WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Conference Object Citation - WoS: 2Kinematic Analysis and Position Control of Motor Grader Blade Mechanism for Automatic Levelling1(IEEE, 2022) Ergezer, Halit; Ozkan, Ekin CansuIn 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: 1Citation - Scopus: 1Dengesiz Epilepsi Veri Seti İçin Sınıflandırmada Farklı SMOTE Yöntemlerinin Etkileri(Institute of Electrical and Electronics Engineers Inc., 2025) Calis, Ahmet Gokay; Ergezer, HalitIn 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, HalitThis 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.Conference Object Formation Control of Fixed-Wing Uavs Using Mpc: Effect of Vehicle Speed(Ieee, 2024) Ozcelik, Ozge Kartal; Ergezer, HalitAs UAV technology has progressed, its applications have expanded across various sectors such as Surveillance, Military, Maintenance, and Delivery. Due to their cost-effectiveness and safety in operations, UAVs have gained rapid popularity in these domains. Scenarios demanding the coordinated flight of multiple UAVs have become prevalent, leading to various formation types and control strategies tailored to specific use cases. Among these control mechanisms, Model Predictive Control (MPC) has found application in UAV formation flight. This study focuses on the development and implementation of MPC for the formation flight of fixed-wing UAVs. Experiments were conducted utilizing a setup involving three UAVs, elucidating the functionality of MPC and examining the impact of variations in the speed parameter on MPC performance.Article Citation - WoS: 2Citation - Scopus: 2Development of Air-To Engagement Analysis Model of Fighter Aircrafts(Gazi Univ, Fac Engineering Architecture, 2022) Bektas, Almila; Ergezer, Halit; Erdogan, SinemIn operational analysis studies; it is possible to model and simulate at an engineering level, engagement level, task level and campaign forces level. In this study, modelling and simulation studies are performed in engagement-level allowing the analysis of air-to-ground engagement effectiveness of fighter aircraft according to the operational environment. The operating environment of the combat aircraft, which provides survivability analysis based on low visibility and electronic mixing capabilities, is created. The search radar and tracking radar models for ground-to-air threats have been designed in accordance with the engagement level. The dynamic model of the fighter aircraft and the ground-to-air missile have been modelled using pseudo 5 degree-of-freedom. Modelling has been carried out to allow the use of changes in the Radar Crosssectional Area (RCS), which is one of the most important factors affecting the survivability of the aircraft, with respect to azimuth and elevation angles. The Radio Frequency (RF) jamming capability of the fighter aircraft has also been modelled in accordance with the engagement level. The results of the generic scenarios for the analysis of the effect of these models' parameters on the survivability of fighter aircraft have been presented.Article Citation - WoS: 4Citation - Scopus: 4Window Length Insensitive Real-Time Emg Hand Gesture Classification Using Entropy Calculated From Globally Parsed Histograms(Sage Publications Ltd, 2023) Alguner, Ayber Eray; Ergezer, HalitElectromyography (EMG) signal classification is vital to diagnose musculoskeletal abnormalities and control devices by motion intention detection. Machine learning assists both areas by classifying conditions or motion intentions. This paper proposes a novel window length insensitive EMG classification method utilizing the Entropy feature. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. Additionally, the entropy feature can classify feature vectors of different sliding window lengths without including them in the training data. Many kinds of entropy feature succeeded in electroencephalography (EEG) and electrocardiography (ECG) classification research. However, to the best of our knowledge, the Entropy Feature proposed by Shannon stays untested for EMG classification to this day. All the machine learning models are tested on datasets NinaPro DB5 and the newly collected SingleMyo. As an initial analysis to test the entropy feature, classic Machine Learning (ML) models are trained on the NinaPro DB5 dataset. This stage showed that except for the K Nearest Neighbor (kNN) with high inference time, Support Vector Machines (SVM) gave the best validation accuracy. Later, SVM models trained with feature vectors created by 1 s (200 samples) sliding windows are tested on feature vectors created by 250 ms (50 samples) to 1500 ms (300 samples) sliding windows. This experiment resulted in slight accuracy differences through changing window length, indicating that the Entropy feature is insensitive to this parameter. Lastly, Locally Parsed Histogram (LPH), typical in standard entropy functions, makes learning hard for ML methods. Globally Parsed Histogram (GPH) was proposed, and classification accuracy increased from 60.35% to 89.06% while window length insensitivity is preserved. This study shows that Shannon's entropy is a compelling feature with low window length sensitivity for EMG hand gesture classification. The effect of the GPH approach against an easy-to-make mistake LPH is shown. A real-time classification algorithm for the entropy features is tested on the newly created SingleMyo dataset.Article Citation - WoS: 3Citation - Scopus: 9Two Majority Voting Classifiers Applied To Heart Disease Prediction(Mdpi, 2023) Karadeniz, Talha; Maras, Hadi Hakan; Tokdemir, Gul; Ergezer, HalitTwo novel methods for heart disease prediction, which use the kurtosis of the features and the Maxwell-Boltzmann distribution, are presented. A Majority Voting approach is applied, and two base classifiers are derived through statistical weight calculation. First, exploitation of attribute kurtosis and attribute Kolmogorov-Smirnov test (KS test) result is done by plugging the base categorizer into a Bagging Classifier. Second, fitting Maxwell random variables to the components and summating KS statistics are used for weight assignment. We have compared state-of-the-art methods to the proposed classifiers and reported the results. According to the findings, our Gaussian distribution and kurtosis-based Majority Voting Bagging Classifier (GKMVB) and Maxwell Distribution-based Majority Voting Bagging Classifier (MKMVB) outperform SVM, ANN, and Naive Bayes algorithms. In this context, which also indicates, especially when we consider that the KS test and kurtosis hack is intuitive, that the proposed routine is promising. Following the state-of-the-art, the experiments were conducted on two well-known datasets of Heart Disease Prediction, namely Statlog, and Spectf. A comparison of Optimized Precision is made to prove the effectiveness of the methods: the newly proposed methods attained 85.6 and 81.0 for Statlog and Spectf, respectively (while the state of the heart attained 83.5 and 71.6, respectively). We claim that the Majority Voting family of classifiers is still open to new developments through appropriate weight assignment. This claim is obvious, especially when its simple structure is fused with the Ensemble Methods' generalization ability and success.Article Citation - WoS: 2Citation - Scopus: 3Control Structure Design With Constraints for a Slung Load Quadrotor System(Sage Publications Ltd, 2024) Leblebicioglu, Kemal; Ergezer, HalitWe propose a control structure for a quadrotor carrying a slung load with swing-angle constraints. This quadrotor is supposed to pass through the waypoints at specified speeds. First, a cascaded PID autopilot is designed, which adaptively gives attention to position and speed requirements as a function of their errors. Its parameters are found from an optimization problem solved using the PSO algorithm. Second, this controller's performance is improved by adding the Complementary Controller employing an ANN. 5. Training data for the ANN is created by solving optimal control problems. The ANN is activated when the swing angle constraint is about to be violated. It is trained using optimal control values corresponding to the cases where the swing angle falls in a particular band about the upper swing angle constraint. Simulations are performed in a MATLAB environment. Finally, some of the simulation results are validated on a physical system.Conference Object Citation - WoS: 1Evaluation of Features Used in Electromyography Classification(Ieee, 2021) Ergezer, Halit; Alguner, Ayber ErayClassification of electromyography (EMG) signals using machine learning has been studied for a long time. Today, this classification is tried to be made more accurate, fast and applicable by using the methods developed. However, beside this effort, it is suspected that researchers are using features without taking into account the effects on the classification performance, but often by influence of other researches. From this point of view, the effects of some features used in studies published in recent years on classification performance were tested and the results obtained were shared. In the experiments performed using a common method support vector machine (SVM), it was found that increasing the number of features does not always provide an increase in performance, even in some cases, it causes a decrease in accuracy rates.Conference Object Design and Implementation of Visual Simultaneous Localization and Mapping (Vslam) Navigation System(Ieee, 2021) Ergezer, Halit; Bekcan, ArdaIt is very important to guess the location of the redetected objects and loop closures with the visual simultaneous localization and mapping system (VSLAM), one of the biggest problems of a mobile robot. VSLAM makes it possible to eliminate and/or reduce these applications' errors and realize or improve the robot's direction and position correctly by creating a map of the environment. This study aims to achieve an autonomous indoor/outdoor navigation of a ground robot using VSLAM algorithm in an unknown environment using a monocular camera. In this context, the theoretical information was tested in real-world conditions. Performance of localization and loop closing were compared based on the results obtained by experiments