Emrullah Fatih Yetkin

Yetkin, Emrullah Fatih

Name Variants

Y., Emrullah Fatih
Yetkin, E. F.
Emrullah Fatih, Yetkin
Yetkin, Emrullah Fatih
Yetkin, E.
Fatih Yetkin E.
Yetkin, EMRULLAH FATIH
E. Yetkin
YETKIN, Emrullah Fatih
Emrullah Fatih Yetkin
E. F. Yetkin
YETKIN, EMRULLAH FATIH
Yetkin,E.F.
Yetkin E.
Yetkin,Emrullah Fatih
Emrullah Fatih YETKIN
Y.,Emrullah Fatih
EMRULLAH FATIH YETKIN
Yetkin, Emrullah Fatih
E.,Yetkin
E. F. Yetkin
E.,Yetkin
Emrullah Fatih, Yetkin
Yetkin, E. Fatih

Job Title

Dr. Öğr. Üyesi

Email Address

fatih.yetkin@khas.edu.tr

ORCID ID

0000-0003-1115-4454

Full item page

Scholarly Output

25

Articles

13

Citation Count

8

Supervised Theses

3 Scholarly Output Search Results

Now showing 1 - 10 of 25

Active and Reactive Power Load Profiling Using Dimensionality Reduction Techniques and Clustering
(Institute of Electrical and Electronics Engineers Inc., 2019) Yetkin, E. Fatih; Ceylan, Oğuzhan; Ceylan, Oğuzhan; Yetkin, Emrullah Fatih; Papadopoulos, Theofilos A.; Kazaki, Anastasia G.; Barzegkar-Ntovom, Georgios A.
This paper proposes a methodology to characterize active and reactive power load profiles. Specifically, the approach makes use of fast Fourier Transform for conversion into frequency domain, principle component analysis to reduce the dimension and K-means++ to determine the representative load profiles. The data set consists of five-year measurements taken from the Democritus University of Thrace Campus. Test days were also classified as working and non-working. From the results it is observed that the proposed methodology determines representative load profiles effectively both regarding active and reactive power.
Comparative Classification Performances of Filter Model Feature Selection Algorithms in Eeg Based Brain Computer Interface System
(Gazi Univ, Fac Engineering Architecture, 2023) Bulut, Cem; Ballı, Tuğçe; Balli, Tugce; Yetkin, Emrullah Fatih; Yetkin, E. Fatih
Brain-computer interface (BCI) systems enable individuals to use a computer or assistive technologies such as a neuroprosthetic arm by translating their brain electrical activity into control commands. In this study, the use of filter-based feature selection methods for design of BCI systems is investigated. EEG recordings obtained from a BCI system designed for the control of a neuroprosthetic device are analyzed. Two feature sets were created; the first set was band power features from six main frequency bands (delta (1.0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-25 Hz), high-beta (25-30Hz) and gamma (30-50 Hz)) and the second set was band power features from ten frequency sub-bands (delta (1-4 Hz), theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), beta1 (12-15 Hz), beta2 (15-18 Hz), beta3 (18-25 Hz), gamma1 (30-35 Hz), gamma2 (35-40 Hz), gamma3 (40-50 Hz)). Ten filter-based feature selection methods are investigated along with linear discriminant analysis, random forests, decision tree and support vector machines algorithms. The results indicate that feature selection methods leads to a higher classification accuracy and eigen value centrality (Ecfs) and infinite feature selection (Inffs) methods have consistently provided higher accuracy rates as compared to rest of the feature selection methods.
Parallel Implementation Of Iterative Rational Krylov Methods For Model Order Reduction
(IEEE, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih
Model order reduction (MOR) techniques are getting more important in large scale computational tasks like large scale electronic circuit simulations. In this paper we present some experimental work on multiprocessor systems for rational Krylov methods. These methods require huge memory and computational power especially in large scale simulations. Therefore these methods are fairly suitable for parallel computing.
Filtre Modelli Öznitelik Seçim Algoritmalarının Eeg Tabanlı Beyin Bilgisayar Arayüzü Sistemindeki Karşılaştırmalı Sınıflandırma Performansları
(2023) Bulut, Cem; Ballı, Tuğçe; Ballı, Tuğçe; Yetkin, Emrullah Fatih; Yetkin, E. Fatih
Beyin bilgisayar arayüzleri (BBA), beyin elektriksel aktivitelerini kontrol komutlarına çevirerek bilgisayar veya nöroprostetik kol gibi yardımcı teknolojilerin kullanılmasını sağlayan sistemlerdir. Bu çalışmada filtre tabanlı öznitelik seçim yöntemlerinin farklı sınıflandırma algoritmaları ile birlikte kullanılmasının BBA sistemlerine getirebileceği kazanımlar araştırılmıştır. Bu çerçevede nöroprostetik bir cihazın kontrolü için tasarlanan BBA sisteminden elde edilmiş EEG kayıtları analiz edilmiştir. EEG kayıtlarının analizi için delta (1.0-4 Hz), teta (4-8 Hz), alfa (8-12 Hz), beta (12-25 Hz), yüksek-beta (25-30Hz) ve gama (30-50 Hz) frekans bantlarından ve delta (1-4 Hz), teta (4-8 Hz), alfa1 (8-10 Hz), alfa2 (10-12 Hz), beta1 (12-15 Hz), beta2 (15-18 Hz), beta3 (18-25 Hz), gama1 (30-35 Hz), gama2 (35-40 Hz), gama3 (40-50 Hz) alt frekans bantlarından bant gücü öznitelikleri çıkarılmıştır. Elde edilen iki veri seti öznitelik seçimi uygulamadan ve öznitelik seçimi uygulayarak sınıflandırılmıştır. Çalışmada toplam 10 adet filtre tabanlı öznitelik seçimi yöntemi ile birlikte, doğrusal ayırt eden analizi, rassal ormanlar, karar ağaçları ve destek vektör makinaları sınıflandırma algoritmaları kullanılmıştır. Çalışma sonucunda EEG kayıtlarının sınıflandırılması için öznitelik seçme algoritmalarının uygulanmasının daha yüksek başarımlı sonuçlar verdiği ve bu çalışmada ele alınan öznitelik seçme yöntemlerinden, özdeğer merkeziyetine göre öznitelik seçimi (Ecfs) ve sonsuz öznitelik seçimi (Inffs) yöntemlerinin filtre tabanlı yaklaşımlar arasında en iyi sonuçları verdiği gözlenmiştir.
A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Yetkin, Emrullah Fatih; Yetkin, E. Fatih; Camps-Valls, Gustau
Feature selection (FS) is essential in various fields of science and engineering, from remote sensing to computer vision. Reducing data dimensionality by removing redundant features and selecting the most informative ones improves machine learning algorithms' performance, especially in supervised classification tasks, while lowering storage needs. Graph-embedding (GE) techniques have recently been found efficient for FS since they preserve the geometric structure of the original feature space while embedding data into a low-dimensional subspace. However, the main drawback is the high computational cost of solving an eigenvalue decomposition problem, especially for large-scale problems. This article addresses this issue by combining the GE framework and representation theory for a novel FS method. Inspired by the high-dimensional model representation (HDMR), the feature transformation is assumed to be a linear combination of a set of univariate orthogonal functions carried out in the GE framework. As a result, an explicit embedding function is created, which can be utilized to embed out-of-samples into low-dimensional space and provide a feature relevance score. The significant contribution of the proposed method is to divide an $n$ -dimensional generalized eigenvalue problem into $n$ small-sized eigenvalue problems. With this property, the computational complexity (CC) of the GE is significantly reduced, resulting in a scalable FS method, which could be easily parallelized too. The performance of the proposed method is compared favorably to its counterparts in high-dimensional hyperspectral image (HSI) processing in terms of classification accuracy, feature stability, and computational time.
On the Selection of Interpolation Points for Rational Krylov Methods
(Springer-Verlag Berlin, 2012) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih
We suggest a simple and an efficient way of selecting a suitable set of interpolation points for the well-known rational Krylov based model order reduction techniques. To do this some sampling points from the frequency response of the transfer function are taken. These points correspond to the places where the sign of the numerical derivation of transfer function changes. The suggested method requires a set of linear system's solutions several times. But they can be computed concurrently by different processors in a parallel computing environment. Serial performance of the method is compared to the well-known H-2 optimal method for several benchmark examples. The method achieves acceptable accuracies (the same order of magnitude) compared to that of H-2 optimal methods and has a better performance than the common selection procedures such as linearly distributed points.
Assesment of soft error sensitivity of power flow analysis
(Gazi Univ, Fac Engineering Architecture, 2023) Yetkin, Emrullah Fatih; Yetkin, Emrullah Fatih
Today's power systems are large and interconnected to each other with many buses, lines, loads, and generators. Even the solution of a single snapshot of the system for specific conditions requires the solution of systems of equations with large sizes. Thus, to obtain the results in a reasonable time for large problems like electrical power flow simulations, modern large computational environments should be employed. However, because of the increasing number of components in the modern computational environment, the possibility of soft errors also increases. Soft errors can be defined as failures arising from several fluctuations due to x-rays, cosmic particle effects, etc. These types of errors usually appear at any time of computation as a bit-flip in any floating-point operations. In this paper, we will investigate the soft-error effects on large-scale power flow simulations. Generally, power flow calculations are performed by using Newton Raphson Method. The system is modeled by nonlinear equations and the solution process requires a linear solver is employed to take the inverse of the Jacobian matrix at each iteration. In this study, the soft-error sensitivity of the numerical methods used in load flow was examined, and the problems that may be encountered were revealed.
Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction
(Springer-Verlag Berlin, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih
We suggest a simple and an efficient iterative method based on both the Gerschgorin eigenvalue inclusion theorem and the deflation methods to compute a Reduced Order Model (ROM) to lower greatly the order of a given state space system. This method is especially efficient in symmetric state-space systems but it works for the other cases with some modifications.
Assessment of Load and Generation Modelling on the Quasi-Static Analysis of Distribution Networks
(Elsevier, 2021) Lamprianidou, I. S.; Yetkin, Emrullah Fatih; Papadopoulos, T. A.; Kryonidis, G. C.; Yetkin, E. Fatih; Pippi, K. D.; Chrysochos, A., I
Quasi-static analysis of power systems can be performed by means of timeseries-based and probability density function-based models. In this paper, the effect of different load and generation modelling approaches on the quasi-static analysis of distribution networks is investigated. Different simplified load and distributed renewable energy sources generation timeseries-based models are considered as well as probabilistic analysis. Moreover, a more sophisticated approach based on cluster analysis is introduced to identify harmonized sets of representative load and generation patterns. To determine the optimum number of clusters, a three-step methodology is proposed. The examined cases include the quasi-static analysis of distribution networks for different operational conditions to identify the simplified modelling approaches that can efficiently predict the network voltages and losses. Finally, the computational efficiency by using the simplified models is evaluated in temperature-dependent power flow analysis of distribution networks. (C) 2021 Elsevier Ltd. All rights reserved.
Recycling Newton-Krylov algorithm for efficient solution of large scale power systems
(Elsevier Sci Ltd, 2023) Ceylan, Oğuzhan; Yetkin, Emrullah Fatih
Power flow calculations are crucial for the study of power systems, as they can be used to calculate bus voltage magnitudes and phase angles, as well as active and reactive power flows on lines. In this paper, a new approach, the Recycling Newton-Krylov (ReNK) algorithm, is proposed to solve the linear systems of equations in Newton-Raphson iterations. The proposed method uses the Generalized Conjugate Residuals with inner orthogonalization and deflated restarting (GCRO-DR) method within the Newton-Raphson algorithm and reuses the Krylov subspace information generated in previous Newton runs. We evaluate the performance of the proposed method over the traditional direct solver (LU) and iterative solvers (Generalized Minimal Residual Method (GMRES), the Biconjugate Gradient Stabilized Method (Bi-CGSTAB) and Quasi-Minimal Residual Method (QMR)) as the inner linear solver of the Newton-Raphson method. We use different test systems with a number of busses ranging from 300 to 70000 and compare the number of iterations of the inner linear solver (for iterative solvers) and the CPU times (for both direct and iterative solvers). We also test the performance of the ReNK algorithm for contingency analysis and for different load conditions to simulate optimization problems and observe possible performance gains.