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Keywords: neural network
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Proceedings Papers

Paper presented at the SNAME Maritime Convention, September 27–29, 2023
Paper Number: SNAME-SMC-2023-019
... the system’s effectiveness. To investigate a possible improvement to modern systems, an artificial neural network-based controller, trained by potential flow simulations, was used to prescribe the optimum future actuation of a ride control system. By prescribing the optimum future actuation in anticipation...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, September 27–29, 2022
Paper Number: SNAME-SMC-2022-074
... The main purpose of this study is to apply a Deep Neural Network (DNN) method to linear systems and to predict in a relatively short time span the ultimate vertical bending moment (VBM) for damaged ships. A Deep Neural Network approach, which is composed of multiple fully connected layers...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, September 27–29, 2022
Paper Number: SNAME-SMC-2022-065
... is on building and testing machine learning models that can accurately predict the shaft power of a vessel under different conditions. The models examined include pure empirical models, pure neural network models, and combinations of the two. Using data on two car carrying vessels for 8 years it was found...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, October 27–29, 2021
Paper Number: SNAME-SMC-2021-041
.... However, current work observation requires both time and labor, and in some cases, shipyards hesitate to implement work observation. The aim of this study is to develop a methodology that uses deep neural networks to reduce the disadvantages of current work observation approaches while identifying work...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, October 27–29, 2021
Paper Number: SNAME-SMC-2021-048
... Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, September 29–October 2, 2020
Paper Number: SNAME-SMC-2020-035
... machine learning freight & logistics services upstream oil & gas energy conservation renewable energy climate change artificial intelligence marine transportation reinforcement learning neural network regression sustainability smc2020-a virtual event application fuel...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, September 29–October 2, 2020
Paper Number: SNAME-SMC-2020-098
...-of-the-art in machine learning techniques with the increasingly available ship design data in order to improve the hull form design process. artificial intelligence design space optimal hull form neural network training data iteration machine learning node design variable geometry ship design...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, October 24–27, 2018
Paper Number: SNAME-SMC-2018-060
... work observation. The aim of this study is to develop a new work observation method by use of deep neural networks to reduce the disadvantages of current work observation approaches while improving the accuracy of work identification. dnn work observation air chipper identification ability...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, November 8, 2013
Paper Number: SNAME-SMC-2013-P48
..., which can be adapted for shipbuilding. This technology is the key to realizing the cost reduction and productivity found in industries, such as automobile manufacturing, for shipbuilding. neural network artificial intelligence marine transportation criteria us government renewable energy...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, October 19–21, 2005
Paper Number: SNAME-SMC-2005-P30
... the capability for automation of the process and the ability to go directly from CAD to formed part. A sophisticated controller which uses an embedded neural network system, developed by N. A. Tech., and a precision measurement system enabling feedback control, has shown the ability to form parts with a high...
Proceedings Papers

Paper presented at the SNAME Maritime Convention, October 19–21, 2005
Paper Number: SNAME-SMC-2005-P19
... upstream oil & gas characteristic application neural network simulation module virtual reality welder training author weld bead human computer interaction simulator welding torch gdeb endeavr software framework end effector hapticmaster welding parameter Based...

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