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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 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 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 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 Estimation of short-term power load of a small house by generalized behavioural learning method(IEEE, 2017-06-15) Ertuğrul, Ömer Faruk; Tağluk, Mehmet EminPower 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.Öğe Smart city planning by estimating energy efficiency of buildings by extreme learning machine(IEEE, 2016-06-20) Ertuğrul, Ömer Faruk; Kaya, YılmazEstimation of energy efficiency is one of the major issues in smart city planning. Although, there are some papers about estimation of energy efficiency of the buildings, there is still a requirement of an effective method that can be used in all climatic zones. Therefore, extreme learning method (ELM), which is a training method for single hidden layer neural network, was employed in the dataset that contains the properties of buildings such as shape, area and height and cooling and heating loads were calculated. Achieved results by ELM were compared with the results in the literature and the results obtained by some popular machine learning methods such as artificial neural network, linear regression, and etc. Obtained results by ELM found acceptable.Öğe Determining optimal artificial neural network training method in predicting the performance and emission parameters of a biodiesel-fueled diesel generator(International Journal of Automotive Engineering and Technologies, 2019-04-03) Altun, Şehmus; Ertuğrul, Ömer FarukArtificial neural network (ANN) methods were employed and suggested in modeling the emissions and performance of a diesel generator fueled with waste cooking oil derived biodiesel during steady-state operation. These papers are generally built on determining optimal network structure, but the modelling accuracy of an ANN is also highly dependent on employed training method. In modeling, operating conditions and fuel blend ratio were used as the inputs while the performance and emission parameters were the outputs. The modeling results obtained by conventional ANNs that were trained by back propagation (BP) learning algorithm, radial basis function (RBF), and extreme learning machine (ELM) were compared with experimental results and each other. The accuracy of the estimations by ELM was above 95% for all the output parameters except for specific fuel consumption and thermal efficiency. Moreover, ELM performed better than BP and RBF with lower mean relative error (MRE) in case where the emissions were estimated. The ELM provided correlation coefficients of 0.987, 0.950 and 0.996 for unburned hydrocarbons (HCs), nitrogen oxides (NOx) and smoke opacity (SO), respectively, while for BP, they were 0.973, 0.818, 0.993, and for RBF, 0.975, 0.640 and 0.981. The most suitable training function for each emission and performance parameters of diesel generator was determined based on obtained accuracies.
