Person: AKGÜN, GAZİ
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AKGÜN
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GAZİ
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Publication Open Access Comparative design improvement of the growing rod for the scoliosis treatment considering the mechanical complications(2023-01-01) AKGÜN, GAZİ; YILDIRIM, ALPER; DEMİR, UĞUR; DEMİR U., AKGÜN G., KOCAOĞLU S., Okay E., Heydar A., AKDOĞAN E., YILDIRIM A., Yazi S., Demirci B., Kaplanoglu E.In this study, the focus is on an implant used in the treatment of early-onset scoliosis called magnetically controlled growing rods (MCGR). The primary goal of the study is to address and propose solutions for the mechanical problems reported in the literature concerning MCGR. The problems of the MCGR are mainly due to excessive stress and mechanical bearing problems. Therefore, an MCGR removed from a patient is teardown and geometrically modeled. Then, eleven design parameters are determined on the MCGR for the mechanical problems experienced and these are evaluated by mechanical analysis over 14 control points. In this study, analysis processes are carried out with L12 orthogonal array for eleven design parameters and 2 levels using Taguchi’s experimental design method (DoE). With the obtained data by analyzing the experiments in L12, the fitness functions depending on the design parameters are created for 14 control points. Since the problem is multi-objective, a non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO) are used to minimize stress and displacement in existing mechanical problems using fitness functions. The obtained design models from NSGA II and MOPSO are analyzed and evaluated in comparison with the existing mechanical model obtained through pre-optimization teardown study of MCGR.Publication Open Access Investigation of UWB-IMU sensor fusion for indoor navigation with DoE(2023-01-01) DEMİR, UĞUR; AKGÜN, GAZİ; YILDIRIM, ALPER; Durmus S., DEMİR U., AKGÜN G., YILDIRIM A.This study presents an evaluation of the optimal parameter configuration for Ultra-Wide Band (UWB) - Inertial Measurement Units - (IMU) based sensor fusion for indoor localization in Non-Line-of-Sight (NLOS) environments. The study employs the least squares method to predict position using UWB technology. Subsequently, sensor fusion techniques combining UWB and IMU are employed, utilizing the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms to enhance position estimation. To mitigate the effects of noise in IMU data, a high-pass filter is applied before feeding the data into the EKF and UKF. The experimental findings are then evaluated using Design of Experiment (DoE) techniques, and the optimal parameter configurations are analysed using linear regression. This study provides insight into the parameter settings that yield improved accuracy and robustness in UWB-IMU sensor fusion for indoor localization in NLOS scenarios.Publication Open Access FFT Analysis and Motion Classification of EMG Signals(2022-10-01) AKGÜN, GAZİ; DEMİR, UĞUR; AKGÜN G., DEMİR U.Bu çalışmada EMG sinyallerinin frekans analizi yapılarak elde edilen veriler ile hareket sınıflandırması yapmak amaçlanmıştır. Üç kanaldan toplanan EMG sinyalleri uygun pencerelere ayrılarak her bir pencereye” hilbert “ zarflama yöntemi uygulanmış ve FFT katsayıları hesaplanmıştır. Kaydedilen EMG sinyallerinin frekans spektrumları incelenmiştir. Bu katsayıları ile bir sınıflandırma algoritmasında kullanmak amacıyla her bir pencerenin ağırlıklı frekans bileşeni hesaplanmıştır. Elde edilen veriler YSA (Yapay sinir Ağları) algoritmasının eğitilmesi amacıyla kullanılmış ve bu işlem EMG sinyallerinin sınıflandırılması amacıyla kullanılmıştır. Sınıflandırma işlemi sonucunda özellikle aynı kas gruplarındaki kasılma kuvvetleri ile birbirinden ayırt edilebilen hareketlerin yalnızca frekans domeninde değil zaman domeninde de incelenmesi gerektiği sonucuna varılmıştır.Publication Open Access EMG sinyallerinin derin öğrenme ile hareket sınıflandırması(2022-09-18) AKGÜN, GAZİ; YILDIRIM, ALPER; DEMİR, UĞUR; KAPLANOĞLU, ERKAN; Akgün G., Yıldırım A., Demir U., Kaplanoğlu E.Bu çalışmada EMG sinyalleri üzerinde öznitelikler hesaplanmıştır. Bu öznitelikler ile el hareketlerini sınıflandırmak için derin öğrenme algoritmaları kullanılmıştır. Bir zaman serisi olarak toplanan EMG sinyalleri üzerinde zaman alanında hesaplanan öznitelik vektörleri belirli boyutlarda simetrik matrisler olarak kaydedilmiştir. Yeniden oluşturulan ve resim dosyası formatında kaydedilen veri seti ile Evrişimsel Sinir Ağı eğitilmiştir. Bu eğitim sonucunda tüm veriler ile %93, test verileri ile %79 başarı ile hareket sınıflandırması gerçekleştirilmiştir.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 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.