Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/77790
Title: Using a Deep Learning Algorithm to Improve the Results Obtained in the Recognition of Vessels Size and Trajectory Patterns in Shallow Areas Based on Magnetic Field Measurements Using Fluxgate Sensors
Authors: Perez, Marina
Parras, Juan
Zazo, Santiago
Pérez Álvarez, Iván Alejandro 
Sanz Lluch, María del Mar
UNESCO Clasification: 3308 Ingeniería y tecnología del medio ambiente
Keywords: Magnetic sensors
Fluxgate
Magnetic simulations
Vessels
Deep learning, et al
Issue Date: 2021
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: Safety in coastal areas such as beaches, ports, pontoons, etc., is a current problem with a difficult solution and on which many organizations are putting efforts in terms of technological innovation. In this work the design of a possible solution based on magnetic sensors is presented. First, a study has been made of the type of sensors that best suit the application based on parameters such as sensitivity, the allowed bandwidth of excitation, price or physical construction. Then the system of excitation of the sensors and signal measurement is presented. To justify the design, a series of simulations of magnetic field variations have been carried out in the presence of large objects of conductive material, in the vicinity of the measuring points. With these data a mathematical model has been established that allows the identification of the dimensions and position of the object through triangulation and knowing only the data of the magnetic field. It was found that although this method seems quite effective, it has a significant error, so another method based on neural networks was developed also using data from the simulations. This method seems to yield much better and more reliable results
URI: http://hdl.handle.net/10553/77790
ISSN: 1524-9050
DOI: 10.1109/TITS.2020.3036906
Source: IEEE Transactions on Intelligent Transportation Systems [ISSN 1524-9050], 26 enero 2021
Appears in Collections:Artículo preliminar
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