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.
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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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Hernández, G., García-Retuerta, D., Chamoso, P., Rivas, A. (2020). Design of an AI-Based Workflow-Guiding System for Stratified Sampling. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds) Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence. ISAmI 2019. Advances in Intelligent Systems and Computing, vol 1006 . Springer, Cham. https://doi.org/10.1007/978-3-030-24097-4_13
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