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Pregled bibliografske jedinice broj: 877707

Application of a pattern recognition method to estimate wind loads on ships and marine objects = Anwendung eines Erkennungsmodells zur Abschätzung von Windlasten auf Schiffe und Marineobjekte


Valčić, Marko; Prpić-Oršić, Jasna; Vučinić, Dean
Application of a pattern recognition method to estimate wind loads on ships and marine objects = Anwendung eines Erkennungsmodells zur Abschätzung von Windlasten auf Schiffe und Marineobjekte // Materialwissenschaft und Werkstofftechnik, 48 (2017), 5; 387-399 doi:10.1002/mawe.201700009 (međunarodna recenzija, članak, znanstveni)


Naslov
Application of a pattern recognition method to estimate wind loads on ships and marine objects = Anwendung eines Erkennungsmodells zur Abschätzung von Windlasten auf Schiffe und Marineobjekte
(Application of a pattern recognition method to estimate wind loads on ships and marine objects)

Autori
Valčić, Marko ; Prpić-Oršić, Jasna ; Vučinić, Dean

Izvornik
Materialwissenschaft und Werkstofftechnik (0933-5137) 48 (2017), 5; 387-399

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Wind loads, Pattern recognition, Elliptic Fourier descriptors, Neural network

Sažetak
This paper presents an extension of the application capabilities of elliptic Fourier descriptors from the usual pattern recognition and classification problems to problems of very complex nonlinear multivariable approximations of multi-input and multi-output functions. Wind loads on ships and marine objects are a complicated phenomenon because of the complex configuration of the above-water part of the structure. The proposed approach of the wind load estimation method presented in this paper consists of four basic parts: acquisition and pre-processing of vessel images ; image editing ; data preparation for neural network training ; validating and testing of the created neural network. The method is based on elliptic Fourier features of a closed contour which are used for the frontal and lateral closed contour representation of ships. Therefore, this approach takes into account all aspects of the variability of the above-water frontal and lateral ship profile. For the purpose of multivariate nonlinear regression, the generalized regression radial basis neural network is trained by elliptic Fourier features of closed contours and wind load data derived from wind tunnel tests. The trained neural network is used for the estimation of non-dimensional wind load coefficients. The results for a group of car carriers are presented and compared with the experimental data.

Izvorni jezik
Engleski

Znanstvena područja
Brodogradnja, Tehnologija prometa i transport, Temeljne tehničke znanosti

Napomena
S.I.: Advanced Computational Engineering and Experimenting (ACE-X 2016) (odabrana sudjelovanja).



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2013-11-8722 - Ekološki pristup projektiranju broda i optimalnom planiranju rute (Jasna Prpić-Oršić, )

Ustanove
Tehnički fakultet, Rijeka,
Pomorski fakultet, Rijeka

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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