Arama Sonuçları

Listeleniyor 1 - 10 / 14
  • Öğe
    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.
  • Öğe
    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.
  • Öğe
    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.
  • Öğe
    Dalgacık dönüşümü tabanlı parmak izi tanıma
    (IEEE, 2015-06-19) Çalışkan, Abidin; Ertuğrul, Ömer Faruk
    Bir biyometrik sistem, bir bireyin sahip olduğu karakteristik veya eşsiz özniteliğe dayalı olarak otomatik tanımlamayı sağlar. Parmak izi, günümüzde birçok alanda geniş bir kullanım alanına sahip bir biyometrik sistemdir. Özellikle insan kimliğinin doğrulanması ve tespit edilmesinde kullanılan parmak izi, erişim için geleneksel olarak kullanılan yöntemlere göre daha güvenilirdir. Bu çalışmada, Gabor dalgacık dönüşümü tabanlı parmak izi tanıma sistemi gerçekleştirilmiştir. Gri seviye parmak izi imgelerinden dalgacık öznitelikleri çıkarılmıştır. Son olarak, parmak izi imgelerinin tanınmasında k en yakın komşuluk sınıflandırıcısı kullanılmıştır. Önerilen algoritma, PolyU yüksek çözünürlüklü parmak izi veri tabanı görüntüleri üzerinde test edilmiştir. Deneysel sonuçlar, önerilen yöntemin mevcut metotların doğruluğunu arttırabildiğini göstermiştir.
  • Öğe
    Recognition of daily and sports activities
    (IEEE, 201-01-24) İnanç, Nihat; Kayri, Murat; Ertuğrul, Ömer Faruk
    Since being physically inactive was reported as one of the major risk factor of mortality, classifying daily and sports activities becomes a critical task that may improve human life quality. In this paper, the daily and sports activities dataset was used in order to evaluate and validate the employed approach. In this approach, the statistical features were extracted from the histograms of the local changes in the wearable sensors logs were obtained by one-dimensional local binary patterns. Later, extracted features were classified by extreme learning machines. Results were showed that the proposed approach is enough to recognize the action type, but in order to recognize the actions, or gender, different feature extraction methods must be employed.
  • Öğe
    Randomized feed-forward artificial neural networks in estimating short-term power load of a small house: A case study
    (IEEE, 2017-11-02) Ertuğrul, Ömer Faruk; Tekin, Ramazan; Kaya, Yılmaz
    Randomized feed-forward artificial neural networks (ANNs) have been employed in various domains. This paper was written in order to assess the efficiency of the basic forms of randomized feed-forward ANNs, which are randomized weight artificial neural network, random vector functional link network, extreme learning machine, and radial bases function neural network. In order to compare these methods, a complex dataset, which is the power load of a small house dataset, was used. Obtained results showed that lower training error rates were achieved by randomized vector functional link network. On the other hand, lower test error rates were achieved by ELM. Furthermore, ELM has faster training and test stages than the other employed randomized ANNs.
  • 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.
  • Öğe
    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.
  • Öğe
    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.
  • Öğe
    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.