F
F. Sánchez Lasheras
Researcher at University of Oviedo
Publications - 43
Citations - 1231
F. Sánchez Lasheras is an academic researcher from University of Oviedo. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 17, co-authored 31 publications receiving 973 citations.
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Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability
TL;DR: Remaining useful life values have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured parameters (input variables) for aircraft engines with success.
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Battery state-of-charge estimator using the SVM technique
J.C. Álvarez Antón,P.J. García Nieto,F. J. de Cos Juez,F. Sánchez Lasheras,M. Gonzalez Vega,M.N. Roqueñí Gutiérrez +5 more
TL;DR: In this paper, the authors estimate the state-of-charge (SOC) of a high capacity LiFePO 4 battery cell from an experimental data set obtained in the University of Oviedo Battery Laboratory (UOB Lab) using support vector machine (SVM) approach.
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PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study.
TL;DR: Simulations showed that the SVM model performs better than the other models when forecasting one month ahead and also for the following seven months.
Journal ArticleDOI
Prediction of work-related accidents according to working conditions using support vector machines
A. Suárez Sánchez,P. Riesgo Fernández,F. Sánchez Lasheras,F. J. de Cos Juez,P.J. García Nieto +4 more
TL;DR: Support vector machines (SVMs), which are a kind of statistical learning methods, were applied in this research work to predict occupational accidents with success and was able to classify, according to their working conditions, those workers that have suffered a work-related accident in the last 12 months and those that have not.
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Artificial neural networks applied to cancer detection in a breast screening programme
TL;DR: A neural network based approach to breast cancer diagnosis is described; the model developed is able to determine which women are more likely to suffer from a particular kind of tumour before they undergo a mammography.