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Öğe An Approach for DC Motor Speed Control with Off-Policy Reinforcement Learning Method(2023) Tufenkci, Sevilay; Kavuran, Gürkan; Yeroglu, CelaleddinIntegration of self-learning mechanisms with control systems is frequently encountered in the literature due to the development of autonomous systems. This paper proposes a tuning method of PI controllers using a deep reinforcement learning algorithm, which is known as self-learning structure. The coefficients of the PI controller, which is used to control a DC motors, are determined. The proposed method aims to adjust the voltage value applied to the input of the DC motor to reach the desired speed with the tuned PI controller using the twin- delayed deep deterministic policy gradient (TD3) reinforcement learning algorithm. The Kp and Ki coefficients of the PI controller are taken as the absolute values of the neural network weights, which are driven by Gradient descent optimization to positive values with a fully connected layer. The proposed tuning method has been shown to provide a higher gain margin and a more optimal solution.Öğe Fine-Tuning of Feedback Gain Control for Hover Quad Copter Rotors by Stochastic Optimization Methods(Springer, 2020) Ateş, Abdullah; Baykant Alagöz, Barış; Kavuran, Gürkan; Yeroğlu, CelaleddinThree degree of freedom (3 DOF) Hover Quad Copter (HQC) platforms are implemented for various missions in diverse scales from the micro to macro platforms. As HQC platforms scale down, micro platform requires rather robust and effective control techniques. This study investigates applicability of some stochastic optimization methods for tuning feedback gain control of HQC rotors and compares optimization results with results of linear quadratic regulator (LQR) method that has been widely used analytical method for optimal feedback gain control of HQCs. This study considers the utilization of two stochastic methods for tuning of HQCs. These methods are stochastic multi-parameter divergence optimization method (SMDO) and discrete stochastic optimization method (DSO). These methods are employed to optimize feedback gain coefficients of an experimental HQC test platform. Simulation and experimental results of SMDO and DSO methods are reported and compared with results of LQR method.Öğe MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net(Elsevier, 2022) Kavuran, Gürkan; İn, Erdal; Altıntop Geçkil, Ayşegül; Şahin, Mahmut; Kırıcı Berber, NurcanCOVID-19PneumoniaArtificial intelligence (AI)Deep learningComputed tomography (CT)Öğe On the modeling of the multi-segment capacitance: a fractional-order model and Ag-doped SnO2 electrode fabrication(2022) Kavuran, Gürkan; Gurgenç, Turan; Özkaynak, FatihThis study proposes a methodology of electrochemical capacitor modeling via fractional-order impedance equation for porous electrodes fabricated with pure and Ag-doped SnO2 nanoparticles. It was carried out to prove the assumption that fractional-order integrodifferential expressions better model the various real systems. Firstly, the pure and different amounts of silver (Ag)-doped tin oxide (SnO2) nanoparticles were produced using the hydrothermal method. Tin (II) chloride dihydrate (SnCl2·2H2O) was used as an Sn source and (AgNO3) as an Ag source. Hydrothermal synthesis was completed at 200 °C for 24 h. The synthesized particles were calcined at 600 °C for 2 h. All of the structural and morphological properties were investigated by FT-IR, XRD, FE-SEM, and EDX. It has been observed that the hydrothermal method successfully produced nano-SnO2 particles without and with Ag dopant. As a result of the applied procedure, the structural properties of SnO2 nanoparticles, such as physical shape, were changed from spherical-like to nano-sheet with the Ag doping. Next, the nanopowders were coated on AZ31 magnesium sheets. Electrochemical impedance spectroscopy measurements were examined to determine the capacitance of EC materials with Ag-doped SnO2 nanoparticles. Finally, using the multi-objective cost function, the experimentally measured real and imaginary impedance parts are fitted to the proposed fractional-order model by the particle swarm optimization algorithm. It has been proven that fractional-order modeling enables finding the electrical parameters and properties of EC with higher accuracy. Furthermore, the Ag-doped SnO2 electrode can significantly improve electrical performance because of the increase in conductivity. The total capacitance gets increased by 10.788% for 7% Ag-doped SnO2 against pure SnO2.Öğe Optimal PI Kontrolör Tasarımı için Üçgenler Ağında Lineer Enterpolasyon Yöntemiyle Kararlılık Sınır Yüzeyinin Oluşturulması(Bitlis Eren Üniversitesi, 2020) Kavuran, GürkanBu çalışmada, PI parametrelerinin grafiksel olarak hesaplanması için geliştirilen kararlılık sınır eğrisi kullanılarak, yeni bir yaklaşım önerilmiştir. Geleneksel kararlılık sınır eğrisi, kapalı çevrim sistemin karakteristik polinomu kullanılarak, kontrolör parametrelerinin belirli bir frekans aralığında birbirine göre çizimiyle elde edilir. Kararlılık sınır eğrisi altında kalan bölgedeki herhangi kp ve ki değerinin sistemi kararlı yaptığı bilinmektedir. Ancak hatanın değişimine göre hangi parametrelerin optimal sonuç verdiği kesin değildir. KSE altında kalan her nokta belirli bir frekans aralığında dağınık veri enterpolasyon yöntemine göre belirlenerek, 3 boyutlu kararlılık sınır yüzeyi (KSY) oluşturulmuştur. Çoğunlukla sistem kararlılığını garanti eden bu noktalar kullanılarak, kararlı kp ve ki parametre havuzu oluşturulmuştur. Havuzdaki her bir kp ve ki değerinin birbiriyle olan kombinasyonu kullanılarak, ITAE kriterine göre referans girdi ile sistem çıkışı arasındaki farkı minimize eden optimal PI parametreleri elde edilmiştir. Böylece hem kararlılık hem de optimallik sağlanmıştır. Benzetim çalışmalarının yanı sıra, çift rotorlu model helikopter sistemi üzerinde önerilen yöntemin geçerliliği test edilmiştir.Öğe Prediction of the optimal FSW process parameters for joints using machine learning techniques(Walter de Gruyter GmbH, 2021) Sarsilmaz, Furkan; Kavuran, GürkanIn this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.Öğe SEM-Net: Deep features selections with Binary Particle Swarm Optimization Method for classification of scanning electron microscope images(Elsevier, 2021) Kavuran, GürkanMaterials Science is increasingly handling artificial intelligence methods to address the complexity in the field of everyday life necessities. Researchers in both academia and industry are interested in imaging techniques used in the characterization of nanomaterial with designed properties to meet the needs of applications in the literature. However, the increase in image size and complexity in its content restricts the use of traditional methods. Recent advances in machine learning have been used to benefit computers' potential to make sense of these images. The approach proposed in this paper aims for the feature reduction with the Binary Particle Swarm Optimization method to execute the classification process on SEM images by concatenating the deeper layers of pre-trained CNN models AlexNet and ResNet-50. The feature vectors were used as input to support vector machine classifier (SVMC) after dimension reduction to obtain the final model. Finally, the trained model's performance was tested using SEM images of Ag-doped SnO2 nanoparticles, which were prepared by the author using the low-temperature hydrothermal method. To the best of the author knowledge, these images were not available in the databases. The best accuracy value was observed with 3112 features for the SEM dataset with optimized vectors as 99.3 %. An example was illustrated where the feature selection with the BPSO technique could provide novel insight into nanoscience research and test the model with the SEM images of Ag-doped SnO2 particles that are obtained by the hydrothermal method.Öğe When machine learning meets fractional-order chaotic signals: detecting dynamical variations(Elsevier, 2022) Kavuran, GürkanThe challenge of classifying multivariate time series generated by discrete and continuous dynamical systems according to their chaotic or non-chaotic behavior has been studied extensively in the literature. The examination of noise or the variation of variables that affect a dynamic system's chaoticity will not be beneficial in analyzing structures employing random number generators (RNG) that are already assured to be chaotic. However, detecting the structural changes and their time intervals in deterministic systems with proven chaoticity can contribute to the literature in encryption applications. Machine Learning algorithms provide flexible possibilities to analyze and predict manipulations that may occur in the dynamics of chaotic and complex systems. This study proposes a deep Long-Short-Term-Memory (LSTM) network with a classification process to predict dynamical changes in a fractional-order chaotic (FOC) system. First, the appropriate system parameters are calculated to satisfy the chaotic behavior in the fractional-order Chen system. The predictive-corrective Adams-Bashforth-Moulton algorithm is used to simulate the FOC Chen system in the time domain. The Lyapunov exponents of the system were obtained according to the Wolf method. Next, three different scenarios have been designed to test and demonstrate the effectiveness of the proposed method. Synthetic FOC signals obtained after sub-sampling and statistical feature extraction processes fed the input of the deep bidirectional LSTM (BiLSTM) network to perform the training and testing process. The classification performance for "q" and "c" classes reaches 100% with the proposed model. The overall average testing accuracy, sensitivity, specificity, precision, F1 score and MCC are 98%, 98%, 99.3%, 98.1%, 98%, and 97.3%, respectively. Our results demonstrate the utility of using a deep BiLSTM network for detecting dynamical variations in nonlinear FOC systems.
