Yazar "Ertugrul, Omer Faruk" seçeneğine göre listele
Listeleniyor 1 - 14 / 14
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Classification of Hand Opening/Closing and Fingers by Using Two Channel Surface EMG Signal(Ieee, 2017) Sezgin, Necmettin; Ertugrul, Omer Faruk; Tekin, Ramazan; Tagluk, 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 Complexity and Irregularity Analysis of the Output Data of a Cortical Network(Ieee, 2013) Tekin, Ramazan; Tagluk, M. Emin; Ertugrul, Omer 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 Detecting Fault Type and Fault Location in Power Transmission Lines by Extreme Learning Machines(Ieee, 2015) Tagluk, M. Emin; Mamis, Mehmet Salih; Arkan, Muslum; Ertugrul, Omer FarukImportance of supplying qualified and undisturbed electricity is increasing day by day. Therefore, detecting fault, fault type and fault location is a major issue in power transmission system in order to prevent power delivery system security. In previous studies, we observed that faults can be easily determined by extreme learning machine (ELM) and the aim of this study is to determine applicability of ELM in fault type, zone and location detection. 8 different feature sets were exacted from fault data that produced by ATP and these features were assessed by 15 different classifier and 5 different regression method. The results showed that ELM can be employed for detecting fault types and locations successfully.Öğe Determining relevant features in estimating short-term power load of a small house via feature selection by extreme learning machine(Ieee, 2017) Ertugrul, Omer Faruk; Sezgin, Necmettin; Oztekin, Abdulkerim; Tagluk, 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 Signal Classification by Extreme Learning Machine(Ieee, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, Yilmaz; Tekin, RamazanFrom 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 Estimation of Short-Term Power Load of a Small House by Generalized Behavioural Learning Method(Ieee, 2017) Ertugrul, Omer Faruk; Tagluk, 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 A fast feature selection approach based on extreme learning machine and coefficient of variation(Tubıtak scıentıfıc & technıcal research councıl turkey, ataturk bulvarı no 221, kavaklıdere, ankara, 00000, turkey, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminFeature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.Öğe Fault Detection at Power Transmission Lines by Extreme Learning Machine(Ieee, 2013) Ertugrul, Omer Faruk; Tagluk, M. Emin; Kaya, YilmazWith 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 Forecasting financial indicators by generalized behavioral learning method(Springer, 2018) Ertugrul, Omer Faruk; Tagluk, Mehmet EminForecasting financial indicators (indexes/prices) is a complex and a quite difficult issue because they depend on many factors such as political events, financial ratios, and economic variables. Also, the psychological facts or decision-making styles of investors or experts are other major reasons for this difficulty. In this study, a generalized behavioral learning method (GBLM) was employed to forecast financial indicators, which are the indexes/prices of 34 different financial indicators (24 stock indexes, 2 forexes, 3 financial futures, and 5 commodities). The achieved results were compared with the reported results in the literature and the obtained results by artificial neural network, which is widely used and suggested for forecasting financial indicators. These results showed that GBLM can be successfully employed in short-term forecasting financial indicators by detecting hidden market behavior (pattern) from their previous values. Also, the results showed that GBLM has the ability to track the fluctuation and the main trend.Öğe Forecasting Local Mean Sea Level by Generalized Behavioral Learning Method(Springer Heidelberg, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminDetermining and forecasting the local mean sea level (MSL), which is a major indicator of global warming, is an essential issue to set public policies to save our future. Owing to its importance, MSL values are measured and shared periodically by many agencies. It is not easy to model or forecast MSL because it depends on many dynamic sources such as global warming, geophysical phenomena, and circulations in the ocean and atmosphere. Several of researchers applied and recommended employing artificial neural network (ANN) in the estimation of MSL. However, ANN does not take into account the order of samples, which may consist essential information. In this study, the generalized behavioral learning method (GBLM), which is based on behavioral learning theories, was employed in order to achieve higher accuracies by using samples in the training dataset and the order of samples. To evaluate and validate GBLM, MSL of seven stations around the world was picked up. These datasets were employed to forecast the local MSL for the future. Obtained results were compared with the ones obtained by ANN that is trained by extreme learning machine and the literature. The GBLM is found to be successful in terms of the achieved high accuracies and the ability to tracking trends and fluctuations of a local MSL.Öğe A joint generalized exemplar method for classification of massive datasets(Elsevier Science Bv, 2015) Tagluk, Mehmet Emin; Ertugrul, Omer FarukDue to technological improvements, the number and volume of datasets are considerably increasing and bring about the need for additional memory and computational complexity. To work with massive datasets in an efficient way; feature selection, data reduction, rule based and exemplar based methods have been introduced. This study presents a method, which may be called joint generalized exemplar (JGE), for classification of massive data sets. This method aims to enhance the computational performance of NGE by working against nesting and overlapping of hyper-rectangles with reassessing the overlapping parts with the same procedure repeatedly and joining non-overlapped hyper-rectangle sections that falling within the same class. This provides an opportunity to have adaptive decision boundaries, and also employing batch data searching instead of incremental searching. Later, the classification was done in accordance with the distance between each particular query and generalized exemplars. The accuracy and time requirements for classification of synthetic datasets and a benchmark dataset obtained by JGE, NGE and other popular machine learning methods were compared and the achieved results by JGE found acceptable. (C) 2015 Elsevier B.V. All rights reserved.Öğe LEARNING WITH CLASSICAL CONDITIONING(Ieee, 2014) Ertugrul, Omer Faruk; Tagluk, Mehmet EminBehavioral learning theory evaluates human's learning process in terms of observable stimulus and responses. One of the behavioral learning methods is the classical conditioning. The classical conditioning theory proposed by Pavlov concerns the analyses of conditioning a response with a neutral stimulus, inspiring from the relation between natural stimulus and response. In this study the classical conditioning theory is modeled in real-time. The viability of the proposed method to basic principles of classical conditioning, based on stimulus-response relations was achieved and compared to the available computational methods.Öğe A novel machine learning method based on generalized behavioral learning theory(Springer, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet EminLearning is an important talent for understanding the nature and accordingly controlling behavioral characteristics. Behavioral learning theories are one of the popular learning theories which are built on experimental findings. These theories are widely applied in psychotherapy, psychology, neurology as well as in advertisements and robotics. There is an abundant literature associated with understanding learning mechanism, and various models have been proposed for the realization of learning theories. Nevertheless, none of those models are able to satisfactorily simulate the concept of classical conditioning. In this study, popular behavioral learning theories were firstly simplified and the contentious issues with them were clarified by conducting intuitive experiments. The experimental results and information available in the literature were evaluated, and behavioral learning theories were jointly generalized accordingly. The proposed model, to our knowledge, is the first one that possesses not only modeling all features of classical conditioning but also including all features with behavioral theories such as Pavlov, Watson, Guthrie, Thorndike and Skinner. Also, a microcontroller card (Arduino Mega 2560) was used to validate the applicability of the proposed model in robotics. Obtained results showed that this generalized model has a high capacity for modeling human learning. Then, the proposed learning model was further improved to be utilized as a machine learning method that can continuously learn similar to human being. The result obtained from the use of this method, in terms of computational cost and accuracy, showed that the proposed method can be successfully employed in machine learning, especially for time ordered datasets.Öğe A novel version of k nearest neighbor: Dependent nearest neighbor(Elsevier, 2017) Ertugrul, Omer Faruk; Tagluk, Mehmet Emink nearest neighbor (kNN) is one of the basic processes behind various machine learning methods In kNN, the relation of a query to a neighboring sample is basically measured by a similarity metric, such as Euclidean distance. This process starts with mapping the training dataset onto a one-dimensional distance space based on the calculated similarities, and then labeling the query in accordance with the most dominant or mean of the labels of the k nearest neighbors, in classification or regression issues, respectively. The number of nearest neighbors (k) is chosen according to the desired limit of success. Nonetheless, two distinct samples may have equal distances to query but, with different angles in the feature space. The similarity of the query to these two samples needs to be weighted in accordance with the angle going between the query and each of the samples to differentiate between the two distances in reference to angular information. This opinion can be analyzed in the context of dependency and can be utilized to increase the precision of classifier. With this point of view, instead of kNN, the query is labeled according to its nearest dependent neighbors that are determined by a joint function, which is built on the similarity and the dependency. This method, therefore, may be called dependent NN (d-NN). To demonstrate d-NN, it is applied to synthetic datasets, which have different statistical distributions, and 4 benchmark datasets, which are Pima Indian, Hepatitis, approximate Sinc and CASP datasets. Results showed the superiority of d-NN in terms of accuracy and computation cost as compared to other employed popular machine learning methods. (C) 2017 Elsevier B.V. All rights reserved.
