Person: AKGÜN, GAZİ
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AKGÜN
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GAZİ
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Publication Open Access Neural network and IoT-based test maneuver deployment for 2 DoF vehicle simulator(2023-05-15) DEMİR, UĞUR; AKGÜN, GAZİ; YILDIRIM, ALPER; AKÜNER, MUSTAFA CANER; DEMİRCİ B., DEMİR U., AKGÜN G., YILDIRIM A., AKÜNER M. C., ÖZKAN M.This paper presents the driving scenarios deployment for 2 DoF (Degree of Freedom) vehicle simulator based on IoT (Internet of Things) and Neural Network. The controller structure is chosen as Neural Network-based controller is preferred as the transferring appropriate accelerations in 3 axes in the 2 DoF manipulator evokes a nonlinear problem. Due to the microcontroller used in the vehicle simulator to perform Neural Network calculations has limited processing capacity and speed, IoT-based computing and data transferring are chosen. Firstly, an open-loop measurement is performed to identify the vehicle simulator and to generate the training data for the neural network. Thereafter the acceleration data on the axes and the control signals are logged. Secondly, the neural network training is carried out with the logged data. Finally, the trained neural network was tested with various driving maneuvers. And the measurements are evaluated.Publication Open Access Developing a wearable device for upper extremity tremors(2024-05-31) AKGÜN, GAZİ; GÜNAL, DİLEK; AKÜNER, MUSTAFA CANER; ŞEHİRLİ, ÜMİT SÜLEYMAN; Yildiz S. D., AKGÜN G., GÜNAL D., Kaplanoglu E., AKÜNER M. C., ŞEHİRLİ Ü. S.Objective: This project aims to develop a wearable device to suppress both the essential and resting tremor and investigate its effectiveness. Materials and Methods: This study details the development and assessment of a wearable device for upper extremity tremors. The wearable device underwent a comprehensive design and a prototype was produced with a 3D-printer. To refine the functionality of the prototype, a motor that mimics tremor was attached to a 3D-printed prototype. Then, the printed prototype was applied to the hand model, and tested its effectiveness for tremor suppressing. The wearable device was further investigated on patients with essential tremor and Parkinson`s disease seeking treatment at Neurology Clinics. We recorded the tremor data and processed and visualized the recorded data by using the MatLab (version R2021a, MathWorks Inc., USA) software. Results: The wearable device effectively decreased the tremors both during the simulation phase and the patient testing phase. The data from the wearable device revealed a notable decrease in the amplitude of the tremor. This decrease signifies an achievement of tremor suppression. Conclusion: The prototype of the wearable device signifies a remarkable efficacy in tremor supression. It holds promise for being a potential solution to alleviate the tremor symptoms of essential tremor and Parkinson`s disease patients.Publication Open Access An intelligent machine condition monitoring model for servo systems(2022-01-01) AKÜNER, MUSTAFA CANER; AKGÜN, GAZİ; MUTLU H., AKÜNER M. C., AKGÜN G.The installation of industrial servo systems and the determination of control parameters are limited to the skills and knowledge of the commissioner. In addition, commissioned systems are often not re-optimized if environmental influences or loads change. The goal of this research is to create an artificial neural network (ANN) model for servo systems that will keep the servo system's proportional, integral, and derivative (PID) parameters working optimally. For this process, a machine condition monitoring algorithm developed with the ANN technique, which uses the data such as actual current, torque, power, position to be obtained from the servo system on an industrial controller, for the control and rearrangement of the parameters.Publication Open Access An innovative approach to electrical motor geometry generation using machine learning and image processing techniques(2023-01-01) DEMİR, UĞUR; AKGÜN, GAZİ; AKÜNER, MUSTAFA CANER; AKGÜN, ÖMER; DEMİR U., AKGÜN G., AKÜNER M. C., Pourkarimi M., AKGÜN Ö., Akıncı T. Ç.This paper presents a methodology for generating geometries for interior permanent magnet (IPM) motors in electric vehicles (EVs) through the application of artificial intelligence (AI) and image processing (IP) techniques. Due to the implementation of green agreements and policies aimed at reducing greenhouse gas emissions, EVs have become popularity. As a consequence, the improvement studies on the powertrain and battery system of EVs are focused. Especially for the powertrain, design optimization studies of e-motor have increased in the literature. One of the most widely used e-motor topologies is interior permanent magnet (IPM) motor. However, designing the IPM motor presents a challenge due to the dynamic considerations with geometric constraints. Therefore, e-motor designers encounter challenges related to determining initial geometry and the long time of the optimization process. To address these challenges, a novel approach is proposed that leverages machine learning (ML) techniques in combination with IP to generate initial geometries and design parameters for IPM motors. The proposed approach generates images of the motor geometry and extract dimensional features from the resulting images by using artificial neural networks (ANNs). The proposed method performs an analysis of the input vectors to reduce their size using techniques such as Histogram, 2D Maximum, 2D Mean, 2D Minimum, 2D Standard Deviation, and 2D Variance to enhance feature extraction. Additionally, FFT (Fast Fourier Transform) and IFFT (Inverse Fast Fourier Transform) are used to improve the neural network process in generating the image geometry. Further, the generated image geometry is improved by applying digital filtering techniques such as Log, FFT, Log+FFT, Laplacian, Sobel, and Histogram Equalization. Finally, the trained ANNs are tested to validate the results by using Ansys RMXprt and Maxwell. Eventually, the proposed method represents an innovative solution to generating initial geometries for IPM motors in EVs that satisfies desired design requirements. This approach leverages the power of AI and image processing techniques, which could lead to significant improvements in the optimization process for IPM motors, accelerate the designer’s analysis process, and enhance the performance of EVs.Publication Open Access Feature extraction and NN-based enhanced test maneuver deployment for 2 DoF vehicle simulator(2023-01-01) DEMİR, UĞUR; AKGÜN, GAZİ; AKÜNER, MUSTAFA CANER; AKGÜN, ÖMER; DEMİR U., AKGÜN G., AKÜNER M. C., Demirci B., AKGÜN Ö., Akıncı T. Ç.This paper presents a deployment method of various test maneuver scenarios for 2 degree of freedom (2 DoF) vehicle simulator by using feature extraction and neural networks (NN). A prototype version has been set up for the 2 DoF vehicle simulator. Then, a hardware in the loop (HIL) model with 2 inputs (torque, τ1-τ2) and 3 outputs (acceleration, ax-ay-az) is created. System identification is performed to obtain the training data of NNs to be used for the deployment of test maneuvers. In the system identification process, 2 arbitrary sinusoidal torque signals (τ1-τ2) are generated by using the actuator specs of the 2 DoF vehicle simulator. By applying the generated torque signals to the actuators, acceleration (ax-ay-az) data are collected from the inertial measurement sensor (IMU) on the 2 DoF vehicle simulator. It is determined to create 3 different NN models for the obtained data. The 1st NN model is trained with 3 inputs (ax-ay-az) and 2 targets (τ1-τ2) training data. The 2nd NN model is trained with 6 inputs (amplitudes and phases of ax-ay-az) and 2 targets (τ1-τ2) training data. The input data features for the 2nd NN model is extracted by using the Fast Fourier Transform (FFT). The 3rd NN model is trained with 6 inputs (amplitudes and phases of ax-ay-az) and 4 targets (amplitudes and phases of τ1-τ2) training data. For the 3rd NN model, the features of input and target data are extracted by using the FFT. The NN training process continues until acceptable performance criteria are reached. Then, 3 NN models are run and analyzed under various test scenarios such as Double Lane Change, Constant Radius, Increase Steer, Fish Hook, Sine with Dwell and Swept Sine. Only for the 3rd NN, the actuator signals (τ1-τ2) are recomposed by applying an inverse FFT process to the 4 targets (amplitudes and phases of τ1-τ2). Finally, the reference trajectory tracking performances are evaluated by comparing the NN models that are run under the test scenarios.