... ABSTRACT This paper focuses on the conceptualization and implementation of a Machine Learning (ML) algorithm for the temperature control of a cryogenic hydrogen cooling system. Cryogenic hydrogen will be used as energy carrier for mobility applications such as maritime vessels or cargo...

... ABSTRACT This study employs machine learning algorithms and statistical methods (including cosine similarity, Pearson correlation, Mann-Whitney U, and Kolmogorov-Smirnov tests) to investigate suitable promoters for CO 2 sequestration in marine hydrate reservoirs. Machine learning algorithms...

... experiment engineering wave probe machine learning controller flume control system wave flume wave paddle control system design Proceedings of the Fifteenth (2024) ISOPE Pacific-Asia Offshore Mechanics Symposium Chennai, India, October 13-16, 2024 Copyright © 2024 by the International Society...

... for its non-linear characteristics is critical for estimating the wetted surface area, forces, and moments. This study proposes a machine-learning approach to map wave profiles in space and time, accounting for non-linear characteristics, which can later be used to quickly compute water particle...

... existing wake models are primarily suited for fixed wind turbines, while the motion of the FOWT platform can greatly affect its wake characteristics. Therefore, it is necessary to propose new wake models specifically for floating wind turbines. This study employs a machine learning-based symbolic...

... images were collected from the Indian Ocean PMN site and segmentation algorithms were used to determine nodule abundance, coverage and size. This article presents a machine-learning-based model using a Random Forest classifier to perform segmentation by utilizing labelled images and various filter...

..., such as rapid iceberg profiling systems, improved physical and machine learning (ML) models of iceberg drift, and the rapid high-impact test apparatus (RHITA) are discussed in the context of how such technologies may be applied to enhance ongoing and future marine operations in Antarctic waters. INTRODUCTION...

... deep learning test 1 mlp forecasted sway neural network forecasted sway line failure displacement subsea system frequency motion environmental direction renewable energy rmse ratio machine learning mooring system time series artificial neural network test 2 line break sea state...

... the water-seabed-pipeline interaction under wave and current loading system, and the process was fully coupled. saturation machine learning soil displacement engineering vicinity analytical solution offshore pipeline pore pressure liu trench layer ocean engineering equation boundary...

... ABSTRACT This paper describes an original approach to reduce drastically the analysis time needed for fatigue design of structures by using machine learning techniques. The approach is applied to the spectral fatigue analysis of various structural details on a converted FPSO hull, where design...

... in port infrastructure and moored vessels. Thus a reliable estimation of wave overtopping rate is essential to reduce the risk associated with the failure of the breakwaters. janssen berm breakwater machine learning breakwater water depth artificial intelligence mass-armoured berm breakwater...

... Station. parameter estimation method different return period robustness artificial intelligence gev distribution machine learning cmmethod confidence interval lmmethod precipitation return period quantile monte carlo simulation taihu basin china unbiasedness risk management...

... entropy distribution ice thickness artificial intelligence confidence interval liaodong bay weibull distribution calculation gumbel distribution sea ice thickness pearson type-3 distribution machine learning thickness lognormal distribution return period fitting curve return value design...

.... frequency corresponding wind speed return period wave height probability distribution copula function gumbel distribution marginal distribution university wind speed joint distribution artificial intelligence upstream oil & gas machine learning annual maximum wave height distribution...

...-submersible platform. calculation accuracy wave parameter upstream oil & gas fatigue damage simulated result significant wave height wave scatter dnv code sea state artificial intelligence edm riser machine learning approximation analysis method fatigue damage assessment spectral...

... Abstract Gaussian process regression (GPR) is a new machine learning method by the context of Bayesian theory and statistical learning theory. As a nonparametric method, GPR can adapt its complexity flexibly to the complex data distributions in the real-world, without any heuristics or manual...

... correlation selection correlation analysis statistics site pair correlation coefficient wallis pearson correlation coefficient sensitivity test homogeneous region regional frequency analysis taihu basin correlation frequency analysis water resource management machine learning Correlation...

... of the sea area in the Gulf of Bothnia since 1979 till the present time with subsequent statistical data processing ( http://en.ilmatieteenlaitos.fi ). bothnia investigation probability machine learning ice condition gulf raahe lighthouse probabilistic model ice concentration ice thickness...

.... (2012c). climate change seasonal component oscillate ocean wave climate hierarchical space-time model model component spatial field co 2 credible interval artificial intelligence contribution projection ocean area long-term trend machine learning significant wave height future...

... for the analysis. yellow river reservoir rainfall runoff delta discharge yellow river estuary artificial intelligence sediment diversion sediment discharge water diversion linear trend sediment confidence level khistofrov diversion kendall method time series analysis machine learning...