Now showing items 1-20 of 60

  • A Generic Framework for Building Heterogeneous Simulations of Parallel and Distributed Computing Systems 

    Authors:Dursun, Taner; Daǧ, Hasan
    Publisher and Date:(Springer Heidelberg, 2017)
    There have been many systems available for parallel and distributed computing (PDC) applications such as grids clusters super-computers clouds peer-to-peer and volunteer computing systems. High-performance computing (HPC) has been an obvious candidate domain to take advantage of PDC systems. Most of the research on HPC has been conducted with simulations and has been generally focused on a specific type of PDC system. This paper however introduces a general purpose simulation model that can be ...

  • A New Preconditioner Design Based on Spectral Division for Power Flow Analysis 

    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 ...

  • A preconditioner for transient stability time domain simulation 

    Authors:Soykan, Gürkan; Flueck, Alexander J.; Daǧ, Hasan
    Publisher and Date:(2011)
    Transient Stability analysis which is one of the most important tasks of power system dynamic security analysis determines the dynamic behaviour of the power system after a large disturbance. Differential and algebraic equations (DAEs) model the nonlinear dynamic power system. The conventional time domain solution process uses a Newton method to simultaneously solve the differential equations discretized via an implicit integration method alongside the non-linear algebraic network equations. Direct ...

  • A Recommender Model Based on Trust Value and Time Decay Improve the Quality of Product Rating Score in E-commerce Platforms 

    Authors:Işık, Muhittin; Daǧ, Hasan
    Publisher and Date:(IEEE, 2017)
    Most of the existing products rating score algorithms do not take fake accounts and time decay of users' ratings into account when creating the list of recommendations. The trust values and the time decay of users' ratings to an item may improve the quality of product rating score in e-commerce platforms especially when it is thought that nowadays the majority of customers read the reviews before making a purchase. In this paper we first introduce the concept trust value of users by explaining its ...

  • A sparsity-preserving spectral preconditioner for power flow analysis 

    Due 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 ...

  • A spectral divide and conquer method based preconditioner design for power flow analysis 

    Authors:Daǧ, Hasan; Yetkin, Emrullah Fatih
    Publisher and Date:(2010)
    Power system simulations most of the time require solution of a large sparse linear system. Traditional methods such as LU decomposition based direct methods are not suitable for parallelization in general. Thus Krylov subspace based iterative methods (i.e. Conjugate Gradient Generalized Minimal Residuals (GMRES)) can be used as very good alternatives compared to direct methods. On the other hand Krylov based iterative solvers need a preconditioner to accelerate the convergence process. In this ...

  • Alternative Credit Scoring and Classification Employing Machine Learning Techniques on a Big Data Platform 

    With the bloom of financial technology and innovations aiming to deliver a high standard of financial services, banks and credit service companies, along with other financial institutions, use the most recent technologies available in a variety of ways from addressing the information asymmetry, matching the needs of borrowers and lenders, to facilitating transactions using payment services. In the long list of FinTechs, one of the most attractive platforms is the Peer-to-Peer (P2P) lending which ...

  • Applications of Eigenvalue Counting and Inclusion Theorems in Model Order Reduction 

    Authors:Yetkin, E. Fatih; Daǧ, Hasan
    Publisher and Date:(Springer-Verlag Berlin, 2010)
    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.

  • Applying machine learning algorithms in sales prediction 

    Authors:Sekban, Judi
    Publisher and Date:(Kadir Has University, 2019)
    Makine öğrenimi bir çok endüstride üzerinde yoğun çalışmalar yapılan bir konu olmuştur, ve neyse ki şirketler kendi problemlerini çözebilecek çeşitli machine learning yaklaşımları hakkında günden güne daha fazla bilgi sahibi oluyorlar. Fakat, farklı makine öğreniminin modellerinden en iyi şekilde sonuç almak ve verimli sonuçlara ulaşabilmek için, modellerin uygulanış biçimlerini ve verinin doğasını iyi anlamak gerekir. Bu tez, belli bir tahmin görevi için, uygulanan ...

  • Arama motorları mimarisi, web sayfalarının içerik skoru ve google pagerank formülünün incelenmesi 

    Authors:Işık, Muhittin
    Publisher and Date:(Kadir Has University, 2013)
    Ülkemizde arama motorlarının önemi hızla artmasına rağmen, maalesef ki hem akademik ortamda hem de güncel teknoloji piyasasında bu alanla ilgili yeterli kaynak oluşturulamamıştır. Özellikle son dönemlerde internet üzerinden alış verişin yaygınlaşmaya başlamasıyla birlikte bu alana duyulan ilgi hızla bir gelişim süreci içine girmiştir. Artık her sektör, web ortamındaki aramalarda kendilerine ait web sayfalarını ilk sıralara koyma yarışına girmişlerdir. Bu yüzdendir ki gerek ülkemizdeki üniversiteler ...

  • Bobrek nakli gecirmis hastalarda akilli yuntem tabanli yeni oznitelik secme algoritmasi gelistirilmesi 

    Authors:Acar, Saylan Cagil
    Publisher and Date:(Kadir Has University, 2012)
    Veri madenciligi verilerden kesfedilecek desenler yardimiyla yeni bilgiler elde etme amaciyla cok farkli disiplinlerde kullanilan cesitli metotlardan olusmaktadir. Tip alanindaki verinin buyuklugu ve hayati onem tasimasi Veri madenciliginin bu alanda da uygulanmasini gerekli kilmistir. Bu tezde Veri Madenciliginin Tip alaninda kullanimi incelenmistir. Uygulama calismasi icin Ýstanbul universitesi Cerrahpasa Tip Fakultesi.nde ayakta tedavi goren hastalar arasindan Mart 2006 . Aralik 2007 tarihleri ...

  • Branch outage simulation based contingency screening by gravitational search algorithm 

    Power systems contingency analysis is an important issue for electric power system operators. This paper performs branch outage simulation based contingency screening using a bounded network approach. Local constrained optimization problem representing the branch outage phenomena is solved by the gravitational search algorithm. The proposed method is applied to IEEE 14 30 57 and 118 Bus Test systems and its performance from the point of capturing violations is evaluated. In addition false alarms ...

  • Branch outage solution using particle swarm optimization 

    Authors:Ceylan, Oğuzhan; Ozdemir, Aydogan; Daǧ, Hasan
    Publisher and Date:(2008)
    For post outage MW line flows and voltage magnitude calculations most of the methods use linear methods because of their simplicity. Especially for reactive power flow calculations one can face high errors. In this paper we use a minimization method that minimizes the errors resulting from the linear system model implementation. We solve the optimization problem using particle swarm optimization. We give some outage examples using IEEE 14 bus IEEE 30 bus and IEEE 57 bus data and compare the results ...

  • Comparison of Cost-Free Computational Tools for Teaching Physics 

    Authors:Er, Neslihan Fatma; Daǧ, Hasan
    Publisher and Date:(IEEE, 2010)
    It is widely accepted that it is quite difficult to engage today's students, from high schools to university, both in educational activities in class and "teaching" them physics due to their prejudices about the complexity of physics. The difficulty in capturing students' attention in class for a long time also plays a role in less effective teaching during learning activities. Research shows that students learn little from traditional lectures. According to constructivist learning theories, visual ...

  • Comparison of feature selection algorithms for medical data 

    Data mining application areas widen day by day. Among those areas medical area has been receiving quite a big attention. However working with very large data sets with many attributes is hard. Experts in this field use heavily advanced statistical analysis. The use of data mining techniques is fairly new. This paper compares three feature selection algorithms on medical data sets and comments on the importance of discretization of attributes. © 2012 IEEE.

  • Distributed Memory Parallel Transient Stability Analysis on a PC Cluster with Ethernet 

    On-line transient stability analysis is a necessity for real-time power system control and security. Parallel processing is a natural technology for achieving real-time solution performance. This paper presents a parallel-in-space algorithm based on a multi-level partitioning scheme in a distributed memory cluster environment. The main aim of the research is to decrease the wallclock time of transient stability analysis of large scale power systems by leveraging open source software and commodity ...

  • Double branch outage modeling and its solution using differential evolution method 

    Authors:Ceylan, Oǧuzhan; Ozdemir, Aydogan; Daǧ, Hasan
    Publisher and Date:(2011)
    Power system operators need to check the system security by contingency analysis which requires power flow solutions repeatedly. AC power flow is computationally slow even for a moderately sized system. Thus fast and accurate outage models and approximated solutions have been developed. This paper adopts a single branch outage model to a double branch outage one. The final constrained optimization problem resulted from modeling is then solved by using differential evolution method. Simulation ...

  • Double branch outage modeling and simulation: Bounded network approach 

    Authors:Ceylan, Oǧuzhan; Özdemir, Aydoğan; Daǧ, Hasan
    Publisher and Date:(Elsevier Science, 2015)
    Energy management system operators perform regular outage simulations in order to ensure secure operation of power systems. AC power flow based outage simulations are not preferred because of insufficient computational speed. Hence several outage models and computational methods providing acceptable accuracy have been developed. On the other hand double branch outages are critical rare events which can result in cascading outages and system collapse. This paper presents a double branch outage model ...


    Authors:Işık, Muhittin; Daǧ, Hasan
    Publisher and Date:(2017)
    Sahte kullanıcı hesapları, veri tabalarındaki seyreklik problemlerinden dolayı özellikle yeteri kadar kullanıcı tarafından puanlanmamış ürünlerde tavsiye algoritmalarını kolaylıkla etkileyebilmektedirler. Genellikle bu kullanıcı hesapları kendi ürününün puanını artırmak isteyen ürün sahipleri olabildiği gibi herhangi bir ürünü veya şirketi karalamak isteyen kötü niyetli kişiler de olabilmektedir. Bu durum birçok şirketin veri tabanı yoğunluğunun %1 den daha az olduğu düşünülürse e-ticaret ortamlarına ...

  • Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods 

    Authors:Çayır, Aykut; Yenidoğan, Işıl; Daǧ, Hasan
    Publisher and Date:(IEEE, 2018)
    Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper we propose a method which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms such as support vector machine gradient boosting machines and random forest. All of the proposed hybrid models and the above mentioned ...