Yazar "Ustundag, Mehmet" seçeneğine göre listele

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    A Novel Analog Modulation Classification: Discrete Wavelet Transform- Extreme Learning Machine (DWT-ELM)
    (2021) Ustundag, Mehmet
    The aim of this study is to propose a method using discrete wavelet transform and extreme learning machine (DWT-ELM) in classification of communication signals. Six types of analog modulated signals as “AM”, “DSB”, “USB”, “LSB”, “FM” and “PM” are used for classification and analog modulated signal dataset consists of 1920 signals. These signals are also added white noise. Feature extraction is performed using different DWT filters. The feature vector obtained from DWT is used in classification. ELM is preferred due to its advantages over conventional back-propagation based classification. The feature vector is fed by the inputs of the ELM. The performance of the proposed method is evaluated for different types of DWT filters. In addition, compared results with similar study are presented to be able to determine the success of the proposed method.
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    Cross-scale fusion network and two-stage decomposition for power forecasting of offshore wind turbines
    (Pergamon-Elsevier Science Ltd, 2025) Korkmaz, Deniz; Acikgoz, Hakan; Ustundag, Mehmet
    This research presents a hybrid forecasting model for offshore wind power, which is based on a two-stage decomposition process and a densely connected convolutional network. Initially, the offshore wind power data is decomposed into several components utilizing an improved complete ensemble empirical mode decomposition with adaptive noise. The high-frequency component is further divided into multiple components through empirical mode decomposition. Following the decomposition, the dataset is transformed into the HSV color space. The proposed model features a sequential and multi-scale convolutional block architecture, inspired by the clique network approach. Furthermore, a squeeze-and-excitation module is incorporated to enhance the network performance. Comparative experiments are conducted against state-of-the-art deep learning models using data from two offshore wind turbines. The results indicate that the proposed model achieves superior performance metrics for WT3 and WT4, with root mean square error, mean absolute error, and mean absolute percentage error values ranging from 4.4796 to 4.8578, 3.2736 to 3.6543, and 0.2127 to 0.2193 for 1-h ahead forecast; 4.9674 to 5.7693, 3.5980 to 4.2028, and 0.2214 to 0.2295 for 3-h ahead forecast; and 5.8889 to 5.6338, 4.4247 to 4.1148, and 0.3064 to 0.2436 for 5-h ahead forecast, respectively. This pioneering two-stage decomposition and cross-scale CNN outperforms benchmarks by up to 74 % in RMSE. The proposed methodology improves short-term offshore wind power prediction by removing irregularities in the datasets.
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    OUTPUT POWER ESTIMATION OF A PHOTOVOLTAIC PANEL BY EXTREME LEARNING MACHINE
    (2024) Toprak, Serhat; COTELI, Resul; Ustundag, Mehmet; Esen, Hikmet
    In this study, the output power of a photovoltaic (PV) panel under different operating conditions was estimated with the help of an extreme learning algorithm (ELM). For this purpose, a PV panel with a power of 180W was installed, and the open circuit voltage, short circuit current, panel temperature, and solar radiation of this panel were measured and recorded at regular intervals. A total of 75 measurement data were obtained. The maximum power of the panel was calculated using the open circuit voltage and short circuit current information. While panel temperature and solar radiation were given as inputs to the regression model of the PV panel based on ELM, the output of the regression model was taken as the maximum power of the PV panel. To improve the prediction accuracy of ELM, the number of input neurons of ELM and the type of activation function used in the hidden layer were determined by trial and error method. The generated PV data set is separated into training and testing sets. The performance of the method was examined with the 5-fold cross-validation method. For this purpose, the dataset was divided into 5 equal parts. One of these parts was used for testing the ELM and the remaining four sets were used for training the ELM, and this was done by changing the test set each time. Thus, the network was trained and tested 5 times with different sets, and the test result of the network was obtained by averaging the sum of the performances of all test functions. Regression results obtained from ELM are given for different numbers of hidden layer neurons and different types of activation functions in the hidden layer. The best prediction result of ELM was obtained for the case where the hidden layer activation function was tangent sigmoid and the number of hidden layer neurons was 20. The R-values were found to be 1 when the number of hidden layer neurons was 20 and tangent and radial basis activation functions were used. From the results obtained, it has been seen that ELM predicts the output power of the PV panel with very high accuracy. It is concluded that ELM is a useful tool for estimating the PV panel output power.