Browsing by Author "Çeltek, Seyit Alperen"

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Citation - WoS: 24
Citation - Scopus: 40
An 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, Alper
In 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.
Çoklu Kavşaklar için Ayarlanabilir ve Ağırlık Tabanlı Adaptif Trafik Sinyal Kontrolü
(Konya Teknik Üniversitesi, 2024) Yıldız Budak, Zülal Hilal; Durdu, Akif; Çeltek, Seyit Alperen
21. yüzyılın en büyük sorunlarından biri olarak kabul edilen trafik sinyal yönetimi; çevre, ekonomi ve halk sağlığı üzerinde olumsuz etkileri nedeniyle önemli bir sorundur. Bu zorluğun üstesinden gelmek amacıyla kentsel alanlardaki izole ve koordineli kavşaklarda trafik sinyal kontrolü için hem deterministik tabanlı hem de yapay zekâ tabanlı birçok yöntem önerilmiştir. Deterministik sistemler, karmaşık trafik koşulları ve değişkenlerini hesaba katma konusunda sınırlıdır ve gerçek zamanlı olarak trafik koşullarına esnek cevap verme yetenekleri kısıtlıdır. Ayrıca, büyük ölçekli trafik ağlarını yönetmek için gereken manuel ayarlamalar zaman alıcı ve maliyetlidir. Yapay zekâ tabanlı sistemler ise modelin eğitim verilerine bağımlı olmaları ve bu verilerin eksik veya yanıltıcı olması durumunda performansları istenen başarıyı gösterememektedir. Maksimum Ağırlıklı Akış yöntemi trafik akışını optimize ederek şehir içi trafiği daha akıcı getirmek için önerilen yöntemlerden biridir. Klasik Maksimum Ağırlıklı Akış yöntemi yapay zekâ kullanarak ağdaki akışın maksimum olmasını sağlarken, aynı zamanda deterministik yaklaşımlar kullanılmaktadır. Ancak, Klasik Maksimum Ağırlıklı Akış yöntemindeki karmaşıklık ve hesaplama maliyetleri, yöntemin pratik olarak uygulanabilirliğini azaltmaktadır. Bu tez çalışmasında Klasik Maksimum Ağırlıklı Akış yönteminin etkinliğini artırmak ve tekniği daha uygulanabilir kılmak için iki stratejiden oluşan yeni bir yaklaşım önerilmektedir. Önerilen ilk strateji, araçların belirli bir lokasyon aralığındaki anlık hızlarına göre kavşağa yaklaşma sürelerini hesaplayarak süreci basitleştirmektedir. Önerilen ikinci strateji, optimum araç bekleme katsayısını tanımlamak için regresyon kullanmaktadır. Önerilen yaklaşımın trafik sinyal kontrolü üzerindeki etkinliği gerçek veriler kullanılarak izole ve koordineli kavşak sistemlerinde SUMO simülasyon ortamında test edilmektedir. Ayrıca önerilen Yeni Maksimum Ağırlıklı Akış yöntemi Statik, Uyarmalı, Gecikme tabanlı, Webster, MaxPressure ve Klasik Maksimum Ağırlıklı Akış yöntemleriyle karşılaştırılmıştır. Sonuç olarak, önerilen yöntem izole ve koordineli kavşak sistemlerinde mevcut algoritmalardan daha iyi performans göstermektedir.
Citation - WoS: 14
Citation - Scopus: 21
Design and Simulation of the Hierarchical Tree Topology Based Wireless Drone Networks
(IEEE, 2018) Çeltek, Seyit Alperen; Durdu, Akif; Kurnaz, Ender
In drone applications, the drone could send data using telemetry devices or radio frequency module which has a limited range. So, there is no interaction between user and drone after a certain range. In this study, a hierarchical tree topology based wireless drone network is designed and presented to overcome range challenge. Proposed network consist of three main parts; Control Center (CC), Master Drone (MD) and Slave Drones (SDs). The CC as a network manager communicates with just MD via telemetry devices. SDs are explorer drones for the search and rescue application. The data transfer between CC and SDs is provided by MD which is explorer like SDs. This paper clearly shows that the enhancement of the communication range is possible with such this approach. Also the designed drone networks are simulated using V-REP (Virtual Robot Experimentation Platform). According to the simulation results, the proposed drone network system operates quickly, and finds the target in 5 minutes, which classical system not find in 10 minutes. The proposed model clearly shows that an application using a drone is completed in a shorter time with the drone swarm well organized.
Citation - WoS: 1
Citation - Scopus: 1
Detection of Vortex Cavitation With the Method Adaptive Neural Fuzzy Networks in the Deep Well Pumps
(Univ Namik Kemal, 2021) Durdu, Akif; Çeltek, Seyit Alperen; Orhan, Nuri
Nowadays submersible deep well pumps are the most used irrigation systems in agriculture field. Efficient operation and economical life of pumps is an important issue. One of the most important parameters affecting pump efficiency and life is cavitation The cavitation is one of the problems frequently faced in the pump systems that widely used in the agriculture field. The cavitation could cause more undesired effects such as loss of hydraulic performance, erosion, vibration and noise. This paper presents a novel model for the detection of vortex cavitation in the deep well pump used in the agriculture system using adaptive neural fuzzy networks. The data submergence, flow rate, power consumption, pressure values, and noise values used for training the ANFIS (Adaptive-Network Based Fuzzy Inference Systems) network are acquired from an experimental pump. In this study, we use to the sixty-seven data for training process, while the fifteen data have used for testing of our model. The average percentage error (APE) has obtained as 0.08 % and as 0.34 % respectively for 67 training data and for 15 test data. The performance of the implemented model shows the advantages of ANFIS. The result of this study shows that ANFIS can be successfully used to detect vortex cavitation. This paper has two novel contributions which are the usage of noise value on cavitation detection and find out cavitation by using adaptive neural fuzzy networks. During the cavitation, the pump parameters must change by controller for prevent unwanted pump errors. The strategy proposed could be preliminary study of automatic pump control. Also proposed novel control strategy can be used for cavitation control in agriculture irrigation pumps, because of easy set up and no need extra cost. The ANFIS based model has real-time applicable thanks to rapid and easy control. It is possible to set safe boundaries in submergence in this model. Thus, users by adjusting controllable parameters can prevent cavitation and increase pump efficiency.
Citation - WoS: 8
Citation - Scopus: 10
Evaluating Action Durations for Adaptive Traffic Signal Control Based on Deep Q-Learning
(SPRINGER, 2021) Çeltek, Seyit Alperen; Durdu, Akif; Ali, Muzamil Eltejani Mohammed
Adaptive traffic signal control is the control technique that adjusts the signal times according to traffic conditions and manages the traffic flow. Reinforcement learning is one of the best algorithms used for adaptive traffic signal controllers. Despite many successful studies about Reinforcement Learning based traffic control, there remains uncertainty about what the best actions to actualize adaptive traffic signal control. This paper seeks to understand the performance differences in different action durations for adaptive traffic management. Deep Q-Learning has been applied to a traffic environment for adaptive learning. This study evaluates five different action durations. Also, this study proposes a novel approach to the Deep Q-Learning based adaptive traffic control system for determine the best action. Our approach does not just aim to minimize delay time by waiting time during the red-light signal also aims to decrease delay time caused by vehicles slowing down when approaching the intersection and caused by the required time to accelerate after the green light signal. Thus the proposed strategy uses not just information of intersection also uses the data of adjacent intersection as an input. The performances of these methods are evaluated in real-time through the Simulation of Urban Mobility traffic simulator. The output of this paper indicate that the short action times increase the traffic control system performances despite more yellow signal duration. The results clearly shows that proposed method decreases the delay time.
Citation - WoS: 4
Citation - Scopus: 3
Fuzzy Logic and Webster's Optimal Cycle Based Decentralized Coordinated Adaptive Traffic Control Method
(KAUNAS UNIV TECHNOLOGY, 2020) Ali, Muzamil Eltejani Mohammed; Durdu, Akif; Çeltek, Seyit Alperen; Gültekin, Seyfettin Sinan
Traffic control systems for an urban traffic management play an important role in reducing congestion and the negative effects of social and economic aspects. In this paper, the coordinated control method for an arterial road network is proposed. The proposed method is based on fuzzy logic and Webster optimum cycle formula. It is a cyclic method, which means that all-feasible phases at the intersection are get at least a minimum green signal during each cycle. These minimum green times can be used for pedestrian crossing purposes. This method eliminates the starvation that occurs at minor roads due to the non-cyclic strategy. The proposed method is investigated in both coordinated and isolated circumstances. It is compared with non-optimized fixed time control and the cyclic backpressure strategy suggested in the literature. The cyclic backpressure strategy was selected due to its similarity with our proposed method. Based on the obtained results, the adaptive fuzzy logic and Webster based coordinated method outperforms the other methods in terms of the average of waiting time, travel time, travel speed, and queue lengths. In addition, the result achieved from a coordinated situation slightly superior that obtained from isolated situation, which means the proposed method provides good performance both in an isolated and coordinated situations.
Citation - WoS: 4
Citation - Scopus: 4
A Novel Adaptive Traffic Signal Control Based on Cloud/Fog Computing
(Springer, 2022) Çeltek, Seyit Alperen; Durdu, Akif
This paper proposes the Internet of Things-based real-time adaptive traffic signal control strategy. The proposed model consists of three-layer; edge computing layer, fog computing layer, and cloud computing layer. The edge computing layer provides real-time and local optimization. The middle layer, which is the fog computing layer, performs a real-time and global optimization process. The cloud computing layer, which is the top layer, acts as a control center and optimizes the parameters of the fog layer and the edge layer. The proposed strategy uses the Deep Q-Learning algorithm for the optimization process in all three layers. This study employs the SUMO traffic simulator for performance evaluation. These results are compared with the results of adaptive traffic control methods. The output of this study shows that the proposed model can reduce waiting times and travel times while increasing travel speed.
An Operant Conditioning Approach for Large Scale Social Optimization Algorithms
(Konya Technical University, 2020) Çeltek, Seyit Alperen; Durdu, Akif
The changes that positive or negative results cause in an individual's behavior are called Operant Conditioning. This paper introduces an operant conditioning approach (OCA) for large scale swarm optimization models. The proposed approach has been applied to social learning particle swarm optimization (SL-PSO), a variant of the PSO algorithm. In SL-PSO, the swarm particles are sorted according to the objective function and all particles are updated with learning from the others. In this study, each particle's learning rate is determined by the mathematical functions that are inspired by the operant conditioning. The proposed approach adjusts the learning rate for each particle. By using the learning rate, a particle close to the optimum solution is aimed to learn less. Thanks to the learning rate, a particle is prevented from being affected by particles close to the optimum point and particles far from the optimum point at the same rate. The proposed OCA-SL-PSO is compared with SL- PSO and pure PSO on CEC 13 functions. Also, the proposed OCA-SL-PSO is tested for large-scale optimization (100-D, 500-D, and 1000-D) benchmark functions. This paper has a novel contribution which is the usage of OCA on Social Optimization Algorithms. The results clearly indicate that the OCA is increasing the results of large-scale SL-PSO.
Citation - WoS: 47
Citation - Scopus: 67
Real-Time Traffic Signal Control With Swarm Optimization Methods
(ELSEVIER SCI LTD, 2020) Çeltek, Seyit Alperen; Durdu, Akif; Ali, Muzamil Eltejani Mohammed
Real-time traffic signal control is the control methods that control the traffic signal according to the instant traffic situation. In this paper, it is suggested to optimize the traffic control problem with the swarm-based heuristic optimization algorithms. The proposed methods are tested with the real traffic data obtained from Kilis city in Turkey. The performance is evaluated in real-time via the SUMO traffic simulator. The obtained results are compared with the actual traffic measurement data, and the success of the proposed method is expressed numerically. Finally, it is proved that the particle swarm optimization algorithm and its variance algorithm could be used successfully to optimize the traffic signals control in real traffic. In this study, Social Learning-Particle Swarm Optimization is used as a traffic signal optimizer for the first time in the known literature. (C) 2020 Elsevier Ltd. All rights reserved.
Şehir İçi Trafik Sinyal Ağının Takviyeli Öğrenme Algoritmaları ve Nesnelerin İnterneti Tabanlı Kontrolü
(Konya Teknik Üniversitesi, 2021) Çeltek, Seyit Alperen; Durdu, Akif
Uyarlanabilir trafik sinyal kontrolü, sinyal süresini trafiğin durumuna göre ayarlayan ve trafiği gerçek zamanlı olarak yöneten zorlu bir konudur. Klasik trafik sinyal kontrol sistemlerinin performansı, uygun sinyal planlamasını yapmak için yeterli değildir. Bu tez çalışmasında trafikteki gecikmeyi azaltmak için nesnelerin interneti tabanlı üç katmanlı bir kontrol yöntemi önerilmektedir. Önerilen yaklaşım ile her bir katmanda optimizasyon işlemi gerçekleştirilmektedir. En alt katman olan kenar bilişim katmanı, gerçek zamanlı ve yerel bir optimizasyon sağlamaktadır. Orta katman olan sis bilişim katmanı, gerçek zamanlı ve küresel bir optimizasyon işlemi gerçekleştirmektedir. En üst katman olan bulut bilişim ise alt katmanlardan gelen veriler çevrimdışı olarak değerlendirilmekte ve alttaki iki katmanın performansını arttıracak parametreler araştırılmaktadır. Ayrıca hem izole kavşakta hem de trafik ağında kullanılmak üzere yenilikçi bir yaklaşım önerilmektedir. Literatürdeki yöntemler araçların yalnızca kırmızı sinyal fazından dolayı beklemesinden kaynaklanan gecikme süresini minimize etmeye çalışmaktadır. Oysa kavşaklarda gecikmenin araçların yavaşlaması, hızlanması ve beklemesi olmak üzere üç farklı nedeni vardır. Bu çalışmada üç farklı gecikme türünün de minimize edilmesi hedeflenmiştir. Hızlanma ve yavaşlamadan kaynaklı gecikmeyi azaltmak için uygulanan kavşak bilgisinin yanı sıra bitişik kavşakların verilerini de girdi olarak kullanan bir kontrol önerilmiştir. Bu çalışmada yöntem geliştirmek ve geliştirilen yöntemlerin performanslarını değerlendirmek için SUMO trafik simülatörü kullanılmıştır. Hem izole bir kavşakta hem de çoklu trafik sinyallerinin oluşturduğu trafik ağlarında uygulanabilir bir mimari tasarlanmıştır. Böylece literatüre ve akıllı trafik kontrol sistemlerinin gelişimine büyük bir katkı sağlamak amaçlanmıştır.
Submersible Pump Vortex Detection Using Image Processing Technique and Neuro-Fuzzy
(2020) Durdu, Akif; Orhan, Nuri; Çeltek, Seyit Alperen; Aslan, Muhammet Fatih; Sabancı, Kadir
The vortex means the mass of air or water that spins around very fast that often faced in the agriculture irrigation systems used the pump. The undesired effects like loss of hydraulic performance, erosion, vibration and noise may occur because of the vortex in pump systems. It is important to detect and prevent vortex for the economic life and efficiency of the agriculture pump. The image processing and neuro-fuzzy based novel model is proposed for the detection of a vortex in the deep well pump used in the agriculture system with this paper. The used images and data - submergence, flow rate, the diameter of the pipe, power consumption, pressure values and noise values- is acquired from an experimental pump. The proposed approach consists of three steps; Neuro-Fuzzy Learning, Image Processing and Neuro-Fuzzy Testing. In the first step, the eightytwo data have employed for the training process of the Neuro-Fuzzy. Then, the images derived from a camera placed near the experimental pump are used to detect vortex in the image processing step. Finally, the relevant data to vortex cases have employed for the testing process of the NeuroFuzzy. The result of this study demonstrates that image processing and neuro-fuzzy based design can be successfully used to detect vortex formation. This paper has provided novel contributions in the vortex detection issue such as find out vortex cases by using image processing and NeuroFuzzy. The image processing method has shed light on the studies to be done in the classification of vortexes and the measurement of their strength.