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Öğe Altitude and Attitude Control of a Quadcopter Based on Neuro-Fuzzy Controller(Springer Science and Business Media Deutschland GmbH, 2022) Korkmaz, Deniz; Açıkgöz, Hakan; Üstündağ, MehmetIn this paper, a 6-degrees of freedom (DoF) nonlinear dynamic model of the quadcopter is derived and a robust altitude and attitude control is proposed. The motion control is performed with four neuro-fuzzy controllers that ensure rapid and robust performances for nonlinear and uncertain systems. The aim of the designed control scheme is to provide to track the desired yaw, pitch, roll, and altitude trajectories simultaneously. The simulations are realized in the MATLAB/Simulink environment. The obtained results show that the designed control scheme is robust and efficient in both altitude and attitude responses with different uncertain trajectories.Öğe COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images(Elsevier, 2020) Uçar, Ferhat; Korkmaz, DenizThe Coronavirus Disease 2019 (COVID-19) outbreak has a tremendous impact on global health and the daily life of people still living in more than two hundred countries. The crucial action to gain the force in the fight of COVID-19 is to have powerful monitoring of the site forming infected patients. Most of the initial tests rely on detecting the genetic material of the coronavirus, and they have a poor detection rate with the time-consuming operation. In the ongoing process, radiological imaging is also preferred where chest X-rays are highlighted in the diagnosis. Early studies express the patients with an abnormality in chest X-rays pointing to the presence of the COVID-19. On this motivation, there are several studies cover the deep learning-based solutions to detect the COVID-19 using chest X-rays. A part of the existing studies use non-public datasets, others perform on complicated Artificial Intelligent (AI) structures. In our study, we demonstrate an AI-based structure to outperform the existing studies. The SqueezeNet that comes forward with its light network design is tuned for the COVID-19 diagnosis with Bayesian optimization additive. Fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.Öğe An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network(Elsevier, 2022) Korkmaz, Deniz; Açıkgöz, HakanPhotovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs. In this study, an efficient PV fault detection method is proposed to classify different types of PV module anomalies using thermographic images. The proposed method is designed as a multi-scale convolutional neural network (CNN) with three branches based on the transfer learning strategy. The convolutional branches include multi-scale kernels with levels of visual perception and utilize pre-trained knowledge of the transferred network to improve the representation capability of the network. To overcome the imbalanced class distribution of the raw dataset, the oversampling technique is performed with the offline augmentation method, and the network performance is increased. In the experiments, 11 types of PV module faults such as cracking, diode, hot spot, offline module, and other classes are utilized. The average accuracy is obtained as 97.32% for fault detection and 93.51% for 11 anomaly types. The experimental results indicate that the proposed method gives higher classification accuracy and robustness in PV panel faults and outperforms the other deep learning methods and existing studiesÖğe An improved residual-based convolutional neural network for very short-term wind power forecasting(ELSEVIER, 2021) Yıldız, Ceyhun; Açıkgöz, Hakan; Korkmaz, Deniz; Budak, ÜmitAn accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy producers, exposing them to potential additional costs. Being aware of this challenge, we introduced a two-step novel deep learning method for wind power forecasting. The first stage includes processes of Variational Mode Decomposition (VMD)-based feature extraction and converting these features into images. In the second stage, an improved residual-based deep Convolutional Neural Network (CNN) was utilized to forecast wind power. Meteorological wind speed, wind direction, and wind power data, which are directly related to each other, were employed as a dataset. The combined dataset was procured from a wind farm in Turkey between January 1 and December 31, 2018. The results of the proposed method were compared with the results obtained from the state-of-the-art deep learning architectures namely SqueezeNet, GoogLeNet, ResNet-18, AlexNet, and VGG-16 as well as physical model based on available meteorological forecast data. The proposed method outperformed the other architectures and demonstrated promising results for very short-term wind power forecasting due to its competitive performance.Öğe Long Short-Term Memory Network-based Speed Estimation Model of an Asynchronous Motor(IEEE (Institute of Electrical and Electronics Engineers), 2021) Açıkgöz, Hakan; Korkmaz, DenizIn this paper, an effective deep rotor speed estimation model of an asynchronous motor is presented. The estimation model is based on the long short-term memory (LSTM) network which is one of the deep learning models. The designed model includes three main steps as the preprocessing, training of the deep speed estimation model, and evaluation of the model with testing. The dataset of the asynchronous motor model is obtained in MATLAB/Simulink environment under variable step speed references. The input parameters of the network are the dq-axis currents (id, iq) and voltages (vd, vq). The output is selected as the rotor speed (wr). The whole data is normalized to increase the estimation performance and then randomly divided into the training and validation. For the testing stage, different test data is also constructed. In the training process, the variation of the network performance is analyzed according to the neuron number increasing and optimum neuron number is achieved. The obtained results show that the proposed model is robust and efficient under the variable step speed references.Öğe A novel ship classification network with cascade deep features for line-of-sight sea data(2021) Ucar, Ferhat; Korkmaz, DenizIn ship classifcation, selecting distinctive features and designing a proper classifer are two key points of the process. As a lack of most of the studies, these two essential points are considered separately. In this study, our proposal includes joint feature extraction, selection, and classifer design framework to build a novel deep cascade network for ship classifcation. We propose a transfer learning-based deep feature extraction using cascade Convolutional Neural Network architecture to convert the input image to multi-dimensional feature maps. The distributions of the MUTual Information (MUTInf) based feature selection algorithm compose a distinctive feature set originated for a public ship imagery dataset. The dataset consists of fve specifc classes of ships most existed in the maritime domain. A quadratic kernel-based non-linear Support Vector Machine is the designed classifer. Extensive experiments on the benchmark dataset indicate that the proposed framework can integrate the optimal feature set and a well-designed classifer to increase the performance of the classifcation process in ship imagery. In the experiments, the proposed method achieves an overall accuracy of 95.06%. The ship classes are also performed high classifcation performances into cargo, military, carrier, cruise, and tanker with an accuracy of 88.26%, 98.38%, 98.38%, 98.78%, and 91.50%, respectively. In addition, MUTInf feature selection reduces the features at a rate of 50.04%. These results show that the proposed method provides the highest performance value with less number of elements and outperforms state-of-the-art methodsÖğe A Novel Short-Term Photovoltaic Power Forecasting Approach based on Deep Convolutional Neural Network(2021) Korkmaz, Deniz; Açıkgöz, Hakan; Yıldız, CeyhunIn this study, a novel photovoltaic power forecasting system that utilizes a deep Convolutional Neural Network (CNN) structure and an input signal decomposition algorithm is proposed. The proposed CNN architecture extracts deep features to forecast short-term power using transfer learning-based AlexNet. The historical power, solar radiation, wind speed, and temperature data are selected as the input. The signal decomposition algorithm called Empirical Mode Decomposition (EMD) is utilized to decompose the historical power signal into sub-components. In order to extract deep features, all input parameters are converted to 2D feature maps and feed to the input of the CNN. The experiments are realized on a grid-tied Photovoltaic Power Plant (PVPP) that has 1000 kW installed capacity located in Turkey. The experiments are performed under four weather conditions as partial cloudy, cloudy-rainy, heavy-rainy, and sunny days to show the effectiveness of the proposed method. The obtained results are compared with the benchmark regression algorithms. When the results are analyzed, the proposed method gives the highest Correlation Coefficient (R) and the lowest Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and SMAPE values under all horizons and weather conditions. For 1-h to 5-h ahead, the average R values of the proposed method are obtained as 97.28%, 95.77%, 94.49%, 93.61%, and 92.62%, respectively. The average RMSE values are observed as 4.90%, 6.30%, 7.50%, 8.00%, and 9.17% for 1-h to 5-h ahead. The experimental results confirm that the proposed method outperforms the conventional regression algorithms and reveals effective results with its competitive performance.Öğe A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images(Sakarya Üniversitesi, 2020) Uçar, Ferhat; Korkmaz, DenizShip detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, the intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship detection. In this study, we propose a Deep Convolutional Neural Network (DCNN) model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image with a deep learning model built from scratch. The designed CNN model is constructed with a plain and easy to implement form in particular to the preferred satellite image set. Visual and graphical results show that the proposed model provides an efficient detection process with an accuracy of 99.60%.Öğe SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting(Elsevier, 2021) Korkmaz, DenizPhotovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this study, a novel convolutional neural network (CNN) model, namely SolarNet, is proposed for short-term PV output power forecasting under different weather conditions and seasons. The proposed CNN model is designed as a parallel pooling structure to increase the forecasting performance. This structure consists of max-pooling and average-pooling blocks. The input parameters are the measured historical solar radiation, temperature, humidity, and active power data. The power data is decomposed into sub-components with the variational mode decomposition method and a data preprocessing and reconstruction process is utilized to obtain deep input feature maps. After input parameters are converted to hue-saturation-value (HSV) color space, the subsets feed to the input of the network. The experimental studies are performed with a case study using a 23.40 kW PVPP dataset from the Desert Knowledge Australia Solar Centre. The design CNN model is also compared with benchmark deep learning methods. In the experiments, the average correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method for 1-h different weather conditions are achieved as 0.9871, 0.3090, and 0.1750, respectively. The experimental results show that the proposed deep forecasting method has higher accuracy and stability in short-term PV power forecasting and outperforms the other deep learning methods.Öğe WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network(Elsevier, 2021) Açıkgöz, Hakan; Budak, Ümit; Korkmaz, Deniz; Yıldız, CeyhunThis paper introduces a novel deep neural network (WSFNet) to efficiently forecast multi-step ahead wind speed. WSFNet forms the basis of the stacked convolutional neural network (CNN) with dense connections of different blocks equipped with the channel attention (CA) module. Dense connections create direct transition paths between the input and all subsequent convolutional blocks. This encourages the reuse of all activations at the network input without loss of gradients in subsequent layers. The CA modules contribute significantly to the performance of the network by suppressing non-useful features extracted by each convolution block. In the proposed method, variational mode decomposition (VMD) was utilized to provide an effective preprocessing and improve the forecasting ability. The case study was conducted on publicly available data from Sotavento Galicia (SG) wind farm. In the evaluations, three variants of the proposed network were analyzed and compared with state-of-the-art deep learning methods. When the results were analyzed, the overall correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (SMAPE) were obtained as 0.9705, 0.7383, 0.5826, and 0.0466, respectively. The obtained results indicate that the proposed method achieved a competitive performance and can be effectively used for smart-grid operations.
