Filtreler

2013 - 20182
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İndeks: Web of Science × Konu: ELM ×

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Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Enerji iletim Hatlarında oluşan arızaların aşırı öğrenme makinesi ile tespiti
    (IEEE, 2013-06-13) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin
    With the increase of energy demand continuous energy transmission gained considerable attention. For a continuous energy transmission, the faulty power transmission line needs to be quickly isolated from the system. In this study, Extreme Learning Machine (ELM) possessing fast learning and high generalization capacity was used for this purpose and it was found as showing a good performance in detecting the faulty transmission line. In the study real fault signals recorded from transmission lines were used. A feature vector was formed from a cycle of the energy signal using relative entropy and classified via ELM. The obtained results were compared with the ones obtained through SVM, YSA, NB, J48 and PART learning techniques and the ones obtained in the previous studies. According the obtained results ELM both in terms of speed and performance was found superior.
  • Küçük Resim
    Öğe
    An efficient noisy pixels detection model for CT images using extreme learning machines
    (Sveuciliste Josipa Jurja Strossmayera u Osijeku, 2018-06) Çalışkan, Abidin; Çevik, Ulus
    In this study, a new and rapid hidden resource decomposition method has been proposed to determine noisy pixels by adopting the extreme learning machines (ELM) method. The goal of this method is not only to determine noisy pixels, but also to protect critical structural information that can be used for disease diagnosis. In order to facilitate the diagnosis and also the treatment of patients in medicine, two-dimensional (2-D) images were calculated tomography (CT) which is obtained using medical imaging techniques. Utilizing a large number of CT images, promising results have been obtained from these experiments. The proposed method has shown a significant improvement on mean squared error and peak signal-to-noise ratio. The experimental results indicate that the proposed method is statistically efficient, and it has a good performance with a high learning speed. In the experiments, the results demonstrated that remarkable successive rates were obtained through the ELM method.