Browsing by Author "Yetkin, E. Fatih"

Now showing 1 - 14 of 14

Citation Count: 1
Active and Reactive Power Load Profiling Using Dimensionality Reduction Techniques and Clustering
(Institute of Electrical and Electronics Engineers Inc., 2019) Ceylan, Oğuzhan; Ceylan, Oğuzhan; 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.
Citation Count: 0
Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction
(Springer-Verlag Berlin, 2010) Dağ, Hasan; Dağ, Hasan
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.
Citation Count: 3
Assessment of load and generation modelling on the quasi-static analysis of distribution networks
(Elsevier, 2021) Lamprianidou, I. S.; 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.
Citation Count: 0
Comparative classification performances of filter model feature selection algorithms in EEG based brain computer interface system
(Gazi Univ, Fac Engineering Architecture, 2023) Ballı, Tuğçe; Balli, Tugce; 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.
Citation Count: 1
Filtre modelli öznitelik seçim algoritmalarının EEG tabanlı beyin bilgisayar arayüzü sistemindeki karşılaştırmalı sınıflandırma performansları
(2023) Ballı, Tuğçe; Ballı, Tuğçe; 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.
Citation Count: 4
Generic dynamic load modelling using cluster analysis
(IEEE, 2018) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Papadopoulos, Theofilos A.; Yetkin, E. Fatih
In this paper a new generic load modelling procedure is proposed based on the application of cluster analysis on load model parameters identified from measured dynamic responses. The performance of the proposed approach is assessed using measurements obtained from a low-voltage laboratory scale test configuration. In order to develop robust generalized load models applicable to a wide range of operating conditions different load compositions operating conditions and voltage disturbances are considered in the analysis. The findings of this paper verify the validity of the proposed generic modelling procedure and indicate robust results using the proposed methodology.
Citation Count: 3
A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Hindistan, Yavuz Selim; Yetkin, E. Fatih
There 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.
Citation Count: 2
A New Preconditioner Design Based on Spectral Division for Power Flow Analysis
(Praise Worthy Prize Srl, 2011) Dağ, Hasan; Yetkin, E. Fatih; Manguoglu, Murat
Solution of large sparse linear systems is the most lime consuming part in many power system simulations. Direct solvers based on LU factorization although robust are known to have limited satiability on parallel platforms. Thus. Krylov subspace based iterative methods (i.e. Conjugate Gradient method Generalized Minimal Residuals (GMRES) method) can be used as alternatives. To achieve competitive performance and robustness however the Krylov subspace methods need a suitable preconditioner. In this work we propose a new preconditioner iterative methods which can be used in Newton-Raphson process of power flow analysis. The suggested preconditioner employs the basic spectral divide and conquer methods and invariant subspaces for clustering the eigenvalues of the Jacobian matrix appearing in Newton-Raphson steps of power flow simulation. To obtain the preconditioner we use Matrix Sign Function (MSF) and to obtain the MSF itself we use Sparse Approximate Inverse (SPAI) algorithm with Newton iteration. We compare the convergence characteristics of our preconditioner against the well-known black-box preconditioners such as incomplete-LU and SPAI. Copyright (C) 2011 Praise Worthy Prize S.r.l. - All rights reserved.
Citation Count: 0
On the selection of ınterpolation points for rational Krylov Methods
(Springer-Verlag Berlin, 2012) Dağ, Hasan; Dağ, Hasan
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.
Citation Count: 0
Parallel Implementation Of Iterative Rational Krylov Methods For Model Order Reduction
(IEEE, 2010) Dağ, Hasan; Dağ, Hasan
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.
Citation Count: 2
Recycling Newton-Krylov algorithm for efficient solution of large scale power systems
(Elsevier Sci Ltd, 2023) Ceylan, Oğuzhan; Ceylan, Oguzhan
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.
Citation Count: 1
A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Taskin, Gulsen; 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.
Citation Count: 3
Sensitivity of computational fluid dynamics simulations against soft errors
(Springer Wien, 2021) Yetkin, E. Fatih; Piskin, Senol
Computational capabilities of the largest high performance computing systems have increased by more than 100 folds in the last 10 years and keep increasing substantially every year. This increase is made possible mostly by multi-core technology besides the increase in clock speed of CPUs. Nowadays, there are systems with more than 100 thousand cores installed and available for processing simultaneously. Computational simulation tools are always in need of more than available computational sources. This is the case for especially complex, large scale flow problems. For these large scale problems, the soft error tolerance of the simulation codes should also be encountered where it is not an issue in relatively small scale problems due to the low occurrence probabilities. In this study, we analyzed the reaction of an incompressible flow solver to randomly generated soft errors at several levels of computation. Soft errors are induced into the final global assembly matrix of the solver by manipulating predetermined bit-flip operations. Behaviour of the computational fluid dynamics (CFD) solver is observed after iterative matrix solver, flow convergence and CFD iterations. Results show that the iterative solvers of CFD matrices are highly sensitive to customized soft errors while the final solutions seem more intact to bit-flip operations. But, the solutions might still differ from the real physical results depending on the bit-flip location and iteration number. So, the next generation computing platforms and codes should be designed to be able to detect bit-flip operations and be designed bit-flip resistant.
Citation Count: 1
Sparsity Preserving Computation for Spectral Projectors
(Amer Inst Physics, 2011) Dağ, Hasan; Dağ, Hasan
Several areas of applications such as model order reduction preconditioner design and eigenvalue problems for spectral projectors can be found in the literature. In this paper a fast and sparsity preserving approach for computing the spectral projectors is proposed. The suggested approach can be used in both Newton iteration and integral representation based methods. A comparison of the original and the suggested approaches in terms of computation time sparsity preservation and accuracy is presented in this paper.