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master's thesis
Primjena dubokog učenja u autonomnim plovilima
2021. urn:nbn:hr:187:757407
Vulić, Jure
University of Rijeka
Faculty of Maritime Studies
Department of Marine Engineering and Ship Power Systems

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Vulić, J. (2021). Primjena dubokog učenja u autonomnim plovilima (Master's thesis). Rijeka: University of Rijeka, Faculty of Maritime Studies. Retrieved from https://urn.nsk.hr/urn:nbn:hr:187:757407

Vulić, Jure. "Primjena dubokog učenja u autonomnim plovilima." Master's thesis, University of Rijeka, Faculty of Maritime Studies, 2021. https://urn.nsk.hr/urn:nbn:hr:187:757407

Vulić, Jure. "Primjena dubokog učenja u autonomnim plovilima." Master's thesis, University of Rijeka, Faculty of Maritime Studies, 2021. https://urn.nsk.hr/urn:nbn:hr:187:757407

Vulić, J. (2021). 'Primjena dubokog učenja u autonomnim plovilima', Master's thesis, University of Rijeka, Faculty of Maritime Studies, accessed 03 December 2024, https://urn.nsk.hr/urn:nbn:hr:187:757407

Vulić J. Primjena dubokog učenja u autonomnim plovilima [Master's thesis]. Rijeka: University of Rijeka, Faculty of Maritime Studies; 2021 [cited 2024 December 03] Available at: https://urn.nsk.hr/urn:nbn:hr:187:757407

J. Vulić, "Primjena dubokog učenja u autonomnim plovilima", Master's thesis, University of Rijeka, Faculty of Maritime Studies, Rijeka, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:187:757407

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Metadata
TitlePrimjena dubokog učenja u autonomnim plovilima
Title (english)Use of deep learning in autonomous vessels
AuthorJure Vulić
MentorVinko Tomas (mentor)
Committee memberNikola Tomac (predsjednik povjerenstva)
Committee memberMiroslav Bistrović (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Maritime Studies
(Department of Marine Engineering and Ship Power Systems)
Rijeka
Defense date and country2021-10-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Traffic and Transport Technology
Abstract
Primjena tehnologije dubokog učenja u navigaciji i upravljanju pokazuje obećavajuće rezultate ne samo kod cestovnih i zračnih već i kod morskih vozila. Iako prekomorska plovidba postavlja jedinstvene zahtjeve sigurnosti prometa i robe postoji velika potreba za optimizacijom procesa koji se odvijaju na brodu uz smanjenje ljudskih resursa. S obzirom da ljudska pogreška pri odlučivanju snosi veliki dio uzroka nesreća i kvarova, područje razvijanja pametnih tehnologija koje bi zamijenile ljudski faktor u ovu svrhu predstavlja brzorastuću disciplinu. Naravno, ovaj pristup nosi svoje mane i prednosti koje su detaljno iznesene u radu. Cilj rada je pregled aktualnih metoda dubokog učenja te opis autonomnih vozila poznatih u današnjem pomorstvu.
Abstract (english)
The application of deep learning technology in navigation and control shows promising results not only in road and air vehicles but also in sea vehicles. Although overseas travel sets unique requirements for the safety of traffic and goods, there is a great need to optimize the processes that take place onboard while reducing human resources. Given that human error in decision-making bears much of the cause of accidents and breakdowns, the area of developing smart technologies that would replace the human factor for this purpose is a rapidly growing discipline. Of course, this approach carries its disadvantages and advantages which are detailed in the paper. The aim of this paper is to review current methods of deep learning and a description of autonomous vehicles known in today's maritime industry.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:187:757407
Study programmeTitle: Marine Engineering and Maritime Transport Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka brodostrojarstva i pomorske tehnologije (magistar/magistra inženjer/inženjerka brodostrojarstva i pomorske tehnologije)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-12-17 08:15:54