G
George Georgoulas
Researcher at Luleå University of Technology
Publications - 110
Citations - 2186
George Georgoulas is an academic researcher from Luleå University of Technology. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 21, co-authored 109 publications receiving 1821 citations. Previous affiliations of George Georgoulas include University of Patras & University of Ioannina.
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Journal ArticleDOI
Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression
TL;DR: A data-driven approach for the remaining useful life (RUL) estimation of rolling element bearings based on ε-Support Vector Regression, with Wiener entropy utilized for the first time in the condition monitoring of rolling bearings.
Journal ArticleDOI
Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines
TL;DR: This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis, constituting a promising new automatic methodology for the prediction of metabolicacidosis.
Journal ArticleDOI
Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition
TL;DR: In this article, an integrated anomaly detection approach for seeded bearing faults is presented, in which the Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set.
Proceedings ArticleDOI
Improving EMG based classification of basic hand movements using EMD
TL;DR: The results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.
Journal ArticleDOI
Using nonlinear features for fetal heart rate classification
Jiří Spilka,Vaclav Chudacek,Michal Koucký,Lenka Lhotska,Michal Huptych,Petr Janků,George Georgoulas,Chrysostomos D. Stylios +7 more
TL;DR: It is proven that the addition of nonlinear features improves accuracy of classification and the process of FHR evaluation can become more objective and may enable clinicians to focus on additional non-cardiotocography parameters influencing the fetus during delivery.