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Analysis of institutional authors

Perez-Castan, Javier ACorresponding AuthorPerez Sanz, LuisAuthor

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October 24, 2022
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Learning Assurance Analysis for Further Certification Process of Machine Learning Techniques: Case-Study Air Traffic Conflict Detection Predictor

Publicated to: SENSORS. 22 (19): 7680- - 2022-10-01 22(19), DOI: 10.3390/s22197680

Authors:

Perez-Castan, Javier A; Perez Sanz, Luis; Fernandez-Castellano, Marta; Radisic, Tomislav; Samardzic, Kristina; Tukaric, Ivan
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Affiliations

Univ Politecn Madrid, ETSI Aeronaut & Espacio, Plaza Cardenal Cisneros, Madrid 28008, Spain - Author
Univ Zagreb, Fac Transport & Traff Sci, Zagreb 10000, Croatia - Author

Abstract

Designing and developing artificial intelligence (AI)-based systems that can be trusted justifiably is one of the main issues aviation must face in the coming years. European Union Aviation Safety Agency (EASA) has developed a user guide that could be potentially transformed as means of compliance for future AI-based regulation. Designers and developers must understand how the learning assurance process of any machine learning (ML) model impacts trust. ML is a narrow branch of AI that uses statistical models to perform predictions. This work deals with the learning assurance process for ML-based systems in the field of air traffic control. A conflict detection tool has been developed to identify separation infringements among aircraft pairs, and the ML algorithm used for classification and regression was extreme gradient boosting. This paper analyses the validity and adaptability of EASA W-shaped methodology for ML-based systems. The results have identified the lack of the EASA W-shaped methodology in time-dependent analysis, by showing how time can impact ML algorithms designed in the case where no time requirements are considered. Another meaningful conclusion is, for systems that depend highly on when the prediction is made, classification and regression metrics cannot be one-size-fits-all because they vary over time.
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Keywords

Air transportAlgorithmsArtificial intelligenceAviationCertificationConflict detectionLearning assuranceMachine learningTrustworthiness

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal SENSORS due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Instrumentation.

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 2026-04-28:

  • Google Scholar: 5
  • WoS: 5
  • Scopus: 7
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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 2026-04-28:

  • 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: 34 (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:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/86533/

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: 170
  • Downloads: 57
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Croatia.

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 CASTAN, JAVIER ALBERTO) .

the author responsible for correspondence tasks has been PEREZ CASTAN, JAVIER ALBERTO.

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Awards linked to the item

This paper is part of a project that has received funding from the SESAR Joint Undertaking under grant agreement No. 892618 under European Union's Horizon 2020 research and innovation programme and from `Programa Propio de I+D+I de la Universidad Politecnica de Madrid'. Particularly, the authors would like to acknowledge the whole AISA consortium for their contribution during the project.
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