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AKGÜN, GAZİ

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AKGÜN

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2022 - 20234
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Author: YILDIRIM, ALPER ×

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Now showing 1 - 4 of 4
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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen 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.
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    PublicationOpen 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.