Person: AK, AYÇA
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AK
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AYÇA
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Publication Metadata only Fiber optik uygulamaları(Proje Kitabı, 2013-12-01) AK, AYÇA; Ak A.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 Fiber optic training program with intensive experiments using both real laboratory and simulation environments(WILEY, 2019) SARIKAŞ, ALİ; Aydin, Serkan; Sarikas, Ali; Ak, Ayca; Yayla, Ayse; Kesen, Ugur; Oral, BekirThis paper highlights a fiber optic training program developed according to the occupational competencies using real and simulation platforms to train young people aged between 15 and 24. The most important objective is to overcome the shortages of fiber optic employees by providing training qualifications accredited by the Fiber Optic Association. This training program was designed on three levels, and participants were tested at the end of each level. Successful participants continued with a higher level of training. Theoretical knowledge was given to the participants at the first two levels and extensive practical applications were done. At the third level, computer networks trainings were provided to identify the much more fiber optic network modules by using simulation software tool. The training program includes installation of DVB-X (Digital Video Broadcast - satellite, cable) and the FFT-X (fiber-to-the - home, building, curb) devices that have a fiber optic cable infrastructure, and point-to-point line measurements. This training program differs from similar programs due to the inclusion of effective real laboratory, simulation platform, and field practices. It is significantly found that this training program supported by more extensive real experiments and simulations besides of theoretical education increases the technical qualifications and satisfaction ratio of the participants.Publication Open Access Visual Servoing Application for Inverse Kinematics of Robotic Arm Using Artificial Neural Networks(NATL INST R&D INFORMATICS-ICI, 2019-01-07) AK, AYÇA; Ak, Ayca; Topuz, Vedat; Ersan, EmregulThis paper presents novel approach for a visual servoing application of six axis robotic arm. Basic image-processing techniques were used for object recognition and position determination of robotic arm. The inverse kinematics solution of the robot arm was performed with artificial neural networks. Afterwards the robot's inverse kinematics solution was completed, the determined joint-angle values were used to control the robot arm. Performance of radial basis function network (RBF) and multilayer perceptron (MLP) were also compared.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 Field Programmable Gate Arrays Based Real Time Robot Arm Inverse Kinematic Calculations and Visual Servoing(ISTANBUL UNIV, FAC ENGINEERING, 2018-07-29) AK, AYÇA; Celik, Baris; Ak, Ayca; Topuz, VedatReliability and precision are very important in space, medical, and industrial robot control applications. Recently, researchers have tried to increase the reliability and precision of the robot control implementations. High precision calculation of inverse kinematic color based object recognition, and parallel robot control based on field programmable gate arrays (FPGA) are combined in the proposed system. The precision of the inverse kinematic solution is improved using the coordinate rotation digital computer (CORDIC) algorithm based on double precision floating point number format. Red, green, and blue (RGB) color space is converted to hue saturation value (HSV) color space, which is more convenient for recognizing the object in different illuminations. Moreover, to realize a smooth operation of the robot arm, a parallel pulse width modulation (PWM) generator is designed. All applications are simulated, synthesized, and loaded in a single FPGA chip, so that the reliability requirement is met. The proposed method was tested with different objects, and the results prove that the proposed inverse kinematic calculations have high precision and the color based object recognition is quite successful in finding coordinates of the objects.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 Controlling of Five Axis Manipulator with Turkish Voice Commands Using Microcontrollers(2018-12-31) AK, AYÇA; Ayça AK;Vedat TOPUZ;Musa AYDINThe interaction between human beings and machines has been increasing in conjunction with the development of computer technology.Controlling a system with voice-based commands is one of the most popular applications in this area. In this study, the main goal is to builda system, which is able to control a robotic arm comprised of five controllable axes with certain voice commands.The robotic arm controlling process starts with the matching of sounds taken from the user. Then sounds processed in voice recognitioncard. After the command recognized by voice recognition card, then index number is sent to a microcontroller. Consequently, this operationprovides a communication between voice recognition module and servo motor drive card. Finally, the microcontroller calculates therequired angles by using the data provided by the previous process and sends this data to the servo motor drive card in order to realize therobotic arm action.Publication Open Access Development of a Remote Laboratory Infrastructure and LMS for Mechatronics Distance Education(2018-04-15) AK, AYÇA; Ak, Ayça; Topuz, Vedat; Altıkardeş, Aysun; Oral, BekirPublication Metadata only ROBOT TRAJECTORY TRACKING WITH ADAPTIVE RBFNN-BASED FUZZY SLIDING MODE CONTROL(KAUNAS UNIV TECHNOLOGY, 2011) AK, AYÇA; Ak, Ayca Gokhan; Cansever, Galip; Delibasi, AkinDue to computational burden and dynamic uncertainty, the classical model-based control approaches are hard to be implemented in the multivariable robotic systems. In this paper, a model-free fuzzy sliding mode control based on neural network is proposed. In classical sliding mode controllers, system dynamics and system parameters are required to compute the equivalent control. In Radial Basis Function Neural Network (RBFNN) based fuzzy sliding mode control, a RBFNN is developed to mimic the equivalent control law in the Sliding Mode Control (SMC). The weights of the RBFNN are changed for the system state to hit the sliding surface and slide along it with an adaptive algorithm. The initial weights of the RBFNN are set to zero and then tuned online, no supervised learning procedures are needed. In the proposed method, by introducing the fuzzy concept to the sliding mode and fuzzifying the sliding surface, the chattering can be alleviated. The proposed method is implemented on industrial robot (Manutec-r15) and compared with a PID controller. Experimental studies carried out have shown that this approach is a good candidate for trajectory tracking applications of industrial robot.