Application Perspective of Digital Neural Networks in the Context of Marine Technologies

ABSTRACT: 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.

KEYWORDS: Maritime Industry, Neural Networks, Artificial Neural Network (ANN), Marine Technology, Multi-Layer Perceptron (MLP), Digital Neural Networks, MLP Algorithm, 3D Model

REFERENCES

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. - doi:10.28919/cmbn/7727

Abramowski T. Application of artificial neural networks to assessment of ship manoeuvrability qualities. Polish Maritime Research. 2008; 15(2) 1521. - doi:10.2478/v10012-007-0059-0

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. - doi:10.3182/20120912-3-BG-2031.00059

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. - doi:10.1016/j.engappai.2013.08.009

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. - doi:10.3182/20130918-4-JP-3022.00036

Ahmed Y.A., Hasegawa K.: Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, No. 3, doi:10.12716/1001.09.03.15, pp. 417-426, 2015

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. - doi:10.1007/s40868-020-00089-x

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

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. - doi:10.1016/j.oceaneng.2021.108898

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. - doi:10.1016/S0379-7112(02)00088-7

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. - doi:10.1080/09377255.2017.1309786

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. - doi:10.1016/j.oceaneng.2020.107522

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. - doi:10.1016/j.ifacol.2019.12.289

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. - doi:10.3390/fire5010011

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 - doi:10.1007/978-3-319-66251-0_40

Qiang L., Bi-Guang H. Artificial Neural Net-work Controller for Automatic Ship Berthing Using Separate Route. Journal of Web Engineer-ing. 2020; - doi:10.13052/jwe1540-9589.19788

Qiang Z., Guibing Z., Xin H., Renming Y. Adaptive neural network auto-berthing control of marine ships. Ocean Engineering. 2019; 177, 40–48. - doi:10.1016/j.oceaneng.2019.02.031

Starnenkovich M. An application of artificial neural networks for autonomous ship navigation through a channel. Vehicle Navigation and In-formation Systems Conference. 1991; - doi:10.1109/VNIS.1991.205794

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. - doi:10.1007/s10694-022-01319-x

Xiao Perry, “Practical Java Programming”. Indi-anapolis, IN: John Wiley & Sons, Inc., 2019, ISBN: 978-1-119-56001-2.

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. - doi:10.3390/math10162936

Zăgan R., Chiţu M. G., Manea E. Ship Manoeu-vrability Prediction Using Neural Networks Analysis. Advanced Materials Research. 2014; 1036, 946–951. - doi:10.4028/www.scientific.net/AMR.1036.946

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. - doi:10.1016/j.enavi.2017.06.004

Citation note:

Konon V., Konon N.: Application Perspective of Digital Neural Networks in the Context of Marine Technologies. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 16, No. 4, doi:10.12716/1001.16.04.16, pp. 743-747, 2022

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