Faruk Yigit

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A linear perturbation method is used to solve two-dimensional heat conduction problem in which a liquid becomes solidified by heat transfer to a plane mold of finite thickness. Heat flux drawn from the lower surface of the mold is approximately uniform, but contains a small sinusoidal perturbation in one space dimension. Numerical results are obtained for(More)
Nowadays, probabilistic neural networks have been used to pattern discrimination in non-stationary biological signals with individual characteristics. The main objective of this study was to develop a neural network based on Gaussian mixture model and logarithmic linearization to classify the T-wave ends, which are of the major parts of the ECG signals, For(More)
Nowadays, probabilistic neural networks have been frequently used to pattern discrimination in biological signals despite of non-stationary and individual characteristics of human subjects. In this study, a new approach was proposed to pattern classification for electrocardiography (ECG) signals based on Gaussian mixture model and logarithmic linearization.(More)
This paper proposes an approximate analytical and a numerical solution method to a twodimensional heat conduction problem in which a liquid becomes solidified by heat transfer to a planar mold surface by using a linear perturbation method. It is assumed that the cooling rate is perturbed by a small spatially sinusoidal heat flux at the shell–mold interface.(More)
A two-dimensional heat conduction problem in which a liquid becomes solidified by heat transfer to a sinusoidal mold of finite thickness is solved by using a linear perturbation method. The liquid perfectly wets the sinusoidal mold surface prior to the beginning of solidification. This leads to a corresponding undulation of the solidified shell thickness.(More)
Probabilistic neural networks have been frequently used in classification of nonstationary and individual signal patterns due to its prediction capability rather than certain results. The purpose of this study is to perform the classification of heartbeat in arrhythmic and non-arrhythmic electrocardiogram (ECG) signal based on Gaussian mixture model and(More)
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