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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2022: 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) pp 166–175Cite as

Compression of Clustered Ship Trajectories for Context Learning and Anomaly Detection

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

Abstract

This paper presents a context information extraction process over Automatic Identification System (AIS) real world ship data, building a system with the capability to extract representative points of a trajectory cluster. With the trajectory cluster, the study proposes the use of trajectory segmentation algorithms to extract representative points of each trajectory and then use the K-means algorithm to obtain a series of centroids over all the representative points. These centroids combined, form a new representative trajectory of the cluster. The results show a suitable approach with several compression algorithms that are compared with a metric based on the Perpendicular Euclidean Distance.

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Acknowledgement

This research was partially funded by public research projects of Spanish Ministry of Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/https://doi.org/10.13039/501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17.

Author information

Authors and Affiliations

  1. GIAA, University Carlos III of Madrid, Madrid, Spain

    David Sánchez Pedroche, Jesús García & José Manuel Molina

Authors
  1. David Sánchez Pedroche
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  2. Jesús García
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  3. José Manuel Molina
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Corresponding author

Correspondence to David Sánchez Pedroche .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Sánchez Pedroche, D., García, J., Molina, J.M. (2023). Compression of Clustered Ship Trajectories for Context Learning and Anomaly Detection. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_16

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