Machine Learning Methods for Marine Systems

Autor:	G. T. Hummert, A. R. Keeton, J. L. Johnson
Rok vydání:	2021
Předmět:	Liquid metal Materials science Homopolar motor Nuclear engineering Alloy engineering Electric current Current (fluid) engineering.material GeneralLiterature_MISCELLANEOUS Eutectic system Power (physics) Magnetic field
Zdroj:	IOP Conference Series: Materials Science and Engineering. 1177:012002
ISSN:	1757-899X 1757-8981
DOI:	10.1088/1757-899x/1177/1/012002
Popis:	Automation plays a key role in shipping industry and aims towards minimal operating staff. However, the effective automation relies on effective controlling at various levels starting from shipbuilding to navigation. The industry is currently focussing on autonomous shipping which actually requires precise controlling. Although many conventional methods are available for control and automation with regard to automation, Artificial Intelligence Schemes (AIS) are widely attracting the maritime sector because of their benefits. The AIS along with fuzzy logic systems are offering promising results. The emerging use of AIS in a variety of maritime applications can act as a reference wpoint for new researchers. This paper aims to conduct a valid AIS study and to examine the various machine learning approaches used in various maritime applications. It is possible to achieve complete automation in the shipping industry by implementing a related technique.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ff32ac219d7803b8c8311c98930708d9 https://doi.org/10.1088/1757-899x/1177/1/012002 Zobrazit plný text záznamu