Mühendislik-Mimarlık Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü, Bildiri Metinleri Koleksiyonu

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https://hdl.handle.net/20.500.12402/1087

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Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine
(IEEE, 2017-11-02) Ertuğrul, Ömer Faruk; Sezgin, Necmettin; Öztekin, Abdulkerim; Tağluk, Mehmet Emin
Estimating short-term power load is a fundamental issue in the power distribution system. Since short-term power load is related to many parameters such as weather conditions, and time. The aim of this study is to determine the relevant parameters in estimating short-term power load not only in order to decrease the computational cost, but also to achieve higher success rates. Furthermore, by using selected features the required memory, equipment and communication costs are also decreased in real time applications. Feature selection by extreme learning machine method was used in determining relevant features. The short-term power loads of two houses (one of them has a power generation capability) were used in tests and achieved results showed lower error rates were obtained by using less number of features.
EMG sinyallerinin aşırı ögrenme makinesi ile sınıflandırılması
(IEEE, 2013-06-13) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin; Kaya, Yılmaz; Tekin, Ramazan; Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümü
From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.
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.
Enerji̇ i̇leti̇m hatlarında Wi̇gner Vi̇lle dağılımı, gri̇ düzey eş oluşum matri̇si̇ ve örüntü tanıma yöntemleri̇ i̇le arıza anali̇zi̇
(IEEE, 2012-05-30) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin; Kaya, Yılmaz
Artan enerji ihtiyacı, enerji iletiminin önemini artırmıştır. Enerjinin kesintisiz iletimi için arızalı iletim hattının iletim sisteminden hızla izole edilmesi gerekmektedir. Yapılan çalışmada enerji iletim hatlarında arıza ve arıza tipinin tespiti için yeni bir yöntem geliştirilmiştir. Gerçek enerji iletim hattı arıza sinyallerinin Wigner-Ville zaman frekans dağılımı elde edilmiş ve bu enerji gri düzey eş oluşum matrisi üzerine transfer edilmiştir. Bu matristen arızaya özgün birtakım özellikler çıkarılmıştır. Bu özellikler istatistiksel ve yapay zeka modelleri ile sınıflandırılarak arıza tespiti yapılmıştır. Geliştirilen yöntemin sonuçları daha önce yapılan çalışmaların sonuçları ile karşılaştırılmıştır.
Estimation of short-term power load of a small house by generalized behavioural learning method
(IEEE, 2017-06-15) Ertuğrul, Ömer Faruk; Tağluk, Mehmet Emin
Power load estimation, especially short-term power load estimation, plays an important role in the management of a power system in terms of system security and electricity costs. Therefore, estimation of short-term power load accurately is a popular research issue. In this paper, the generalized behavioral learning method (GBLM), a method developed based on human's behavioral learning theories, was employed to estimate short-term power load. The datasets that belong to houses B and C were employed in the estimation process. Achieved results by GBLM and extreme learning machine (ELM) ELM were compared. It is showed that GBLM estimates short-term power load with a higher success rate than ELM.
Hari̇ci̇ uyartı akımı ve i̇yoni̇k konsantrasyonların Hodgki̇n-Huxley si̇ni̇r modeli̇ üzeri̇ndeki̇ etki̇leri̇
(IEEE, 2012-05-30) Tekin, Ramazan; Tağluk, Mehmet Emin; Ertuğrul, Ömer Faruk
Hodgkin-Huxley (HH) nöron modeli teorik sinirbilimde çok önemli bir yere sahiptir. Çalışmada HH nöron modelini esas alınarak uyartı akım değerinin ve hücre içi ve dışı iyonik sodyum ve potasyum konsantrasyonlarının Aksiyon Potansiyeli (AP) formu üzerindeki etkileri incelenmiştir. Sodyum konsantrasyonu daha çok AP’nin genliğini etkilerken, Potasyumun AP’nin dinlenim, eşik ve hiper-polarizasyon durumunu etkilediği tespit edildi. Uyartı akım Şiddetinin artışı ise AP’nin oluşumunu daha erken tetiklediği ve bu nedenle AP/saniye sayısında artış olduğu gözlenmiştir. Bu gözlemlerin teorik nörobilimde kullanılabileceği düşünülmektedir.
İki kanal yüzey EMG işareti ile el aç/kapa ve el parmaklarının sınıflandırılması
(IEEE, 2017-11-02) Sezgin, Necmettin; Ertuğrul, Ömer Faruk; Tekin, Ramazan; Tağluk, Mehmet Emin
In this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.
Kortikal bir ağ modelinin çıkış verisindeki karmaşıklık ve uyumluluk analizi
(IEEE, 2013-06-13) Tekin, Ramazan; Tağluk, Mehmet Emin; Ertuğrul, Ömer Faruk; Sezgin, Necmettin
Depending on the complex interconnection of billions of neurons forming cortical network excitation times and the emergence of action potentials or spike trains becomes complex and irregular. The effect of various parameters such as synaptic connections, conductivity and voltage dependent channels on the output of the network has become of research issues. In this study, based on Hodgkin-Huxley neuron model an artificial cortical network that simulates a local region of cortex was designed and the effect of probabilistic values of network parameters used in this model on irregularity and complexity of the spike trains at the neurons' output were investigated. Approximation Entropy, Spectral Entropy and Magnitude Squared Coherence methods were used for irregularity analysis.

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