Alt birimler

• Hava Trafik Kontrol Bölümü
• Havacılık Yönetimi Bölümü
• Uçak Elektrik-Elektronik Bölümü
• Uçak Gövde-Motor Bakım Bölümü

Araştırmacılar(21)

• Atasoy, Vehbi Emrah
• Ayar, Murat
• Aydın, Emre
• Cam, O.
• Can, Erol
• Demirel, Soner
• Doğan, Ümit
• Ercümen, K.M.
• Işilak, C.
• Kılıç, Uğur
• Koçak, Daimi
• Köse, Oğuz
• Kurt, Bülent
• Özdemir, Mustafa
• Polater, Abdüssamet
• Sivri, Ilker
• Tanrıverdi, Gökhan
• Tatli, Ali
• Yalin, Gorkem
• Yelgin, Çetin
• Yilmaz, Harun

• ArticleYalnız künye2027/r/ebyu/17946
  A hybrid Fuzzy neural network approach for nonlinear control systems: Performance, adaptability, and robustness

  Can, Erol

  This study presents a Fuzzy Neural Network (FNN)-based control architecture for nonlinear control mapping and evaluates its performance using a stable second-order benchmark plant. The proposed FNN combines a compact seven-rule fuzzy structure with a hybrid AND/OR inference mechanism and a hybrid learning strategy based on Least Squares Estimation (LSE) and Gradient Descent (GD). Five operating-condition variables are used as fuzzy inputs, while Gaussian membership functions represent the antecedent variables. LSE is employed to estimate the consequent parameters, whereas GD is used to adapt the antecedent membership-function parameters during offline training. The proposed controller is compared with classical PID and conventional Fuzzy controllers, as well as Adaptive Neuro-Fuzzy Inference System (ANFIS), Takagi-Sugeno (TSK), and Interval Type-2 Fuzzy (IT2Fuzzy) controllers under common benchmark simulation conditions. The evaluation considers transient response, gain variations, external disturbances, measurement noise, and parameter perturbations. Under the reported nominal step-response comparison, the proposed FNN achieves a rise time of 0.72 s, a settling time of 1.28 s, and zero measured overshoot. The IT2-Fuzzy controller achieves an overshoot of 4.03%, whereas PID, conventional Fuzzy, ANFIS, and TSK exhibit overshoots of 16.30%, 12.63%, 14.77%, and 9.48%, respectively. Sensitivity and noise analyses further indicate that the proposed seven-rule architecture maintains favorable performance under the investigated parameter variations and measurement-noise conditions. The results demonstrate that the proposed FNN provides a compact and interpretable framework for nonlinear control mapping, offering favorable transient performance with limited rule complexity. Since the evaluation is based on numerical simulations, experimental validation on physical control platforms is required to further assess its practical applicability.

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• ArticleYalnız künye2026/r/ebyu/18238
  Adaptive hybrid control algorithm for integrated wind-solar energy systems: Enhancing efficiency and minimizing environmental impact

  Can, Erol

  This study introduces a three-tier adaptive control strategy designed for hybrid wind - solar energy systems, with the goal of optimizing energy capture, stabilizing the DC bus voltage, and enhancing overall system performance under varying environmental conditions. The control framework incorporates a TSR-based Maximum Power Point Tracking (MPPT) method for the wind turbine, a temperature-sensitive Perturb-and-Observe (P&O) MPPT for the photovoltaic array, and a real-time energy coordination system that dynamically adjusts the power allocation between wind and solar sources based on their instantaneous availability. A conventional Proportional-Integral (PI) controller manages the DC bus voltage, minimizing fluctuations and ensuring seamless integration with energy storage or the grid. The proposed approach is adaptable to both off-grid and grid-connected microgrids and is computationally efficient for real-time application. Simulation results from four typical scenarios - including high wind/low solar, low wind/high solar, intermittent generation, and balanced generation - show system efficiencies consistently exceeding 92%, along with a substantial reduction in voltage ripple when compared to traditional MPPT techniques. These findings validate the effectiveness of the coordinated control strategy in maintaining power quality, system reliability, and stable energy output, offering a scalable and practical solution for integrating renewable energy into microgrid systems.

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• ArticleYalnız künye2026/r/ebyu/18035
  A thermal-aware and self-adapting method for accurate state of charge estimation in lithium-ion batteries

  Can, E. R. O. L.

  Accurate state-of-charge (SOC) estimation remains a key challenge for lithium-ion batteries (LIBs) operating under varying thermal and load conditions, particularly in electric vehicles and grid-scale energy storage systems. Conventional model-based estimators such as EKF and UKF typically assume fixed parameters and neglect coupled electro-thermal dynamics, which degrades estimation accuracy under temperature fluctuations and aging effects. To address these limitations, this paper proposes a Thermal-Aware Hierarchical Self-Tuning Square Root Unscented Kalman Filter (TA-HST-SRUKF) for real-time SOC estimation. The proposed framework explicitly incorporates a lumped thermal model into the SOC estimation loop and employs a hierarchical self-tuning mechanism to adapt process and measurement noise covariances online. In addition, the square-root formulation enhances numerical stability and reduces computational burden, making the approach suitable for embedded battery management systems. Compared with conventional EKF- and UKF-based methods, the proposed TA-HST-SRUKF demonstrates improved robustness and accuracy across a wide temperature range and varying operating conditions. Simulation results confirm lower estimation errors and better adaptability without increasing computational complexity. The proposed method provides an effective and practical solution for highaccuracy SOC estimation in thermally dynamic and aging-prone battery applications.

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• ArticleAçık Erişim2026/r/ebyu/18113
  Adaptive Vibration Suppression in High-Speed Precision Drives Using Multi-Modal Sensor Fusion and Actuator Parameter Tuning

  Can, Erol

  Background High-speed precision drive systems, such as servo motors and fast-steering platforms, are often affected by coupled torsional and bending vibrations. These vibrations reduce system stability, prolong settling time, and limit achievable control bandwidth, particularly in applications requiring high precision and rapid response. Purpose This study aims to develop a simulation-based adaptive vibration suppression approach capable of simultaneously mitigating torsional and bending vibrations while enhancing dynamic performance and control accuracy in high-speed precision drive systems. Methods A simulation model of a high-speed precision drive system was developed by integrating a distributed multi-modal sensor network with an adaptive control strategy. The system incorporates fiber Bragg grating (FBG) strain sensors, piezoelectric torque sensors, and MEMS accelerometers to monitor vibration modes. A real-time sensor-fusion algorithm identifies dominant vibration frequencies and modes, and an adaptive PD controller dynamically adjusts actuator parameters-such as torque limits, damping coefficients, and phase compensation-based on the simulated structural response under different disturbance scenarios. Results Simulation results clearly demonstrate the effectiveness of the proposed method. Time-domain analysis shows that peak torsional vibration amplitudes are reduced by 65%, while bending vibration amplitudes are reduced by 58%. RMS analysis indicates that torsional vibrations decrease from 0.021 rad to 0.008 rad, and bending vibrations decrease from 0.018 m to 0.007 m. In addition, the adaptive control strategy improves settling time by 40% and provides a more stable steady-state response compared to conventional fixed-gain controllers. Conclusions The results confirm that the proposed simulation-based adaptive vibration suppression system effectively reduces multi-mode vibrations and enhances system stability. By combining distributed sensing, real-time modal analysis, and adaptive actuator tuning within a simulation framework, the approach provides a robust and efficient solution, demonstrating strong potential for the development of self-sensing and self-tuning high-speed precision drive systems.

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• ArticleYalnız künye2026/r/ebyu/18143
  UAV DC Motor behaviors control using new hybrid PSO-fuzzy logic with a dynamic error based optimization

  Can, Erol

  This study introduces a novel and comprehensive control architecture for DC motor speed regulation in unmanned aerial vehicles (UAVs), leveraging a hybrid Particle Swarm Optimization-Fuzzy Logic (PSO-FL) strategy. UAV platforms inherently exhibit highly nonlinear, time-varying, and uncertain dynamics, which often challenge the efficacy of traditional control techniques such as Proportional-Integral-Derivative (PID) controllers and conventional fuzzy logic systems. To overcome these limitations, a dual-layer PSO-FL controller is proposed, wherein the PSO algorithm is utilized to optimize both fuzzification and defuzzification parameters, thereby enhancing controller adaptability and precision. Moreover, a Centralized Agent Optimization (CAO) framework is integrated to accelerate convergence and improve overall computational efficiency. The proposed controller dynamically adjusts membership functions and rule weights in real time, enabling robust, stable, and rapid system responses even under sudden aerodynamic perturbations and variable payload conditions. Extensive simulation and experimental validations confirm the superior performance of the proposed method over classical PID, PSO-tuned PID, and conventional fuzzy logic controllers, demonstrating faster rise times, reduced overshoot, and minimized steady-state error. These findings significantly advance the development of intelligent UAV control systems by effectively combining swarm intelligence with human-like reasoning methodologies.

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• ArticleYayıncıda açık erişim2026/r/ebyu/19284
  İnsansız Hava Aracı ve Görüntü İşleme Destekli Yasaklı Bitki Tespiti

  Kose, Oguz

  Bu çalışmada insansız hava aracı (İHA) ile tarım arazileri üzerinden alınan görüntüler ile YOLOv11 ve YOLOv26 görüntü işleme algoritmaları kullanılarak tarım arazilerinde ekilmesi yasak olan bitkilerin tespiti gerçekleştirilecektir. Çalışma yazılım kiti ve donanım kiti olmak üzere iki aşamadan oluşmaktadır. Yazılım kitinde görüntü işleme algoritmalarının veri seti, eğitimi, testi ve program ara yüzü geliştirilecektir. Kullanılacak algoritmaların aynı veri seti üzerinden eğitimleri yapılacak, performans metrikleri incelenip en iyi performansı veren algoritma yazılım kitinde kullanılacaktır. Ara yüzün geliştirilmesi ve diğer tüm yazılım bileşenleri Python dilinde yapılmıştır. Donanım kiti ise projenin tüm donanımsal öğelerini içermektedir. Tasarlanan yazılım kiti, donanım kitinde bulunan Raspberry pi içerisinde çalışacaktır. İHA ve şarj istasyonu kendisine ayrılan alan içerisinde muhafaza edilecektir. Önerilen çalışma ile tarım arazilerinde ekilmesi yasak olan bitkilerin tespiti yapılarak erken müdahale edilmesinin avantajı sağlanacaktır. Ayrıca yasak bitkilerinin ekiminin tespiti yapılarak kolluk kuvvetlerinin hem personel hem de zaman ihtiyacını minimuma indirmesi planlanmaktadır.

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• ArticleYalnız künye2026/r/ebyu/18015
  Hybrid PID-fuzzy based adaptive sensor switching strategy for intelligent radiation monitoring systems

  Can, Erol

  Radiation monitoring systems require high sensitivity, stability, and adaptability under dynamically changing environmental conditions. Conventional fixed-sensitivity sensor architectures often suffer from reduced accuracy under high radiation levels, increased noise effects at low radiation levels, and unnecessary energy consumption due to continuous high-sensitivity operation. This study proposes a novel hybrid adaptive control framework that integrates Proportional-Integral-Derivative (PID) control, Sugeno-type fuzzy logic inference, and threshold-based sensor switching within a unified architecture. The proposed method dynamically adjusts sensor sensitivity by evaluating both radiation intensity and PID error behavior. A mathematical control model defined as u(t) = alpha uPID(t)+beta uFLC(t)+gamma R(t)/Rmax is implemented to generate a continuous adaptive control signal for real-time sensor switching. Simulation results under four distinct operating conditions demonstrate that radiation intensity provides the dominant contribution for sensitivity transition, while PID error improves transient regulation and system response. A threshold-based switching mechanism (u(t)>uth = 4) effectively separates low- and high-sensitivity operating regions without causing instability. The results confirm that the proposed hybrid architecture improves responsiveness, robustness, and operational efficiency compared with fixed-sensitivity monitoring approaches. The main contribution of this study is the integration of continuous PID-fuzzy control regulation with discrete sensor selection in a single adaptive framework, providing a scalable solution for intelligent radiation sensing applications and next-generation monitoring systems.

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• ArticleYalnız künye2026/r/ebyu/11389
  A Proactive Approach to Prevent Supply Chain Quality Risk in Food Supply Chains: The Interplay Between Buyer Prohibitive Voice and Operations Cognizance

  Polater, Abdüssamet

  Purpose: Reducing supply chain quality risk (SCQR) is of paramount importance for providing safe and quality products to the community. While abundant literature has considered the effect of relational factors such as knowledge and information sharing to manage supply chain (SC) risks, not much is known about the effect of buyers’ prohibitive voice behavior (PHVB) in this process. This study extends the existing literature on SCQR by examining the underlying mechanism between relational factors (i.e. PHVB and operations cognizance) and SCQR. Drawing on relational view (RV), we develop hypotheses that investigate the moderating effect of operations cognizance in the PHVB-SCQR relationship. Methodology: This study tested the proposed conceptual model using a survey questionnaire method. As a unit of analysis, food manufacturing firms were selected owing to the constant collaboration practices taking place between FSC members to increase relational outcomes. The necessary data was collected from medium- to senior-level respondents who hold SC decision-making authority inside their firms and are skilled at answering questions about PHVB, SCQR, and operations cognizance. The researcher expressly asked whether participants have regular and direct communication with key suppliers, and actively engage in SC operations. The individuals who did not fit these criteria were purged from final survey sample. Ultimately, 275 usable surveys were able to be collected by the researcher. In order to complete the aforementioned processes, this study employed Amos 22.0, SPSS 21.0, and the SPSS plugin “Process Analysis System.” In this study, Amos is used to conduct CFA, and SPSS is selected for data analysis. When testing hypotheses, which include direct and moderation effects, the Hayes Process is used. Findings: The findings of this study confirm that PHVB significantly and negatively affects SCQR. This indicates that establishing a speaking-up mechanism between buyers and suppliers significantly to reduces quality risks in FSC. In other words, buyers’ expression of their concerns regarding the suppliers’ maloperations can reduce the risk of obtaining poor-quality products. This study also indicates that operations cognizance strengthens the PHVB-SCQR relationship. Thus, the extent to which PHVB reduces SCQR may be explained by varying levels of operations cognizance. This supports the literature, which investigated the role of understanding the operational routines and working cultures of suppliers in reducing supply risks, including quality risks. Research implications: The findings of this study contribute to the existing literature and RV by defining and conceptualizing the buyers’ PHVB, which is postulated as a prosocial and proactive behavior in the SC context, and investigating its effect on SCQR in FSC. An extensive body of research has been conducted in the SC literature to examine the effects of knowledge and information-sharing practices (i.e. sharing technical knowledge or order and delivery time information) on managing SC risks. In addition, there exist a limited number of SCM studies that partially or indirectly imply the importance of addressing supplier-related issues to minimize supply risk. But there is a lack of studies that investigates this relationship through the lens of PHVB. Therefore, by utilizing RV, this research provides a nuanced understanding of PHVB in reducing SCQR. This study makes several contributions to RV and the SCM literature in several ways by analyzing the effect of operations cognizance in the PHVB-SCQR relationship. Although it was not hypothesized, the findings indicate that operations cognizance has a significant and positive effect on PHVB which is hardly studied in the literature. Additionally, whilst the extant literature commonly has focused on the role of monitoring on managing quality risk in SC, this study revealed the impact of operations cognizance to better manage SCQR. Finally, the findings unveil that the effect of PHVB on SCQR can be enhanced by operations cognizance. Practical implications: This study provides evidence to managers regarding the factors influencing SCQR. The success of food manufacturers is associated with the materials they receive from their suppliers. Therefore, it is important to maintain close ties with suppliers to obtain quality inputs. In this regard, this study suggests that buying firms should be willing to voice their concerns and opinions with their suppliers. In this way, they can reduce the risk of obtaining lower grad materials, so that prevent their SC performance from deteriorating and their company from losing its reputation. Risk occurrences can arise at any point in the FSC; hence, food quality risk control encompasses all links within the FSC. For this reason, managers need to have a proactive approach to detect the risks in advance and take preventive actions. This can be achieved by establishing a mechanism to thoroughly understand suppliers, including their operational processes and culture. In this way, managers can better know the shortcomings of their suppliers, be aware of the potential risks, and present PHVB based on the solid evidence they have. Originality/value/contribution: In this regard, this research makes a significant contribution to the field of supply chain management (SCM) and RV by introducing the concept of buyers’ PHVB and extending the discussion of factors that have an effect on SCQR.

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• ArticleYayıncıda açık erişim2026/r/ebyu/11281
  A MACHINE LEARNING APPROACH FOR EVALUATING AIR TRAFFIC CONTROLLER CANDIDATES: A COMPARISON WITH AHP AND ANP METHODS

  Özdemir, Mustafa; Özdemir, Müjgan Sağır

  The safety and efficiency of air traffic operations depends heavily on air traffic controllers, making the accurate identification of suitable candidates a critical challenge for air navigation service providers. The selection process is traditionally based on standardized aptitude testing combined with structured interviews and expert evaluation, which may be affected by subjectivity and require substantial time and resources. This study introduces a machine learning based decision support approach for air traffic controller (ATCO) candidate selection. The candidate evaluation task is formulated as a binary classification problem to predict interview outcomes using historical data from an actual selection process involving 194 candidates, including 30 who passed the interview phase. Three machine learning models, Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT), are trained using candidate background variables such as prior examination scores, type of high school attended, and high school grade point average. The performance of the models is evaluated in comparison with existing approaches based on the Analytic Hierarchy Process and the Analytic Network Process. The results of this study provide preliminary evidence that, in the context of ATCO selection, machine learning models may approximate the outcomes of traditional interviews, with LR achieving an accuracy of 93%, and both DT and SVM achieving accuracies of 90%. Using the best-performing model, 46 eliminated candidates were correctly classified as eliminated (true negatives) and 9 selected candidates were correctly identified as selected (true positives), while 4 eliminated candidates were incorrectly classified as selected (false positives). These results suggest that the proposed approach may support air navigation service providers and related institutions by assisting early-stage screening through data driven prioritization of ATCO candidates.

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• ArticleYalnız künye2026/r/ebyu/10985
  An adaptive hybrid energy management system for UAVs integrating fuzzy logic, solar forecasting and real-time control

  Can, Erol

  This study presents a hybrid energy management system (EMS) for unmanned aerial vehicles (UAVs), integrating flexible photovoltaic (PV) panels with a high-capacity lithium-polymer (Li-Po) battery to enhance flight endurance and energy resilience. The proposed EMS features a multi-layered adaptive control architecture, combining fuzzy logic-based decision-making, short-term solar irradiance prediction using machine learning and closed-loop proportional-integral-derivative (PID) motor control. This architecture dynamically allocates power based on real-time solar irradiance, battery state of charge (SOC) and propulsion load. The approach aims to minimise battery stress while ensuring stable UAV operation under variable environmental conditions. Simulation results indicate that, without PV support, the UAV battery depletes in approximately 28 min. However, incorporating an 80 W PV panel extends the endurance to 37 min, and a 160 W effective PV configuration increases the flight time to over 50 min, while maintaining the SOC above 20%. This corresponds to a 79% improvement in endurance and nearly a 50% reduction in net battery energy consumption. The EMS effectively mitigates deep discharge cycles, improving battery health and operational lifespan. These findings demonstrate that the proposed hybrid EMS provides a scalable, adaptive and computationally efficient solution for balancing renewable and stored energy. It outperforms conventional static or single-source control strategies, particularly under fluctuating solar conditions.

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