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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 EminEstimating 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 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 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.Öğe Horlama işaretlerinden uyku apnesi teşhisinde yeni bir yaklaşım: Üçlü desen yöntemi(IEEE, 2017-11-02) Kaya, Yılmaz; Sezgin, Necmettin; Ertuğrul, Ömer FarukBu çalışmada, Tıkayıcı Uyku Apnesi Sendromu (TUAS) kestirimi için yeni bir yaklaşım önerilmektedir. TUAS, insan hayat konforunu etkileyen son derece ciddi bir uyku hastalığıdır. TUAS’ın teşhisi genellikle pahalı cihazlar ile uzman hekimler tarafından yapılmaktadır. Dolayısıyla TUAS gibi ciddi bir hastalığın daha erken teşhis edilerek tedavi edilmesi gerekmektedir. Bu çalışmada horlama işaretlerinden TUAS teşhisi için yeni bir öznitelik çıkarım yöntemi önerilmiştir. Bir (1) boyutlu üçlü desen yöntemi ile ham horlama işaretlerinden etkili öznitelikler çıkarılarak sınıflandırma yöntemleri ile teşhis işlemi gerçekleştirilmiştir. Elde edilen sonuçlara göre 1B-ÜD yönteminin horlama işaretlerinden TUAS teşhisinde önemli başarı sağladığı görülmüştür. Bu çalışmada kullanılan yöntemin, uyku laboratuvarlarında kullanılabilir olması ile hastayı Polisomnografi (PSG) cihazı ile gece boyunca teste tutmadan önce uzman hekimlere bir ön test yapma şansı verebileceği düşünülmektedir.Öğe A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure(TÜBİTAK, 2018-09-28) Ertuğrul, Ömer Faruk; Sezgin, NecmettinArterial blood pressure (ABP) is one of the most vital signs in the prophylaxis and treatment of blood pressure-related diseases because raised blood pressure is the most significant cause of death and the second major cause of disability in the world. Higher ABP yields greater strain on arteries and these extra strains turn arteries into thicker, less flexible, and more narrow structures. This increases the possibility of having an artery busting or artery occlusion, which are the primary reasons for heart attacks, kidney disease, or strokes. In addition to its importance in monitoring cardiovascular homeostasis, measurement of ABP is imperative in surgical operations. In this study, a simple and effective approach was proposed to estimate ABP from electrocardiogram (ECG) and photoplethysmograph (PPG) signals by an extreme learning machine (ELM) and statistical properties of the ECG and/or PPG signals in the time-frequency domain. To evaluate and apply the proposed approach, the Cuffless Blood Pressure Estimation Dataset, which was published and shared by UCI, was employed. First, the statistical properties were extracted from ECG and PPG signals that were in the time-frequency domain. Later, extracted features were employed to estimate cuffless ABP for each subject by the ELM and some popular machine learning methods. Achieved results and reported results in the literature showed that the proposed approach can be successfully employed for estimating cuffless blood pressure (BP) from ECGs and/or PPGs. Additionally, with the proposed approach, the systolic BP, mean BP, and diastolic BP can be calculated simultaneously.
