Profile URL: https://hdl.handle.net/20.500.13091/11646
Email Address:adurdu@ktun.edu.tr
Main Affiliation:02.04. Department of Electrical and Electronics Engineering
Status: Current Staff
ORCID:
0000-0002-5611-2322
0000-0002-5611-2322Scopus ID:
55364612200
55364612200YÖK Akademik: 2BAA07FC84487E28
Google Scholar:
4wHq7iwAAAAJ
4wHq7iwAAAAJWeb of Science ID:
AAQ-4344-2020
AAQ-4344-2020Name Variants:
Durdu, A.
119 results
Scholarly Output Search Results
Now showing 1 - 10 of 119
Article Citation - Scopus: 1Deep Reinforcement Learning-Based Control of a 1-Dof Helicopter: A Comparative Analysis of Classical and Modern Methods(IOP Publishing Ltd, 2026-01-09) Budak, Serkan; Sungur, Cemil; Durdu, AkifThis study presents a comparative analysis of different control methodologies on the linearized model of a single degree-of-freedom (1-DOF) helicopter system around its operating point. The control structures evaluated in this study include a Proportional-Integral-Derivative (PID) controller optimized via Particle Swarm Optimization (PSO), Model Predictive Control (MPC), and the Deep Deterministic Policy Gradient (DDPG) algorithm based on Deep Reinforcement Learning (DRL). The conducted simulations revealed that the DRL-based controller exhibited a superior performance compared to the other methods, even on the linearized model, both in terms of reducing the magnitude of the error and improving the system's transient response performance. This approach demonstrates the capacity to produce a rapid response without compromising the system's stability, concurrently achieving faster rise and settling times. Furthermore, by effectively suppressing the time-weighted error values, it provides an advantage in terms of both accuracy and control quality. These compelling results indicate the significant potential of learning-based control methods in the control of nonlinear systems, establishing DRL-based strategies as a reliable foundation for advanced control applications and a superior alternative to classical methods.Article Kalman Filter and Pid Application on Underwater Vehicles(2022-08-31) Budak, Serkan; Tekin Muhammed Halis; Durdu Akif; Sungur CemilUnmanned underwater vehicles (ROV/AUV) are autonomous or remotely controlled robotic systems that can move underwater at any desired angle. Unmanned underwater vehicles; It is used in areas such as underwater image taking, ship maintenance and repair, coast guard, examination of shipwrecks, underwater cleaning. In this study, the software design of the balance control of underwater vehicles was carried out using the PID algorithm. For the PID algorithm trial, a two-motor test setup with an IMU sensor was prepared. After the data from the sensor were recorded in MATLAB using the Kalman filter, the transfer function of the system was obtained using the System Identification Toolbox. With the obtained transfer function, the stable operation of the system is provided in real time. As a result of the research on software and hardware integration, microcontroller ARM-based STM32 was used.Doctoral Thesis Sinyalize Kavşaklarda Etkileşimli ve Adaptif Trafik Yönetim Modeli(2025) Yalçınlı, Fuat; Akdemir, Bayram; Durdu, AkifNüfus ile birlikte artan şehirleşme oranı ister istemez insanların ulaşımda kullandıkları araçların neden olduğu trafik yoğunluğunu da her geç gün artırmaktadır. Bu nedenle trafiğin yönetimi artık şehir yaşamında en kritik unsurlardan biri olmuştur. İnsanların ulaşımda kullandıkları taşıt yollarının üzerindeki en büyük sorun yolların birbiriyle kesiştiği ve kesintisiz trafik akışını engelleyen sinyalize kavşaklardır. Trafikte meydana gelen gecikmeleri önlemede, sinyalizasyon kontrollerinin çok büyük bir role sahip olduğu görülmektedir. Bu kavşaklarda trafiğin en verimli şekilde akışının sağlanması, hem daha az yakıt tüketimine sebep olması nedeniyle ülke ekonomisine hem de içten yanmalı taşıtların sebep olduğu karbondioksit emisyonunun azaltılması nedeniyle daha sürdürülebilir bir çevre oluşturulmasına sağlayacağı katkılar çok önemlidir. Bu doktora tez çalışması kapsamında trafik akışına olumlu katkılar sağlayacak çalışmalar yapılarak sinyalize kavşaklar üzerinde işletilebilecek bir adaptif trafik yönetim modeli geliştirilmiştir. Kavşaklarda trafik akışını verimli bir şekilde sağlamak ve kontrol etmek üzere geliştirilen adaptif model, trafik yoğunluğuna göre minimum ve maksimum süre aralığında faz işletimi ve faz atlama özelliklerine sahiptir. Geliştirilen bu modelin diğer sinyalize kavşak kontrollerinden en belirgin farklılığı ise sıralı değil, esnek faz yapısına sahip olarak kavşaklarda trafik akışını kontrol etmesidir. Geliştirilen adaptif trafik yönetim modelinin sonuçlarını irdelemek ve karşılaşılması muhtemel sorunların çözümü için çalışma sonucunda elde edilen adaptif model, Antalya ili sınırları içerisinde bulunan Kemer ilçesi yolu üzerindeki Heybe Kavşağı'na ait gerçek kavşak verileri kullanılarak SUMO simülasyon programında birebir simüle edilmiştir. Bu simülasyon çalışması sonucunda elde edilen adaptif trafik yönetim modeli, bu kavşakta uygulanmış ve elde edilen sonuçlar yorumlanarak değerlendirilmiştir. Bu çalışmada, SUMO programından elde edilen iyileştirme sonuçları farklı yöntemlerle doğrulanmıştır. Webster teoremi ile Heybe Kavşağı sabit zamanlı sinyalizasyon sistemi verileri üzerinden gecikme hesaplaması gerçekleştirilmiştir. Ayrıca, kamera üzerinden görsel tespit metodu ile araçların gecikme değerleri saptanmış ve araç başına gecikme parametresi tespit edilmiştir. Son olarak AIMSUN simülasyon programında Heybe Kavşağı modellenmiş ve SUMO simülasyon işletimi ile aynı trafik verilerinde işletilmiştir. Böylelikle, Webster teoremi, görsel tespit metodu ve AIMSUN simülasyon programı olmak üzere üç farklı doğrulama yöntemi uygulanmıştır. Bütün doğrulama yöntemlerinden SUMO simülasyon çıktısı ile benzer sonuçlar elde edilmiştir. Böylelikle geliştirilen adaptif trafik yönetim modelinin kavşağa olan etki sonuçları doğrulanmıştır. Bu tez çalışmasında, son olarak performans ölçüm sensörü tasarlanmış ve Heybe Kavşağı'na yerleştirilmiştir. Bluetooth sinyalleri ile sinyalize kavşaklarda gerçek zamanlı performans ölçümü yapabilecek bir sistem tasarımı gerçekleştirilmiştir. Kavşak kollarındaki araçların bluetooth sinyalleri üzerinden seyahat sürelerini ölçerek sinyalizasyon sisteminden kaynaklı gecikme değerlerini tespit edebilen performans ölçüm sistemi, geliştirilen adaptif trafik yönetim modelinin tüm gün kavşaktaki etkisini tespit etmede kullanılmıştır. Çalışmada geliştirilen adaptif trafik yönetim modelinin sonuçları bu çalışmada tasarlanan performans ölçüm sistemi ile tespit edilmiştir. Tespit edilen sonuçlara göre geliştirilen adaptif trafik yönetim modelinin her bir alt bileşenin performansa etkisi sabit zamanlı sinyalizasyon sistemine göre olumlu yöndedir. Minimum maksimum süre aralığı faz işletimi, faz atlama, matris yapı ve dinamik faz özellikleri sinyalize kavşak performansını daha verimli hale getirmiştir. Çalışma sonucunda, günlük ortalama 50.000 adet aracın kullandığı Heybe Kavşağı'nda geliştirilen adaptif trafik yönetim modelinin sabah bir saatlik yoğun trafikte uygulanması ile sabit zamanlı sinyalizasyon yönetimine göre araç başına ortalama gecikme 28,1 saniye/taşıt, bekleme süresi 28,5 saniye/taşıt azaltılmış ve ortalama hız 0,91 kilometre/saat artırılmıştır. Ayrıca, tasarlanan performans ölçüm sistemi ile tüm gün yapılan ölçümlerde, geliştirilen adaptif trafik yönetim modelinin Heybe Kavşağı'na uygulanması ile sabit zamanlı sinyalizasyon yönetimine göre araç başına ortalama gecikmede %38,42 oranında iyileşme sağladığı tespit edilmiştir.Article Citation - WoS: 1Real-Time Performance Measurement Application Via Bluetooth Signals for Signalized Intersections(Mdpi, 2024-09-04) Yalçınlı, Fuat; Akdemir, Bayram; Durdu, AkifImproving the performance at signalized intersections can be achieved through different management styles or sensor technologies. It is crucial that we measure the real-time impact of these variables on intersection performance. This study introduces a Bluetooth-based real-time performance measurement system applicable to all signalized intersections. Additionally, the developed method serves as a feedback tool for adaptive intersection management systems, providing valuable data input for performance optimization. The method developed in the study is applied at the Refik Cesur Intersection in the Polatl & imath; district of Ankara where delay values are calculated based on traffic flows and data from Bluetooth sensors positioned at strategic locations. Initially, the intersection operated under a fixed-time signaling system, followed by a fully adaptive signaling system the next day. The performance of these two systems is compared using the Bluetooth-based application. The results show that the average delay per vehicle per day is 58.1 seconds/vehicle for the fixed-time system and 45.3 seconds/vehicle for the adaptive system. To validate the Bluetooth-based performance measurement system, the intersection is modeled and simulated using Aimsun Simulation Software Next 20.0.4. The simulation results confirm the findings of the Bluetooth-based analysis, demonstrating the effectiveness of the adaptive signaling system in reducing delays.Conference Object Citation - Scopus: 3Localization Using Two Different Imu Sensor-Based Dead Reckoning System(Institute of Electrical and Electronics Engineers Inc., 2024-03-20) Toy, I.; Durdu, A.; Yusefi, A.Dead reckoning estimates the current position, speed, and direction of moving objects using known position information. Localization determines an object's location on the map, categorized into human and vehicle localization. Autonomous vehicles rely on accurate vehicle localization for effective task execution. While Global Navigation Satellite System (GNSS) is a popular method, weak or absent signals can pose challenges. This study utilizes Inertial Measurement Unit (IMU) sensors for localization, integrating a second IMU to enhance accuracy. Fusing data from two IMU sensors, a dead reckoning system achieves 1.02 degrees and 1.41 meters errors in rotation and translation with a single IMU, and 1.01 degrees and 1.04 meters with two IMUs, respectively. © 2024 IEEE.Conference Object Self-Recovery Algorithm for Autonomous Mobile Robots in Industrial Environments(Institute of Electrical and Electronics Engineers Inc., 2025-10-09) Durdu, Akif; Çaǧiran, Yiǧit Bora; Akpinar, Cihat TalatArticle Citation - WoS: 24Citation - Scopus: 40An Adaptive Method for Traffic Signal Control Based on Fuzzy Logic With Webster and Modified Webster Formula Using Sumo Traffic Simulator(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021) Ali, Muzamil Eltejani Mohammed; Durdu, Akif; Çeltek, Seyit Alperen; Yılmaz, AlperIn the past, the Webster optimal cycle time formula was limited to calculate the optimal cycle from historical data for fixed-time traffic signal control. This paper focuses on the design of an adaptive traffic signal control based on fuzzy logic with Webster and modified Webster's formula. These formulas are used to calculate the optimal cycle time depending on the current traffic situation which applying in the next cycle. The alternation of the traffic condition between two successive cycles is monitored and handled through the fuzzy logic system to compensate the fluctuation. The obtained optimal cycle time is used to determine adaptively the effective phase green times i.e. is used to determine adaptively the maximum allowable extension limit of the green phase in the next cycle. The SUMO traffic simulator is used to compare the results of the proposed adaptive control methods with fuzzy logic-based traffic control, and fixed-time Webster and modified Webster-based traffic control methods. The proposed methods are tested on an isolated intersection. In this study, real field-collected data obtained from three, four, and five approaches intersections in Kilis/Turkey are used to test the performance of the proposed methods. In addition, to examine the efficiency of the proposed techniques at heavy demands, the arbitrary demands are generated by SUMO for a four approaches intersection. The obtained simulation results indicate that the proposed methods overperform the fixed time and fuzzy logic-based traffic control methods in terms of average vehicular delay, speed, and travel time.Article The Performance Evaluation of Image Matching Techniques Within Uav Images(Turkgeo, 2020) Makineci Hasan Bilgehan; Karabörk Hakan; Durdu AkifIt was aimed that the images were acquired with two different types of UAV by featurebased transformation algorithms such as SURF (Speeded Up Robust Features), FAST (Features from Accelerated Segment Test) and BRISK (Binary Robust Invariant Scalable Keypoints) in this study. Images (acquired by both UAV types) grouped by inclination. This classification is based on the wing type of the UAV (Rotary-Wing UAV” and “FixedWing UAV). Images with different characteristics were used to produce mosaics from the algorithms. The first performance preferred a flight height of 30 m (Ground Sample Distance, 0.82 cm/pixel) with the frontal overlap of 80%, and the second performance preferred a flight height of 60 m (GSD, 1.64 cm/pixel) and same overlap. Ten images from both performances were combined in all algorithms. Mismatches have been observed, and the mosaics produced after a very long process are not found satisfactory. According to the results, for rotary-wing UAV (SURF, BRISK and FAST), the algorithm run times were determined as 76.5 minutes, 11 minutes and 1839 minutes. Also, for fixed-wing UAV (SURF, BRISK and FAST), algorithm run times of 238 minutes, 95 minutes and 3350 minutes were determined.Article Citation - WoS: 18Citation - Scopus: 21The Ytu Dataset and Recurrent Neural Network Based Visual-Inertial Odometry(ELSEVIER SCI LTD, 2021-11-01) Gürtürk, Mert; Yusefi, Abdullah; Aslan, Muhammet Fatih; Soycan, Metin; Durdu, Akif; Masiero, AndreaVisual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) are fundamental problems to be properly tackled for enabling autonomous and effective movements of vehicles/robots supported by vision -based positioning systems. This study presents a publicly shared dataset for SLAM investigations: a dataset collected at the Yildiz Technical University (YTU) in an outdoor area by an acquisition system mounted on a terrestrial vehicle. The acquisition system includes two cameras, an inertial measurement unit, and two GPS receivers. All sensors have been calibrated and synchronized. To prove the effectiveness of the introduced dataset, this study also applies Visual Inertial Odometry (VIO) on the KITTI dataset. Also, this study proposes a new recurrent neural network-based VIO rather than just introducing a new dataset. In addition, the effectiveness of this proposed method is proven by comparing it with the state-of-the-arts ORB-SLAM2 and OKVIS methods. The experimental results show that the YTU dataset is robust enough to be used for benchmarking studies and the proposed deep learning-based VIO is more successful than the other two traditional methods.Article Citation - WoS: 17Citation - Scopus: 26Consensus-Based Virtual Leader Tracking Swarm Algorithm With Gdrrt*-Pso for Path-Planning of Multiple-Uavs(Elsevier B.V., 2024-07-01) Yildiz, B.; Aslan, M.F.; Durdu, A.; Kayabasi, A.UAV technology is rapidly advancing and widely utilized, particularly in social and military domains, due to its extensive motion and maneuverability. Coordinating multiple UAVs enables more rapid and efficient task execution compared to a single UAV. The proliferation of UAVs across various sectors, including entertainment, transportation, delivery, and social domains, as well as military applications such as surveillance, tracking, and attack, has spurred research in swarm systems. In this study, a new swarm topology is presented by combining the Consensus-Based Virtual Leader Tracking Swarm Algorithm (CBVLTSA), which provides formation control in swarm systems, with the Goal Distance-based Rapidly-Exploring Random Tree with Particle Swarm Optimization (GDRRT*-PSO) route planning algorithm. Recently proposed, GDRRT* is notable for its efficient operation in expansive environments and rapid convergence to the goal. Within this framework, the path generated by GDRRT* is optimized using PSO to yield the shortest current route. CBVLTSA employs a potential push and pull function to facilitate cooperative, coordinated flight among swarm members. While applying pushing force to avoid collisions with each other and obstacles, members also exert pulling force to maintain flight formation while navigating to target points. This ensures controlled flight formation and collision-free traversal along the GDRRT*-PSO route. Consequently, unlike the others, the proposed algorithm achieves faster target reach with pre-planned routes, demonstrating a robust and flexible swarm topology with CBVLTSA. Moreover, we anticipate the significant utility of this algorithm across various swarm applications, including target detection, observation, tracking, trade and transportation logistics, and collective defense and attack strategies. © 2024 Elsevier B.V.
Research Topics
Domains
Physical Sciences
Fields
EngineeringComputer Science
Subfields
Aerospace EngineeringComputer Vision and Pattern RecognitionControl and Systems EngineeringBuilding and Construction
Specific Research Areas
Robotics and Sensor-Based Localization
Robotic Path Planning Algorithms
Traffic control and management
Robot Manipulation and Learning
Traffic Prediction and Management Techniques
Sustainable Development Goals
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
17
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
16
Research Products
3GOOD HEALTH AND WELL-BEING
5
Research Products
13CLIMATE ACTION
3
Research Products
8DECENT WORK AND ECONOMIC GROWTH
2
Research Products
6CLEAN WATER AND SANITATION
1
Research Products
7AFFORDABLE AND CLEAN ENERGY
1
Research Products
2ZERO HUNGER
1
Research Products
17PARTNERSHIPS FOR THE GOALS
1
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
Documents
81
Citations
1591
h-index
20
Documents
69
Citations
1157
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 88 |
| Karamanoğlu Mehmetbey University | 37 |
| Selçuk University | 23 |
| The Ohio State University | 10 |
| Necmettin Erbakan University | 6 |
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Data obtained from OpenAlex
| Journal | Count |
|---|---|
| Avrupa Bilim ve Teknoloji Dergisi | 9 |
| 2025 14th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2025 - Proceedings -- 14th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2025 -- 9 October 2025 through 11 October 2025 -- Bucharest -- 218575 | 4 |
| International Journal of Trend in Scientific Research and Development | 3 |
| Turkish Journal of Electrical Engineering and Computer Sciences | 3 |
| -- 9th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2025 -- Gaziantep -- 211342 | 3 |
Current Page: 1 / 14
Scholarly Output
119
Articles
74
Views / Downloads
415/1473
Supervised MSc Theses
9
Supervised PhD Theses
7
WoS Citation Count
985
Scopus Citation Count
1346
Patents
0
Projects
1
WoS Citations per Publication
8.28
Scopus Citations per Publication
11.31
Open Access Source
72
Supervised Theses
16
Scopus Quartile Distribution
Competency Cloud
