendez
Summary: This research presents a site-specific metamodel based on the SWASH numerical model simulations, which can predict coastal hydrodynamic variables in a fast and efficient manner. The metamodel uses downscaled and dimensionality reduced synthetic database to accurately reproduce wave setup, wave heights associated with different frequency bands, and wave runup. This method has great potential in coastal risk assessments, early warning systems, and climate change projections.
Article
Engineering, Marine
Xiao Yu, Wangjun Ren, Bukui Zhou, Li Chen, Xiangyun Xu, Genmao Ren
Summary: This study investigated and compared the compression responses and energy absorption capacities of coral sand and silica sand at a strain rate of approximately 1000 s-1. The results showed that coral sand had significantly higher energy absorption capacity than silica sand due to its higher compressibility. The study findings suggest that using poorly graded coral sand can improve its energy absorption capacity.
Article
Engineering, Marine
Jingxi Zhang, Junmin Mou, Linying Chen, Pengfei Chen, Mengxia Li
Summary: This paper proposes a cooperative control scheme for ship formation tracking based on Model Predictive Control. A predictive observer is designed to estimate the current motion states of the leader ship using delayed motion information. Comparative simulations demonstrate the effectiveness and robustness of the proposed controller.
Article
Engineering, Marine
Yu Yao, Danni Zhong, Qijia Shi, Ji Wu, Jiangxia Li
Summary: This study proposes a 2DH numerical model based on Boussinesq equations to investigate the impact of dredging reef-flat sand on wave characteristics and wave-driven current. The model is verified through wave flume experiments and wave basin experiments, and the influences of incident wave conditions and pit morphological features on wave characteristics are examined.
Article
Engineering, Marine
Jayanta Shounda, Krishnendu Barman, Koustuv Debnath
Summary: This study investigates the double-average turbulence characteristics of combined wave-current flow over a rough bed with different spacing arrangements. The results show that a spacing ratio of p/r=4 offers the highest resistance to the flow, and the double-average Reynolds stress decreases throughout the flow depth. The advection of momentum-flux of normal stress shows an increase at the outer layer and a decrease near the bed region after wave imposition. Maximum turbulence kinetic energy production and diffusion occur at different layers. The turbulence structure is strongly anisotropic at the bottom region and near the outer layer, with a decrease in anisotropy observed with an increase in roughness spacing.
Article
Engineering, Marine
Meng Zhang, Lianghui Sun, Yaoguo Xie
Summary: The research proposes a method for online identification of wave bending and torsional moment in hull structures. For structures without large openings, the method optimizes sensor positions and establishes a mathematical model to improve accuracy. For structures with large openings, a joint dual-section monitoring method is proposed to simultaneously identify bending and torsional moments in multiple key cross sections.
Article
Engineering, Marine
Longming Chen, Shutao Li, Yeqing Chen, Dong Guo, Wanli Wei, Qiushi Yan
Summary: This study investigated the dynamic response characteristics and damage modes of pile wharves subjected to underwater explosions. The results showed that the main damaged components of the pile wharf were the piles, and inclined piles had a higher probability of moderate or more significant damage compared to vertical piles. The study also suggested that replacing inclined piles with alternative optimized structures benefits the blast resistance of pile wharves.
Article
Engineering, Marine
I. -C Kim, G. Ducrozet, V. Leroy, F. Bonnefoy, Y. Perignon, S. Bourguignon
Summary: Previous research focused on the accuracy and efficiency of short-term wave fields in specific prediction zones, while we developed algorithms for continuous wave prediction based on the practical prediction zone and discussed important time factors and strategies to reduce computational costs.
Article
Engineering, Marine
Hang Xie, Xianglin Dai, Fang Liu, Xinyu Liu
Summary: This study investigates the load characteristics of a three-dimensional stern model with pitch angle through a drop test, and reveals complex characteristics of pressure distribution near the stern shaft. The study also shows that the vibration characteristics of the load are influenced by the drop height and pitch angle, with the drop height having a greater effect on the high-frequency components.
Article
Engineering, Marine
Hangyuan Zhang, Wanli Yang, Dewen Liu, Xiaokun Geng, Wangyu Dai, Yuzhi Zhang
Summary: The deep-water bridge is more vulnerable to earthquake damage than the bridge standing in air. The larger blocking ratio has a significant impact on the added mass coefficient, which requires further comprehensive study. The generation mechanism of block effect is analyzed using numerical simulation software ANSYS Fluent. The results show that the recirculation zone with focus reduces the pressure on the back surface of the cylinder, resulting in the peak value of in-line force not occurring synchronously with the peak value of acceleration. The change in position and intensity of the recirculation zone with focus, as well as the change in water flow around the cylinder surface, are identified as the generation mechanism of the block effect, which has a 10% influence on the hydrodynamic force. The changing rule of the added mass coefficient with blocking ratio is discussed in detail, and a modification approach to the current added mass coefficient calculation method is suggested. Physical experiments are conducted to validate the modification approach, and the results show that it is accurate and can be used in further study and real practice.
Article
Engineering, Marine
Golnesa Karimi-Zindashti, Ozgur Kurc
Summary: This study examines the performance of an in-house code utilizing a deterministic vortex method on the rotation of circular and square cylinders. The results show that rotational motion reduces drag forces, suppresses fluctuating forces, and increases lift forces. The code accurately predicts vortex shedding suppression and identifies the emergence of near-field wakes in the flow over rotating square cylinders.
Article
Engineering, Marine
George Dafermos, George Zaraphonitis
Summary: The survivability of damaged ships is of great importance and the regulatory framework is constantly updated. The introduction of the probabilistic damage stability framework has rationalized the assessment procedure. Flooding simulation tools can be used to investigate the dynamic response of damaged ships.
Article
Engineering, Marine
Xuyue Chen, Xu Du, Chengkai Weng, Jin Yang, Deli Gao, Dongyu Su, Gan Wang
Summary: This paper proposes a real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on intelligent optimization algorithm and machine learning. By establishing a ROP model with long short-term memory neurons, and combining genetic algorithm, differential evolution algorithm, and particle swarm algorithm, the method achieves real-time optimization of drilling parameters and significantly improves the ROP.
Article
Engineering, Marine
Sung-Jae Kim, Chungkuk Jin, MooHyun Kim
Summary: This study investigates the dynamic behavior of a moored submerged floating tunnel (SFT) under tsunami-like waves through numerical simulations and sensitivity tests. The results show that design parameters significantly affect the dynamics of the SFT system and mooring tensions, with shorter-duration and higher-elevation tsunamis having a greater impact.
Article
Engineering, Marine
G. Clarindo, C. Guedes Soares
Summary: Environmental contours are constructed using the Inverse-First Order Reliability Method based on return periods. The paper proposes the use of the Burr distribution to model the marginal distribution of long-term significant wave heights. The newly implemented scheme results in different environmental contours compared to the reference approach.