Yetkin, Emrullah Fatih
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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
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
Main Affiliation
Business Administration
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals Report Points
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Scholarly Output
28
Articles
13
Citation Count
8
Supervised Theses
6
28 results
Scholarly Output Search Results
Now showing 1 - 10 of 28
Conference Object Citation - WoS: 1Citation - Scopus: 3Active 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.; Business Administration; Management Information SystemsThis 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.Conference Object Citation - WoS: 0On the Selection of Interpolation Points for Rational Krylov Methods(Springer-Verlag Berlin, 2012) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih; Business Administration; Management Information SystemsWe 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.Article Citation - WoS: 2Citation - Scopus: 4A 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; Business AdministrationFeature 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.Article Citation - Scopus: 0A Sparsity-Preserving Spectral Preconditioner for Power Ow Analysis(Turkiye Klinikleri Journal of Medical Sciences, 2016) Yetkin,E.F.; Yetkin, Emrullah Fatih; Daʇ,H.; Business AdministrationDue to the ever-increasing demand for more detailed and accurate power system simulations, the dimensions of mathematical models increase. Although the traditional direct linear equation solvers based on LU factorization are robust, they have limited scalability on the parallel platforms. On the other hand, simulations of the power system events need to be performed at a reasonable time to assess the results of the unwanted events and to take the necessary remedial actions. Hence, to obtain faster solutions for more detailed models, parallel platforms should be used. To this end, direct solvers can be replaced by Krylov subspace methods (conjugate gradient, generalized minimal residuals, etc.). Krylov subspace methods need some accelerators to achieve competitive performance. In this article, a new preconditioner is proposed for Krylov subspace-based iterative methods. The proposed preconditioner is based on the spectral projectors. It is known that the computational complexity of the spectral projectors is quite high. Therefore, we also suggest a new approximate computation technique for spectral projectors as appropriate eigenvalue-based accelerators for efficient computation of power ow problems. The convergence characteristics and sparsity structure of the preconditioners are compared to the well-known black-box preconditioners, such as incomplete LU, and the results are presented. ©2016 Tübitak.Conference Object Citation - WoS: 0Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction(Springer-Verlag Berlin, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih; Business Administration; Management Information SystemsWe 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.Conference Object Citation - WoS: 0Parallel Implementation Of Iterative Rational Krylov Methods For Model Order Reduction(IEEE, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih; Business Administration; Management Information SystemsModel 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.Article 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; Business Administration; Management Information SystemsBeyin 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.Article Citation - WoS: 0A Sparsity-Preserving Spectral Preconditioner for Power Flow Analysis(TUBITAK Scientific & Technical Research Council Turkey, 2016) Yetkin, Emrullah Fatih; Yetkin, Emrullah Fatih; Dağ, Hasan; Dağ, Hasan; Business Administration; Management Information SystemsDue to the ever-increasing demand for more detailed and accurate power system simulations the dimensions of mathematical models increase. Although the traditional direct linear equation solvers based on LU factorization are robust they have limited scalability on the parallel platforms. On the other hand simulations of the power system events need to be performed at a reasonable time to assess the results of the unwanted events and to take the necessary remedial actions. Hence to obtain faster solutions for more detailed models parallel platforms should be used. To this end direct solvers can be replaced by Krylov subspace methods (conjugate gradient generalized minimal residuals etc.). Krylov subspace methods need some accelerators to achieve competitive performance. In this article a new preconditioner is proposed for Krylov subspace-based iterative methods. The proposed preconditioner is based on the spectral projectors. It is known that the computational complexity of the spectral projectors is quite high. Therefore we also suggest a new approximate computation technique for spectral projectors as appropriate eigenvalue-based accelerators for efficient computation of power flow problems. The convergence characteristics and sparsity structure of the preconditioners are compared to the well-known black-box preconditioners such as incomplete LU and the results are presented.Article Citation - WoS: 13Citation - Scopus: 22A Hybrid Approach With Gan and Dp for Privacy Preservation of Iiot Data(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Hindistan, Yavuz Selim; Yetkin, Emrullah Fatih; Yetkin, E. Fatih; Business AdministrationThere are emerging trends to use the Industrial Internet of Things (IIoT) in manufacturing and related industries. Machine Learning (ML) techniques are widely used to interpret the collected IoT data for improving the company's operational excellence and predictive maintenance. In general, ML applications require high computational resource allocation and expertise. Manufacturing companies usually transfer their IIoT data to an ML-enabled third party or a cloud system. ML applications need decrypted data to perform ML tasks efficiently. Therefore, the third parties may have unacceptable access rights during the data processing to the content of IIoT data that contains a portrait of the production process. IIoT data may include hidden sensitive features, creating information leakage for the companies. All these concerns prevent companies from sharing their IIoT data with third parties. This paper proposes a novel method based on the hybrid usage of Generative Adversarial Networks (GAN) and Differential Privacy (DP) to preserve sensitive data in IIoT operations. We aim to sustain IIoT data privacy with minimal accuracy loss without adding high additional computational costs to the overall data processing scheme. We demonstrate the efficiency of our approach with publicly available data sets and a realistic IIoT data set collected from a confectionery production process. We employed well-known privacy six assessment metrics from the literature and measured the efficiency of the proposed technique. We showed, with the help of experiments, that the proposed method preserves the privacy of the data while keeping the Linear Regression (LR) algorithms stable in terms of the R-Squared accuracy metric. The model also ensures privacy protection for hidden sensitive data. In this way, the method prevents the production of hidden sensitive data from the sub-feature sets.Article Güç akışı analizinin geçici hata duyarlılığının değerlendirilmesi(2023) Yetkin, Emrullah Fatih; Yetkin, Emrullah Fatih; Business AdministrationGünümüzün güç sistemleri detaylı modelleme ihtiyaçları nedeniyle çok büyük boyutlara ulaşabilmektedir ve belirli koşullar için sistemin tek bir anlık görüntüsünün çözümü bile büyük boyutlu denklem sistemlerinin çözümünü gerektirir. Bu nedenle de makul bir sürede sonuçları elde etmek için modern yüksek başarımlı hesaplama ortamları kullanılmalıdır. Bununla birlikte, yüksek başarımlı hesaplama ortamlarında artan bileşen sayısı nedeniyle, geçici hata olasılığı da artar. Geçici hatalar, x-ışınları, kozmik parçacık etkileri gibi nedenlerle cihaz bileşenlerinde oluşabilen çeşitli dalgalanmalardan kaynaklı arızalar olarak tanımlanabilir. Bu tür hatalar genellikle herhangi bir hesaplama anında herhangi bir kayan nokta işleminde yaşanan bir bit- kayması ile modellenebilir. Bu makalede, büyük ölçekli güç akışı simülasyonları üzerindeki geçici hata etkileri incelenmektedir. Genel olarak yük akışı hesaplamaları, sistem doğrusal olmayan denklemlerle modellendiği için, Newton-Raphson yöntemi kullanılarak yapılır ve çözüm süreci, her yinelemede Jakobiyen matrisinin tersini almak için doğrusal bir çözücünün kullanılmasını gerektirir. Bu çalışmada, özellikle yenilenebilir enerji kaynaklarının sistemlere eklenmesi ile çok büyük boyutlara ulaşılabilen elektrik yük akış problemlerinde kullanılan matematiksel yöntemlerin geçici-hatalara karşı hassasiyetleri incelenerek, karşılaşılabilecek sorunlar ortaya konulmuştur.
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