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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Pérez M.Corresponding AuthorParras J.AuthorZazo S.AuthorPerez Alvarez I.a.Author

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February 19, 2021
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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

Publicated to: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. 23 (4): 3472-3481 - 2022-04-01 23(4), DOI: 10.1109/TITS.2020.3036906

Authors:

Perez, Marina; Parras, Juan; Zazo, Santiago; Perez Alvarez, Ivan A; Sanz Lluch, Maria del Mar
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Affiliations

Escuela Tecnica Superior de Ingenieria y Sistemas de Telecomunicacion, Universidad Politecnica de Madrid - Author
Institute for Technological Development and Innovation in Communications (IDeTIC) - Author
Univ Politecn Dev Madrid, Sch Telecommun Engn, Madrid 28040, Spain - Author
Univ Politecn Madrid, Informat Proc & Telecommun Ctr IPTC, Madrid 28040, Spain - Author
Univ Politecn Palmas de Gran Canaria, Inst Technol Dev & Innovat Commun IDeTIC, Las Palmas Gran Canaria 35001, Spain - Author
Universidad Politécnica de Madrid - Author
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Abstract

IEEE 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.
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Keywords

deep learningdeep neural networksfluxgatelocalizationmagnetic coresmagnetic sensorsmagnetic simulationsmagnetoacoustic effectsnetworksneural networkspattern recognitionsensitivitysensor phenomena and characterizationsensorsvesselsDeep learningDeep neural networksFluxgateLife below waterMagnetic coresMagnetic sensorsMagnetic simulationsMagnetoacoustic effectsNeural networksPattern recognition.SensitivitySensor phenomena and characterizationSensorsUnderwaterVessels

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 4/139, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Civil. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-25:

  • WoS: 7
  • Scopus: 4
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 16 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/94325/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 24
  • Downloads: 1
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 14 - Life below water, with a probability of 42% according to the mBERT algorithm developed by Aurora University.
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PEREZ JIMENEZ, MARINA) and Last Author (Lluch MdMS).

the author responsible for correspondence tasks has been PEREZ JIMENEZ, MARINA.

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Project objectives

El presente trabajo tiene como objetivo principal mejorar el reconocimiento del tamaño y trayectoria de embarcaciones en zonas someras mediante mediciones del campo magnético con sensores fluxgate. Se pretende analizar el tipo de sensores más adecuados considerando sensibilidad, ancho de banda, precio y construcción física. Además, se busca diseñar un sistema de excitación y medición de señales para dichos sensores. Otro objetivo es establecer un modelo matemático que permita identificar dimensiones y posición de objetos conductores mediante triangulación basada en datos del campo magnético. Finalmente, se aspira a desarrollar y evaluar un método basado en redes neuronales que supere el error significativo del modelo matemático, proporcionando resultados más fiables y precisos.
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Most relevant results

El estudio presenta avances significativos en el reconocimiento del tamaño y trayectoria de embarcaciones en zonas someras mediante sensores magnéticos y algoritmos de aprendizaje profundo. Se identificaron los sensores más adecuados considerando sensibilidad, ancho de banda, precio y construcción física. Se diseñó un sistema de excitación y medición de señales para estos sensores. Se realizaron simulaciones de variaciones del campo magnético en presencia de objetos conductores grandes, permitiendo establecer un modelo matemático basado en triangulación para determinar dimensiones y posición. Finalmente, se desarrolló un método basado en redes neuronales que, utilizando datos simulados, reduce significativamente el error y mejora la fiabilidad de los resultados obtenidos.
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