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Design of an AI-Based Workflow-Guiding System for Stratified Sampling

  • G. HernándezEmail author
  • D. García-Retuerta
  • P. Chamoso
  • A. Rivas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1006)

Abstract

The characterization of the resistance of transmission towers is a difficult and costly procedure which can be mitigated using statistical techniques. A stratified sampling process based on the characteristic of the terrain was shown in previous works to reduce the error in the statistical inference; however, such characteristics are usually unknown before a measure is made. In this work, we present a system which integrates artificial intelligence techniques, such as k-nearest neighbors, decision trees, or random forests, to automatically optimize the workflow of expert workers using various sources of data.

Keywords

Ambient artificial intelligence Statistical sampling Transmission towers 

Notes

Acknowledgments

This research has been partially supported by the European Regional Development Fund (FEDER) within the framework of the Interreg program V-A Spain-Portugal 2014–2020 (PocTep) under the IOTEC project grant 0123 IOTEC 3 E.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • G. Hernández
    • 1
    Email author
  • D. García-Retuerta
    • 1
  • P. Chamoso
    • 1
  • A. Rivas
    • 1
  1. 1.Bisite Research GroupUniversidad de SalamancaSalamancaSpain

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