Mekatronik Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/255

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Citation Count: Özbilge, Kubilay; Ergezer, Halit. "Çekilebilir Sahte Hedef Etkinliğinin Modelleme Ve Simülasyon İle Analizi", 8. Ulusal Savunma Uygulamaları Modelleme ve Simülasyon Konferansı, Ankara 19-20 Kasım 2019, pp. 152-161.
Çekilebilir Sahte Hedef Etkinliğinin Modelleme Ve Simülasyon İle Analizi
(2019) Özbilge, Kubilay; Ergezer, Halit; 293396; 293396
Sahte hedefler, uçağın savaş alanında hayatta kalmasını artırmak için radar güdümlü füzeden korumak için kullanılan bir savunma yöntemlerindendir. Bu çalışmada savaş uçaklarında kullanılan çekilebilir aktif sahte hedeflerin uçağın hayatta kalabilirliğine etkisi, uçağın çekilebilir sahte hedef kullandığı ve kullanmadığı durumlar için vurulma olasılıkları ve füzenin kaçırma uzaklığı değerleri hesaplanarak analiz edilmiştir. Çalışmamızda açık kaynaktan elde edilen veriler kullanılmıştır. Jenerik yapıda kurgulanan savaş uçağı modeli, çekilebilir sahte hedef modeli ve radar modeli ile elde edilen veriler sunulmuştur. Benzetim sonuçları çekilebilir aktif sahte hedef kullanımının savaş uçaklarına özellikle angajmanın son safhasında koruma sağladığını ortaya koymaktadır.
Citation Count: Can, S.E.; Leblebicioğlu, M.k.; Ergezer, H. (2022). "Çoklu ˙IHA Kullanımı ile Kabloya Asılı Yük TaşınımıTransportation of Cable Suspended Slung Load System Using Multiple UAVs", Fırat Üniversitesi Uzay ve Savunma Teknolojileri Dergisi , Vol.1, No.1, pp.435-440.
Çoklu ˙IHA Kullanımı ile Kabloya Asılı Yük Ta¸sınımı Transportation of Cable Suspended Slung Load System Using Multiple UAVs
(2022) Can, Süleyman Emre; Leblebicioğlu, M. Kemal; Ergezer, Halit; 293396
Bu çalı¸smada, kablolar ile asılı esnemeyen bir yükün ortakla¸sa ta¸sınımı için kontrolcü tasarımı tanıtılmı¸stır. Yük, birden çok insansız hava aracına (˙IHA) yükün kablo ile baglanması ˘ ile ta¸sınmakta olup, istenen üç boyutlu rota üzerinde hareket etmesi saglanmaktadır. Sistemde yük lider olarak alınmaktadır. ˘ Hiyerar¸sik ve merkezcil bir yapıda kontrol edilmektedir. Ta¸sıma görevi, bir optimizasyon problemi olarak ele alınmı¸stır. Sistemi istenen rotada hareket ettirebilmek için bir otopilot algoritması tasarlanmı¸s olup PD yapıları kullanılarak olu¸sturulmu¸stur. Pozisyon kontrolcüsünün çıktısı kontrol atama matrisi tarafından ele alınmaktadır ve uygun kuvvet dagılımını sistemin istenen ˘ davranı¸slarına uygun olacak ¸sekilde türetmektedir. Kontrolcü performansları, takip edilmesi istenen bir rota üzerinde test edilmektedir.
Citation Count: Gündoğan, Boran; Ergezer, Halit (2023). Comprehensive Comparison of Various Machine Learning Algorithms for RF Fingerprints Classification. 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, Sivas, 11 October 2023through 13 October 2023.
Comprehensive Comparison of Various Machine Learning Algorithms for RF Fingerprints Classification
(2023) Gündoğan, Boran; Ergezer, Halit; 293396
In these days, the use of drones has become quite common. Remote controls can do the control of these drones with RF signals. It is important to prevent security vulnerabilities caused by using drones in our daily lives. A complex dataset was created by extracting the characteristics of the RF signals and preprocessing them. To solve this complex data set and problem, the application of models including Support Vector Machine (SVM), Random Forest, Decision Tree, Gradient Boosting, XGBoost and Neural Network (NN) models, including various ML models and comparison of optimization studies of these applied models are examined in this article. In addition, a wide range of studies was carried out to compare ML models, including comparison metrics such as Accuracy, Precision, Recall, Mean Squared Error (MSE), F1 Score, $R^{2}$ and Training Time. In line with these results, the highest score was obtained in the $\mathrm{R}^{2}$ comparison metric (97%) in the Neural Network (NN). Compared to the others, the results of Neural Network (NN) were more successful, but the Training Time (245 sec) in the Neural Network (NN) method is by far more than the other ML methods, which shows us that the NN method requires a very high computing process. As a result of the comparison, another outstanding Ensemble-based ML method is Decision Tree. This is because besides the very low Training Time $(5\sec)$, it has managed to be the 2nd ML algorithm with the highest $\mathrm{R}^{2}$ score (96%). Apart from these, among other ML methods, SVM performed slightly less well $(\mathrm{R}^{2}$ 91%) in solving this complex problem. The advanced Gradient Method (95%) and XGBoost (96%), which also have the Ensemble structure, showed a head-to-head performance regarding $\mathrm{R}^{2}$ scores. However, XGBoost (30 sec) has a very short Training Time compared to Gradient Boosting (180 sec). As a result, the approach of each ML method to solving the complex problem differed from each other, and the success rates and Training Time also differed equally. The most important work to be done here is to choose which ML method you want to achieve according to the limited system in hand and the performance-accuracy dilemma.
Citation Count: Ergezer, H.; Leblebicioğlu, H. (2023). "Control structure design with constraints for a slung load quadrotor system", Measurement and Control (United Kingdom).
Control structure design with constraints for a slung load quadrotor system
(2023) Ergezer, Halit; Leblebicioğlu, Kemal; 293396
We 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.
Citation Count: Ateş, A...at all (2021). "Controller design for quadrotor-slung load system with swing angle constraints using particle swarm optimization", 2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedings, 021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021.
Controller design for quadrotor-slung load system with swing angle constraints using particle swarm optimization
(2021) Ateş, A.; Coşan, O.; Değirmenci, B.; Ergezer, Halit; 293396
In this paper, the controller is designed for a quadrotor carrying a slung load with a rope. When we consider the quadrotor and load pair, there are two cases: the quadrotor carries the load, and the load is on the ground. Since the dynamics of the system are different for these two cases, they must be considered separately. Therefore, a different PID controller is designed for each case. The necessary mechanism has been created to ensure that the transitions between these two controllers are smooth. The controller coefficients are adjusted so that the swing angles of the load are minimal. IMU has been added to the load-bearing mechanism to find out what angle the load is. Also, images of the load have been obtained with the camera located under the quadrotor. The swing angles have been calculated according to the position of the load in the image. Although our physical system studies continue, both the IMU and camera models have been created and integrated into the quadrotor-slung load model. PID coefficients have been obtained using the Particle Swarm Optimization method. Tests have been carried out on different flight profiles and the results obtained are presented. © 2021 IEEE.
Citation Count: Bekcan, Arda; Ergezer, Halit (2021). "Design and Implementation of Visual Simultaneous Localization and Mapping (VSLAM) Navigation System", 29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021).
Design and Implementation of Visual Simultaneous Localization and Mapping (VSLAM) Navigation System
(2021) Bekcan, Arda; Ergezer, Halit; 293396
It 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
Citation Count: Erdoğan, S.; Bektaş, a.; Ergezer, H. (2022). "Development of air-to-ground engagement analysis model of fighter aircrafts", Journal of the Faculty of Engineering and Architecture of Gazi University, Vol.37, No.4, pp.2225-2239.
Development of air-to-ground engagement analysis model of fighter aircrafts
(2022) Erdoğan, Sinem; Bektaş, Almila; Ergezer, Halit; 293396
In 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.
Citation Count: Alguner, Ayber Eray; Ergezer, Halit (2021). "Evaluation of features used in electromyography classification", SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings.
Evaluation of features used in electromyography classification
(2021) Alguner, Ayber Eray; Ergezer, Halit; 293396
Classification 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.
Citation Count: Özkan, Ekin Cansu; Ergezer, Halit. "Kinematic Analysis and Position Control of Motor Grader Blade Mechanism for Automatic Levelling", International Conference on Control, Decision and Information Technologies (CoDIT), pp. 237-242, 202.
Kinematic Analysis and Position Control of Motor Grader Blade Mechanism for Automatic Levelling
(2022) Özkan, Ekin Cansu; Ergezer, Halit; 293396
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.
Citation Count: Bektaş, Almila; Ergezer, Halit (2020). "Lpi Radar Waveform Classification Using Binary Svm And Multi-Class Svm Based On Principal Components Of Tfi", Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Vol. 62, No. 2, pp. 134-152.
Lpi Radar Waveform Classification Using Binary Svm And Multi-Class Svm Based On Principal Components Of Tfi
(2020) Bektaş, Almila; Ergezer, Halit; 293396
Since cognition has become an important topic in Electronic Warfare (EW) systems, Electronic Support Measures (ESM) are used to monitor, intercept and analyse radar signals. Low Probability of Intercept (LPI) radars is preferred to be able to detect targets without being detected by ES systems. Because of their properties as low power, variable frequency, wide bandwidth, LPI Radar waveforms are difficult to intercept with ESM systems. In addition to intercepting, the determination of the waveform types used by the LPI Radars is also very important for applying counter-measures against these radars. In this study, a solution for the LPI Radar waveform recognition is proposed. The solution is based on the training of Support Vector Machine (SVM) after applying Principal Component Analysis (PCA) to the data obtained by Time-Frequency Images (TFI). TFIs are generated using Choi-Williams Distribution. High energy regions on these images are cropped automatically and then resized to obtain uniform data set. To obtain the best result in SVM, the SVM Hyper-Parameters are also optimized. Results are obtained by using one-against-all and one-against-one methods. Better classification performance than those given in the literature have been obtained especially for lower Signal to Noise Ratio (SNR) values. The cross-validated results obtained are compared with the best results in the literature.
Citation Count: Ergezer, Halit (2021). "Multi-Objective Trajectory Planning for Slung-Load Quadrotor System", IEEE Access, Vol. 9, pp. 155003-155017.
Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
(2021) Ergezer, Halit; 293396
In this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load. The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions. These costs are optimized by imposing constraints on the slung-load quadrotor system's endpoints, borders, obstacles, and dynamical equations. The dynamic model of a slung-load quadrotor system is used in the Euler-Lagrange formulation. Although the differential flatness feature is mostly used in this system's trajectory planning, a fully dynamic model has been used in our study. A new multi-objective Genetic Algorithm has been developed to solve path planning, aiming to optimize trajectory length, mission time, and energy consumed during the mission. The solution process has a three-phase algorithm: Phase-1 is about randomly generating waypoints, Phase-2 is about constructing the initial non-dominated pool, and the final phase, Phase-3, is obtaining the solution. In addition to conventional genetic operators, simple genetic operators are proposed to improve the trajectories locally. Pareto Fronts have been obtained corresponding to exciting scenarios. The method has been tested, and results have been presented at the end. A comparison of the solutions obtained with MOGA operators and MOPSO over hypervolume values is also presented. © 2013 IEEE.
Citation Count: Ergezer, Halit; Leblebicioğlu, Kemal (2021). "Online path planning for unmanned aerial vehicles to maximize instantaneous information", International Journal of Advanced Robotic Systems, Vol. 18, No. 3.
Online path planning for unmanned aerial vehicles to maximize instantaneous information
(2021) Ergezer, Halit; Leblebicioğlu, Kemal; 293396
In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.
Citation Count: Karadeniz, Talha;...et.al. (2023). "Two Majority Voting Classifiers Applied to Heart Disease Prediction", Applied Sciences, Vol.13, No.6.
Two Majority Voting Classifiers Applied to Heart Disease Prediction
(2023) Karadeniz, Talha; Maraş, Hadi Hakan; Tokdemir, Gül; Ergezer, Halit; 34410; 293396
Two 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.
Citation Count: Algüner, Ayber Eray; Ergezer, Halit. (2023). "Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms", Measurement and Control, Vol.56, No.7-8, pp.1278-1291.
Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms
(2023) Algüner, Ayber Eray; Ergezer, Halit; 293396
Electromyography (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.

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