WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

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Author: search.filters.author.Ergezer, HalitPublisher: search.filters.publisher.IEEE

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Now showing 1 - 3 of 3
  • Conference Object
    Citation - WoS: 2
    Kinematic Analysis and Position Control of Motor Grader Blade Mechanism for Automatic Levelling1
    (IEEE, 2022) Ergezer, Halit; Ozkan, Ekin Cansu
    In this study, mechanism analysis, which is one of the necessary steps for automatic function control in construction machines, is emphasized. Motor grader construction machine has been chosen because there are a minimal number of studies in the literature. The blade mechanism of the motor grader has high degrees of freedom; it can perform various rotations and orientations in the XYZ axis. For this mechanism, which is very challenging to control and make kinematic analysis, functions that specify the motion behavior of the cutting-edge points are obtained using the polynomial surface fitting method. PI controllers were created for the MIMO system to reduce the existing steady-state error. Tests were performed for various scenarios on the actual machine, and the results were compared.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri
    (Institute of Electrical and Electronics Engineers Inc., 2025) Calis, Ahmet Gokay; Ergezer, Halit
    In this study, the effects of different SMOTE methods on machine learning algorithms for the imbalanced epilepsy dataset were investigated. After filtering, the imbalanced dataset was balanced with 5 different SMOTE methods and classified with various machine learning algorithms. Coarse-K-Nearest Neighbor, Bagged Trees, and Artificial Neural Networks models were evaluated in epilepsy detection. The performance of these different models was compared with Matthews Correlation Coefficient (MCC) and F1 Score metrics. The results showed that the Borderline-SMOTE algorithm had the highest F1 Score and MCC values among all machine learning algorithms. © 2025 Elsevier B.V., All rights reserved.
  • Conference Object
    Optimal Fixed-Wing UAV Rendezvous Via LQR-Based Longitudinal Control
    (IEEE, 2025) Buyukekiz, Kadir Bulathan; Ergezer, Halit
    This paper proposes an optimal control-based rendezvous strategy for fixed-wing Unmanned Aerial Vehicles (UAVs) using a Linear Quadratic Regulator (LQR). The goal is precisely tracking a moving target while maintaining flight stability and avoiding predefined restricted areas. The controller optimally adjusts UAVs flight parameters to minimize trajectory errors and enhance robustness against environmental disturbances. A penalty-based method is integrated to prevent UAVs from entering restricted areas while ensuring smooth trajectory adaptation. The proposed approach has been tested in MATLAB simulations under multiple scenarios, demonstrating its effectiveness in achieving stable and efficient rendezvous maneuvers. The results confirm that LQR-based control and adaptive penalty mechanisms offer a practical solution for fixed-wing UAV operations in constrained environments.