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Application perspective of digitalneural networks in the context of marine technologies

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study is focused on the issue of digital neural networks’ implementation in the context of maritime industry. Various algorithms of such networks in the terms of the marine technologies have been reviewed in the current study in order to evaluate the effectiveness of the methodology and to propose a new concept of an artificial neural network’s application in this way. Fire-detection system simulation based on the thermal imagers’ data input had been developed to assess the efficiency of the concept suggested with a multi-layer perceptron (MLP) algorithm integrated into the designed 3d-model.
Twórcy
autor
  • Odesa National Maritime University, Odessa, Ukraine
autor
  • National University “Odessa Maritime Academy”, Odessa, Ukraine
Bibliografia
  • 1. Abdelali K, Yousra M, Khalifa M, Mostafa R. Artificial neural network and mathematical modeling of automatic ship berthing, Commun. Math. Biol. Neurosci. 2022; Article ID 113. https://doi.org/10.28919/cmbn/7727.
  • 2. Abramowski T. Application of artificial neural networks to assessment of ship manoeuvrability qualities. Polish Maritime Research. 2008; 15(2) 1521. https://doi.org/10.2478/v10012-007-0059-0.
  • 3. Ahmed Y. A., Hasegawa K. Automatic Ship Berthing using Artificial Neural Network Based on Virtual Window Concept in Wind Condition. IFAC Proceedings Volumes. 2012; 45(24), 286–291. https://doi.org/10.3182/20120912-3-bg-2031.00059.
  • 4. Ahmed Y. A., Hasegawa K. Automatic ship berthing using artificial neural network trained by consistent teaching data using nonlinear pro-gramming method. Engineering Applications of Artificial Intelligence. 2013; 26(10), 2287–2304. https://doi.org/10.1016/j.engappai.2013.08.009.
  • 5. Ahmed Y. A., Hasegawa K. Implementation of Automatic Ship Berthing using Artificial Neural Network for Free Running Experiment. IFAC Proceedings Volumes. 2013; 46(33), 25–30. https://doi.org/10.3182/20130918-4-jp-022.00036.
  • 6. Ahmed Y. A., Hasegawa K. Consistently Trained Artificial Neural Network for Automat-ic Ship Berthing Control. TransNav, the Interna-tional Journal on Marine Navigation and Safety of Sea Transportation. 2015; 9(3), 417–426. https://doi.org/10.12716/1001.09.03.15.
  • 7. Ahmed Y. A., Hannan M. A., Siang K. H. Arti-ficial Neural Network controller for automatic ship berthing: challenges and opportunities. Ma-rine Systems & Ocean Technology. 2020; 15(4), 217–242. https://doi.org/10.1007/s40868-020-00089-x.
  • 8. Haykin Simon, “Neural networks and learning machines,” —3rd ed, Rev. ed of: Neural net-works. 2nd ed., 1999. Includes bibliographical references and index. ISBN-13: 978-0-13-147139-9 ISBN-10: 0-13-147139-2.
  • 9. Kanghyeok L., Minwoong C., Seungjun K., Do H. S. Damage detection of catenary mooring line based on recurrent neural networks. Ocean Engineering. 2021; 227, 108898, ISSN 0029-8018. https://doi.org/10.1016/j.oceaneng.2021.108898.
  • 10. Kuo H. C., Chang, H. K. A real-time shipboard fire-detection system based on grey-fuzzy algorithms. Fire Safety Journal. 2003; 38(4), 341–363. https://doi.org/10.1016/s0379- 7112(02)00088-7.
  • 11. Li G, Kawan B, Wang H, Zhang H. Neural-network-based modelling and analysis for time series prediction of ship motion. Ship Technolo-gy Research. 2017; 64(1), 3039. https://doi.org/10.1080/09377255.2017.1309786.
  • 12. Minwoong C., Seungjun K., Kanghyeok L., Do H.S. Detection of damaged mooring line based on deep neural networks. Ocean Engineering. 2020; 209, 107522, ISSN 0029-8018. https://doi.org/10.1016/j.oceaneng.2020.10752.
  • 13. Mizuno N., Kuboshima R. Implementation and Evaluation of Non-linear Optimal Feedback Control for Ship’s Automatic Berthing by Re-current Neural Network. IFAC-PapersOnLine. 2019; 52(21), 91–96. https://doi.org/10.1016/j.ifacol.2019.12.289.
  • 14. Nazir A, Mosleh H, Takruri M, Jallad A-H, Alhebsi H. Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Anal-ysis. Fire. 2022; 5(1):11. https://doi.org/10.3390/fire5010011.
  • 15. Neumann, T. (2017). Fuzzy routing algorithm in telematics transportation systems. Communications in Computer and Information Science. 715, 494-505 doi:10.1007/978-3-319-66251-0_40.
  • 16. Qiang L., Bi-Guang H. Artificial Neural Net-work Controller for Automatic Ship Berthing Using Separate Route. Journal of Web Engineer-ing. 2020; https://doi.org/10.13052/jwe1540-9589.19788.
  • 17. Qiang Z., Guibing Z., Xin H., Renming Y. Adaptive neural network auto-berthing control of marine ships. Ocean Engineering. 2019; 177, 40–48. https://doi.org/10.1016/j.oceaneng.2019.02.031.
  • 18. Starnenkovich M. An application of artificial neural networks for autonomous ship navigation through a channel. Vehicle Navigation and In-formation Systems Conference. 1991; https://doi.org/10.1109/vnis.1991.205794.
  • 19. Xavier K.L.B.L., Nanayakkara V.K. Develop-ment of an Early Fire Detection Technique Us-ing a Passive Infrared Sensor and Deep Neural Networks. Fire Technol. 2022; 58, 3529–3552. https://doi.org/10.1007/s10694-022-01319-x.
  • 20. Xiao Perry, “Practical Java Programming”. Indi-anapolis, IN: John Wiley & Sons, Inc., 2019, ISBN: 978-1-119-56001-2.
  • 21. Yang C-H, Lin G-C, Wu C-H, Liu Y-H, Wang Y-C, Chen K-C. Deep Learning for Vessel Tra-jectory Prediction Using Clustered AIS Data. Mathematics. 2022; 10(16):2936. https://doi.org/10.3390/math10162936.
  • 22. Zăgan R., Chiţu M. G., Manea E. Ship Manoeu-vrability Prediction Using Neural Networks Analysis. Advanced Materials Research. 2014; 1036, 946–951. https://doi.org/10.4028/www.scientific.net/amr.1036.946.
  • 23. Zhang Q., Jiang N., Hu Y., Pan D. Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks. Interna-tional Journal of E-Navigation and Maritime Economy. 2017; 7, 34–41. https://doi.org/10.1016/j.enavi.2017.06.004.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-34cf75bf-fc10-432c-aada-620658292a7e
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