Person: AK, AYÇA
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AK
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AYÇA
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Publication Metadata only SMC controller design for DC motor speed control applications and performance comparison with FLC, PID and PI controllers(Springer, 2023-01-01) AK, AYÇA; OYMAN SERTELLER, NECİBE FÜSUN; Rahmatullah R., Ak A., Oyman Serteller N. F.Publication Metadata only Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator(ELSEVIER SCI LTD, 2022) AK, AYÇA; Ak, Ayca; Topuz, Vedat; Midi, IpekControlling of a robotic arm using a brain-computer interface (BCI) is one of the most impressive applications. In this study, a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for BCI. EEG signals are divided into three secondary tables, which were converted into spectrogram images. After applying the spectrogram method, the obtained images are divided into folder structures and deep learning is performed. In the deep learning stage, 400 images are obtained for each task as input to the Goo-gLeNet. After the deep learning completed, the presented system has been tested to imagine up, down, left and right movement to control the movement of the robot arm. It is observed that the robot arm performs the desired movement over 90% accuracy.Publication Open Access Design of sliding mode control using SVPWM modulation method for speed control of induction motor(2023-01-01) AK, AYÇA; OYMAN SERTELLER, NECİBE FÜSUN; Rahmatullah R., Ak A., Oyman Serteller N. F.The sliding mode control method is a highly accurate and easy-to-implement approach that can be effectively utilized in the control of high-dimensional nonlinear systems that operate under uncertain conditions. In this study based on Matlab/Simulink, a Proportional-Integral-Integral Sliding Mode Control (PI-ISMC) method has been developed to control the mechanical speed of a three-phase squirrel cage induction motor. The modeling of the induction motor and the design of the proposed controller have been conducted in the qd0 reference frame. The asymptotic speed tracking under uncertainty and different loading conditions has been ensured by tuning the parameters of the PI-ISMC controller. Additionally, field-oriented control (FOC) with space vector modulation has been applied to the same motor to evaluate the performance of the sliding mode control topology in induction motor control, and its performance has been compared with the sliding mode control methodPublication Open Access Integral fuzzy sliding mode controller for hydraulic system using neural network modelling(2023-08-01) AK, AYÇA; AK A., Yılmaz E., Katrancıoğlu S.In this paper, a hydraulic motor controller is designed with a fuzzy supported integral sliding mode algorithm. The hydraulic system used in the study was modeled using artificial neural networks. Ability of handling nonlinearity of systems makes sliding mode controller to be a good choose for this system. It is thought that the robustness of the system against uncertainties can be achieved with the help of an integral sliding mode controller. The basic concept of the suggested control method is to use fuzzy logic for adaptation of the integral sliding mode control switching gain. Such adjustment reduces the chattering that is the most problem of classical sliding mode control. The equivalent control is computed with utilizing the radial basis function neural network. The simulation results of the presented method are compared with the results of the PID controller whose parameters were obtained by means of a genetic algorithm (GA) and particle swarm optimization (PSO). It proved that it is more efficient to control the hydraulic system with integral fuzzy sliding mode control using neural network.Publication Metadata only SMC controller design for DC motor speed control applications and performance comparison with FLC, PID and PI controllers(2023-01-01) AK, AYÇA; Rahmatullah R., AK A., serteller N. F. O.Sliding Mode Control (SMC), which is built on the variable structure control (VSC) algorithm, is a robust and non-linear control method that can provide the desired dynamic behaviour for the system to be controlled despite external and internal disturbances and uncertainties. The SMC method can be successfully implemented in the control of high-dimensional nonlinear systems operating under uncertain conditions due to its high accuracy and simplicity of application. In this MATLAB/Simulink based study; the SMC method is applied to the speed control of a DC motor. For this purpose, firstly, the dynamic model of DC motor and the mathematical model of the SMC have been designed and transferred to the Simulink environment. The performance of the SMC system has been examined under different loading conditions applied to the motor. In addition, the effects of changing the SMC parameters on the sliding surface, chattering and motor dynamic behaviours have been investigated. In order to evaluate the success of the SMC topology in DC motor control application, Fuzzy Logic Control, PID and PI control methods were applied on the same motor and their performances were compared with the SMC method.Publication Open Access Paralyzed patients‑oriented electroencephalogram signals processing using convolutional neural network through python(2022-12-01) TOPUZ, VEDAT; AK, AYÇA; Topuz V., Ak A.Aim: Some of the systems that use brain–computer interfaces (BCIs) that translate brain activity patterns into commands for an interactive application make use of samples produced by motor imagery. This study focuses on processing electroencephalogram (EEG) signals using convolutional neural network (CNN). It is aimed to analyze EEG signals using Python, convert data to spectrogram, and classify them with CNN in this article. Materials and Methods: EEG data used were sampled at a sampling frequency of 128 Hz, in the range of 0.5–50 Hz. The EEG file is processed using Python programming language. Spectrogram images of the channels were obtained with the Python YASA library. Results: The success of the CNN model applied to dataset was found to be 89.58%. Conclusion: EEG signals make it possible to detect diseases using various machine learning methods. Deep learning-based CNN algorithms can also be used for this purpose.Publication Metadata only İnme hastaları için eeg si̇nyalleri̇ ile kontrol edi̇len beyi̇n bi̇lgi̇sayar arayüzü geli̇şti̇rme(2023-12-18) AK, AYÇA; TOPUZ, VEDAT; MİDİ, İPEK; Ersoy S. D., Ak A., Topuz V., Yardımcı G., Midi İ.