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Unsupervised activity recognition for autonomous water drones

Published: 09 April 2018 Publication History

Abstract

We propose an automatic system aimed at discovering relevant activities for aquatic drones employed in water monitoring applications. The methodology exploits unsupervised time series segmentation to pursue two main goals: i) to support on-line decision making of drones and operators, ii) to support off-line analysis of large datasets collected by drones. The main novelty of our approach consists of its unsupervised nature, which enables to analyze unlabeled data. We investigate different variants of the proposed approach and validate them using an annotated dataset having labels for activity "upstream/downstream navigation". Obtained results are encouraging in terms of clustering purity and silhouette which reach values greater than 0.94 and 0.20, respectively, in the best models.

References

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R. Barták and M. Vomlelová. 2017. Using Machine Learning to Identify Activities of a Flying Drone from Sensor Readings. In Proceedings of FLAIRS 2017. AAAI Press, 436--441. https://aaai.org/ocs/index.php/FLAIRS/FLAIRS17/paper/view/15488
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Cited By

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  • (2020)Time series segmentation for state-model generation of autonomous aquatic drones: A systematic frameworkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2020.10349990(103499)Online publication date: Apr-2020
  • (2020)Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case StudyMachine Learning, Optimization, and Data Science10.1007/978-3-030-37599-7_46(553-565)Online publication date: 3-Jan-2020
  • (2019)eXplainable Modeling (XM)Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3332106(2342-2344)Online publication date: 8-May-2019
  • Show More Cited By

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Published In

cover image ACM Conferences
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing
April 2018
2327 pages
ISBN:9781450351911
DOI:10.1145/3167132
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

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Author Tags

  1. activity recognition
  2. autonomous drone
  3. clustering
  4. segmentation
  5. time series analysis
  6. unsupervised learning
  7. water monitoring

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  • Poster

Funding Sources

  • Executive Agency for Small and Medium-sized Enterprises (EASME), European Union's Horizon 2020

Conference

SAC 2018
Sponsor:
SAC 2018: Symposium on Applied Computing
April 9 - 13, 2018
Pau, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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Cited By

View all
  • (2020)Time series segmentation for state-model generation of autonomous aquatic drones: A systematic frameworkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2020.10349990(103499)Online publication date: Apr-2020
  • (2020)Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case StudyMachine Learning, Optimization, and Data Science10.1007/978-3-030-37599-7_46(553-565)Online publication date: 3-Jan-2020
  • (2019)eXplainable Modeling (XM)Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3332106(2342-2344)Online publication date: 8-May-2019
  • (2019)Subspace clustering for situation assessment in aquatic dronesProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297372(930-937)Online publication date: 8-Apr-2019
  • (2019)Recognition of Drone Formation Intentions Using Supervised Machine Learning2019 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI49370.2019.00079(408-411)Online publication date: Dec-2019
  • (2019)Subspace Clustering for Situation Assessment in Aquatic Drones: A Sensitivity Analysis for State-Model ImprovementCybernetics and Systems10.1080/01969722.2019.167733350:8(658-671)Online publication date: 7-Nov-2019
  • (2019)Bayesian Clustering of Multivariate Immunological DataMachine Learning, Optimization, and Data Science10.1007/978-3-030-13709-0_43(506-519)Online publication date: 14-Feb-2019

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