Karasu, Seçkin | Altan, Aytaç | Saraç, Zehra | Hacıoğlu, Rıfat

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different window functions and lengths, wind speed prediction is made using filters with different weight coefficients. For the number of hidden layer neurons is 14 and the number of delay steps is 10, MAE, MSE and RMSE values are calculated . . .as 0.0315, 0.0019, 0.0445, respectively, with NAR neural network. It is seen that the proposed method for the wind speed dataset has a higher prediction performance than thefilter methods. © 2017 IEEE Daha fazlası Daha az

Kaya, Ceren | Erkaymaz, Okan | Ayar, Orhan | Özer, Mahmut

Proceedings | 2017 | 2016 Medical Technologies National Conference, TIPTEKNO 2016

The insulin hormone secreted from the pancreas gland in the body is not present in sufficient amount, or because they do not fit, which is defined as the elevation of blood glucose 'Diabetes Mellitus (Diabetes)'. 'Diabetic Retinopathy' is the most common in diabetes-related eye diseases. It had done damages in the retina that detect light on behind the eye as a result of changes in the arteries that is one of the reasons that makes blindness (loss of vision) in people. In this study, horizontal and vertical Video-Oculography (VOG) signals captured by using internal tracking camera in Metrovision MonPackOne Electrooculography device. . . . In order to filter the noise from the signals, the wavelet transform method was used. Obtained signals have shown that the signals of diabetic retinopathy patients have higher amplitude and irregular characteristic than the signals obtained from healthy groups. In both groups, significant Daubechies-6 wavelet coefficients (A6-D6) gave better results than Daubechies-4 wavelet coefficients (A4-D4). Obtained data as a result of using wavelet transform sheds light on feature extraction and classification in proposed future works. © 2016 IEEE Daha fazlası Daha az