...: first, collision avoidance methods based on analytical models, such as Velocity Obstacle (VO) and Artificial Potential Field (APF); second, intelligent algorithms, including Simulated Annealing (SA), Genetic Algorithm (GA); and third, machine learning methods, such as K-Means, Deep Reinforcement...

... numerical studies of the wave-induced motions of a crane vessel with a hanging load and tested the linear and nonlinear methods for determining the wave excitation force and restoring force. subsea system topside matrix artificial intelligence thruster controller pid controller machine...

... for a complicated response problem such as coupled analysis of moored FPSO. risk management machine learning reservoir simulation subsea system carlo simulation mooring system floating production systems monte carlo simulation probability moored fpso exceedance probability characteristic...

... both in terms of oscillatory motions and mean drift components whose net result is expressed as an offset with respect to the initial rest position of the system. Two approaches were followed to define the input data for the machine learning model: whether or not to consider the information from...

... Key Research and Development Program of China (Grant No. 2022YFC28061004). REFERENCES Bishop, C (2006). Pattern recognition and machine learning," Springer google schola, 2, 531-537. Brock, C, Sullivan, A, Peltier, R, Weber, R, Wollny, A, De Gouw, J, Middlebrook, A, Atlas, E, Stohl, A and Trainer, M...

... to deal with these data is less efficient and less effective, and machine learning methods have advantages in this regard. Chen Shuai et al. (2023) proposed an intelligent forecasting method for low-frequency wave loads using a Bayesian optimization algorithm and a deep neural network, and based...

... conditions, multiple machine learning models were used to train and predict the data samples, and the better model was compared to the random forest. Bayesian optimization algorithm was used to optimize the hyperparameters of the random forest, and the accuracy of the prediction model was evaluated through...

..., for example image processing, object tracking, or machine learning (Chandan et al., 2018). The first method used a colour filtering approach to identify coating flakes by colour, whereas the second method applied a gray-scale to the images, identifying coating flakes by their brightness. Both methods were...

... cylindrical shell with different rib stiffness distributions under consideration. stiffness machine learning parabolic variation artificial intelligence titanium alloy optimization stability geologist reservoir geomechanics variation requirement thickness strength constraint radial...

... ship prediction displacement machine learning coefficient artificial intelligence optimization algorithm journal accuracy efficiency ship lifting machine cargo ship duration prediction draught Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference...

... distributed in different locations in the ocean, to area-wise measurements such as the use of Lidar, Radar, and stereoscopy. An analogy of the buoy is to estimate sea state parameters from motion measurements onboard a ship through Bayesian estimation, Kalman filter, and machine learning (Nielsen, 2008; Kim...

... gate box layout optimization production monitoring machine learning exponent chromosome sensitivity Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference Rhodes, Greece, June 16 21, 2024 Copyright © 2024 by the International Society of Offshore and Polar...

.... maintenance category energy procedia operation turbine generator model cooling liq artificial intelligence resilience assessment framework ocean engineering table 1 machine learning offshore wind farm major major minor table 4 wind turbine Copyright 2024, International Society of Offshore...

... risk management machine learning intensity artificial intelligence module risk and uncertainty assessment subsea system fpso thermal radiation intensity thermal radiation equation modeling & simulation floating production systems radiation intensity personnel numerical...

...Proceedings of the Thirty-fourth (2024) International Ocean and Polar Engineering Conference Rhodes, Greece, June 16 21, 2024 Copyright © 2024 by the International Society of Offshore and Polar Engineers (ISOPE) ISBN 978-1-880653-78-4; ISSN 1098-6189 www.isope.org Integrating Machine Learning...

... the operational demands on smart buoys. We utilize reinforcement learning to solve the optimization problem of communication quality in smart buoys. Reinforcement learning is a method of machine learning aimed at learning optimal strategies through interaction with the environment to maximize long-term rewards...

... ABSTRACT A framework is introduced to compare fuel efficiencies of each route and calculate fuel savings by optimizing routes and operational strategies. By integrating mathematical models and machine learning techniques, the study standardizes various operational conditions for accurate fuel...

... trajectory tracking control of unmanned ships, and used DDPG to adaptively adjust the control parameters of the controller (Zheng, YM, Tao, J, and Hartikainen, J, 2023). artificial intelligence trajectory controller ship identification machine learning algorithm estimator coefficient voyage...

... is reduced, and the operation is carried out in a small waterplane mode, which cleverly balances the requirement of fast speed under low sea conditions and the requirement of seakeeping comfort under high sea conditions. geologist machine learning engineering waterline artificial intelligence...

... deterioration of the main engine and propulsion system due to aging. resistance performance accuracy onboard monitoring data deterioration equation hull fouling machine learning fouling formula artificial intelligence propulsive performance ship propulsive performance constraint deviation...