No abstract

Determination and investigation of incidents affecting Power Quality (PQ) is very important for consumers. In this study, estimation of PQ events is obtained to determine the disturbances of PQ by using Empirical Wavelet Transform (EWT) and Discrete Wavelet Transform (DWT) methods and with this estimated parameter. PQ disturbances were examined with Support Vector Machine (SVM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) classification methods. Voltage signals (sag, swell, interruption, transient and normal) used in the classification of PQ disturbances were recorded from grid with the aid of a microcontroller based on device designed with a sampling frequency of 6.4 kHz. Classification consequences using Machine Learning Methods show that DWT outperforms over EWT for feature extraction processing and the classification accuracy is tabled. Classification by ANN and ANFIS through the use of conjecture parameters in PQ disturbances based on DWT Method has been recommended.

No abstract

ÖzetçeEEG işaretlerinin beynin fonksiyonları hakkında çok miktarda bilgi içerdiği bilinmektedir. Epilepsi teşhisinde EEG en önemli bilgi kaynağı olduğu için, birçok araştırmacı EEG işaretlerinden bu amaca uygun bilgi elde etmeye çalışmışlardır. Bu çalışmada sunulan yöntemde, önce EEG işaretlerine öz bağlanımlı (AR) uygulanarak güç spektrumu elde edilmiş, daha sonra elde edilen özellik vektörleri TBA, BBA ve DAA kullanılarak boyut indirgemesi yapılmış; elde edilen değerler destek vektör makinesi (DVM) ile sınıflandırılmaya tabi tutulmuş ve çıkışta epileptik veya değil şeklinde sınıflandırma gerçekleştirilmiştir. Đşaretteki özelliklerin belirlenerek hekime sara tanısında yardımcı olacak, otomatik bir sistem elde edilmesi amaçlanmıştır. DVM ile yapılan EEG sınıflandırmasında DAA'nın daha iyi sonuçlar verdiği ve bu sonuçların hastalık teşhisinde kullanılabileceği görülmüştür.Anahtar Kelimeler: EEG, Epilepsi, AR, TBA; BBA; DAA;Destek vektör makinesi (DVM). AbstractSince EEG is one of the most important sources of information in diagnosis of epilepsy, several researchers tried to address the issue of decision support for such a data. We present a method for classifying epilepsy of full spectrum EEG recordings. In the proposed method, autoregressive (AR) model is used to acquire power spectrum of EEG signals, then dimension of the extracted feature vectors is reduced by using ICA, PCA and LDA, and these vectors used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. It is observed that, SVM classification of EEG signals gives better results and these results can also be used for diagnosis of diseases.

The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.