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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 BölümüFrom 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, NecmettinDepending 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 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, YılmazRandomized 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 Hari̇ci̇ uyartı akımı ve i̇yoni̇k konsantrasyonların Hodgki̇n-Huxley si̇ni̇r modeli̇ üzeri̇ndeki̇ etki̇leri̇(IEEE, 2012-05-30) Tekin, Ramazan; Tağluk, Mehmet Emin; Ertuğrul, Ömer FarukHodgkin-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 EminIn 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.
