1-11 of 11
Keywords: artificial neural networks
Close
Follow your search
Access your saved searches in your account

Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A033, June 21–30, 2021
Paper No: OMAE2021-63326
...Abstract Abstract The use of an Artificial Neural Network (ANN) for detection of mooring line failure has been a growing subject of discussion over the past several years. Sidarta et al. [6, 8, 12] have presented papers on the detection of mooring line failure of a moored vessel by monitoring...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A003, June 21–30, 2021
Paper No: OMAE2021-62674
... Dynasim. floating offshore platforms artificial intelligence artificial neural networks mooring system design Proceedings of the ASME 2021 40th International Conference on Ocean, Offshore and Arctic Engineering OMAE2021 June 21-30, 2021, Virtual, Online OMAE2021-62674 NEURAL NETWORK META...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 1: Offshore Technology, V001T01A002, June 21–30, 2021
Paper No: OMAE2021-62413
... of 99.88%, 99.99% and 99.94%, respectively, for detecting changes in platform motion in near real-time, quickly signaling a possible breakage of mooring lines. mooring system failure detection floating offshore platforms artificial neural networks multilayer perceptron Proceedings of the ASME...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 2: Structures, Safety, and Reliability, V002T02A037, June 21–30, 2021
Paper No: OMAE2021-62304
... they treat constraints. Constraints using machine learning in the form of artificial neural networks are unavoidable in engineering, aimed at preventing failures and (ANN). A surrogate model is afterwards utilized in optimization meeting all sorts of requirements we put on the system. In based on ANN...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A007, June 21–30, 2021
Paper No: OMAE2021-63094
... most fluid-related issues in drilling. The review discusses various ML methods, their theory, applications, limitations, and achievements. machine learning digital twinning artificial neural networks artificial intelligence drilling fluids automation Proceedings of the ASME 2021 40th...
Proceedings Papers

Proc. ASME. OMAE2021, Volume 10: Petroleum Technology, V010T11A008, June 21–30, 2021
Paper No: OMAE2021-63653
... operations, instead of using mechanistic or empirical methods. The selected models include Artificial Neural Networks, Random Forest, and AdaBoost. The training of the models is determined using the experimental data regarding cuttings transport tests collected in the last 40 years at The University of Tulsa...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 2A: Structures, Safety, and Reliability, V02AT02A063, August 3–7, 2020
Paper No: OMAE2020-18868
...Abstract Abstract Nowadays, artificial intelligence algorithms are regaining visibility mainly due to the increase in computational capability. Among those, artificial neural networks (ANN) are very useful for the regression of highly nonlinear phenomena, such as the dynamic response...
Proceedings Papers

Proc. ASME. OMAE2020, Volume 6B: Ocean Engineering, V06BT06A019, August 3–7, 2020
Paper No: OMAE2020-18967
...ACTIVE ABSORPTION OF RANDOM WAVES IN WAVE FLUME USING ARTIFICIAL NEURAL NETWORKS Áureo I. W. Ramos COPPE/UFRJ Rio de Janeiro, RJ, Brazil Antonio C. Fernandes COPPE/UFRJ Rio de Janeiro, RJ, Brazil Vanessa M. Thomaz CT/UFRJ Rio de Janeiro, RJ, Brazil ABSTRACT A wave flume is primarily intended...
Proceedings Papers

Proc. ASME. OMAE2007, Volume 4: Materials Technology; Ocean Engineering, 401-409, June 10–15, 2007
Paper No: OMAE2007-29171
... 22 05 2009 A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height ( Hs ). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called...
Proceedings Papers

Proc. ASME. OMAE2003, Volume 1: Offshore Technology; Ocean Space Utilization, 275-284, June 8–13, 2003
Paper No: OMAE2003-37148
... not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem...
Proceedings Papers

Proc. ASME. OMAE2004, 23rd International Conference on Offshore Mechanics and Arctic Engineering, Volume 2, 703-710, June 20–25, 2004
Paper No: OMAE2004-51065
... on LEFM have been proposed in this regard. Each of them uses different methods for estimating Stress Intensity Modification Factor (Y). In this research two types of Artificial Neural Networks (ANN) are trained for predicting the Y factor: Radial Basis Function (RBF) and Multi Layer Perceptron...