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Öğe Akıllı fabrikalarda dağıtılmış kontrol sistemleri uygulaması ve RFID yaklaşımı(2017) Bozkurt, Üzeyir İlbay; Durdu, AkifGünümüzde endüstriyel uygulamalar otomatik kontrol sistemleri üzerine kurulmuştur. Temel amaç, bu kontrol sistemlerini ve endüstriyel fabrikaları akıllı hale getirmektir. Bu fabrikalar akıllı hale getirilerek yüksek düzeyde verimlilik elde edilebilir. Bilindiği gibi otomasyon sektörü endüstri 4.0 olarak bilinen sanayi devrimine hazırlanmaktadır. Bu sanayi devrimi, bilişim teknolojileri ile endüstriyi bir araya getirmeyi amaçlamaktadır. Artık makinelerin birbirleriyle konuştuğu bir ortam oluşturulmaya çalışılmaktadır. Bu devrimin en önemli yapıtaşlarından biri Radyo Frekansı ile Tanımlama (RFID) teknolojisidir. RFID ile fabrika ve malzeme iletişimi sağlanmaktadır. Bu sayede insan müdahalesine gerek kalmadan fabrikalar üretilen ürünlerle iletişim kurabilir. Akıllı fabrikalarda sistemleri dağıtılmış biçimde kontrol etmek birçok avantaj sağlamaktadır. Bunların en önemlisi veri hızı, güvenirlilik, doğruluk ve maliyet kazancıdır. Bu çalışmada örnek bir fabrika ortamı dağıtılmış şekilde kontrol edilerek, akıllı bir yaklaşım gerçekleştirilmiştir. Ayrıca RFID ile sistem optimizasyonuna da katkıda bulunulmuştur.Öğe Classification of EEG Signals Using Spiking Neural Networks(IEEE, 2018) Tahtirvanci, Aykut; Durdu, Akif; Yilmaz, BurakIn signal processing applications of conventional artificial neural networks, the processing time of the data is high and the accuracy rates are not good enough. At the same time, time-dependent processing is not possible. In this study, classification of EEG signals was performed using an artificial neural network including the characteristics of spiking neural networks. Successful results were obtained using large data sets. Moreover, by using the neuron model of Eugene M. Izhikevich as the spiking neural network model, the EEG signals were processed biologically realistically.Öğe Comparison of ELM and ANN on EMG Signals Obtained for Control of Robotic-Hand(IEEE, 2018) Kayabasi, Ahmet; Yildiz, Berat; Aslan, M. Fatih; Durdu, AkifThe intelligent robotics industry is evolving and humanoid robots can be made, as well as being able to perform the physical functions of people. Robotic hands are vital for many people at this point. In this study, it was aimed to classify the Electromyography (EMG) signals received from the human arm in the correct direction and then to perform robotic hand application. This is very important in understanding and classifying the geometric structure of the object held in robotic hand applications. The classification time and accuracy ratio between ANN and ELM used for this classification were investigated. For this, 11 features were extracted and classifications were tried by using Extreme Learning Machine (ELM) and Artificial Neural Networks (ANN). The obtained successful classification results were compared with each other and applied to a robotic-hand.Öğe Comparison of optimal path planning algorithms(IEEE, 2018) Korkmaz, Mehmet; Durdu, AkifThis work is concerned with path planning algorithms which have an important place in robotic navigation. Mobile robots must be moved to the relevant task point in order to be able to fulfill the tasks assigned to them. However, the movements planned in a frame or random may affect the duty time and even in some situations, the duty might be failed. When such problems are taken into consideration, it is expected that the robots should go to the task point and complete their tasks within the shortest time and most suitable way. It is aimed to give results about a comparison of some known algorithms. With this thought, a map for a real time environment has been created and the appropriateness of the algorithms are investigated with respect to the described starting/end points. According to the results, the shortest path is found by the A* algorithm. However, it is observed that the time efficiency of this algorithm very low. On the other hand, PRM algorithm is the most suitable method in terms of elapsed time. In addition to this, algorithm path length is closer to the A* algorithm. The results are analyzed and commented according to the statistical analysis methods.Öğe Comparison of the SLAM algorithms: Hangar experiments(E D P SCIENCES, 2016) Korkmaz, Mehmet; Yilmaz, Nihat; Durdu, AkifThis study purposes to compare two known algorithms in an application scenario of simultaneous localization and mapping (SLAM) and to present issues related with them as well. Mostly used SLAM algorithms Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are compared with respect to the point of accuracy of the robot states, localization and mapping. Because of considering the most implementations in the previous studies, the simulation environments are chosen as big as possible to provide reliable results. In this study, two different hangar regions are tried to be simulated. According to the outcomes of the applications, UKF-based SLAM algorithm has superior performance over the EKF-based one, apart from elapsed time.Öğe Dıjkstra Algorıthm Usıng Uav Path Plannıng(Selçuk Üniversitesi Mühendislik Fakültesi, 2020) Dhulkefl, Elaf; Durdu, Akif; Terzi̇oğlu, HakanThe use of unmanned aerial vehicles (UAV) is increasing today. UAVs can be divided into two parts, which are remote controlled and can travel automatically due to a certain battery problem. Recent research has also focused on the development and application of new algorithms to autonomously control these vehicles and determine the shortest flight paths. Together with these researches, UAVs are used in many civil activities such as weather forecasts, environmental studies and traffic control. Three-dimensional (3D) path planning is an important issue for autonomously moving UAVs. The shortest path for Unmanned Aerial Vehicles (UAV) is determined by using two-dimensional (2D) path planning algorithms using the obstacles in the environment, and allows UAVs to perform their environmental tasks as soon as possible. The purpose of this study is to determine the shortest path to the target point and avoiding obstacles for UAVs using the Dijkstra algorithm. It was developed to evaluate the arrival time of the UAVs in the path planning algorithm with the simulation performed in the MATLAB program. In this study, the obstacles were defined for the purpose of the building with different heights and different widths and 2D and 3D models were carried out, assuming that the UAV flies at certain heights. In addition, the flight of the UAVs in the route planning determined in the real applications was carried out and the data such as battery consumption, amount of battery spent, speed, amount of travel were examined.Öğe Estimating and reshaping human intention via human robot interaction(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2016) Durdu, Akif; Erkmen, Ismet; Erkmen, Aydan MuserrefHuman robot interaction (HRI) is studied in two important research areas, intention estimation and intention reshaping. Although there are many studies in the literature that define human intention, new research examines the reshaping of human intentions by using robots in HRI. In this paper, 2 different robot movements are tested in a real environment in order to reshape current human intention. The hidden Markov model (HMM) is used to estimate human intention in our intelligent robotic system. The algorithmic design of the system comprises 2 parts: the first part tracks the moving objects in the environment, and the second part estimates human intention and reshapes the estimated current human intention by using intelligent robots. In the first part, a feature vector consisting of the headings of the human posture and the locations of the humans and robots is created by using video processing techniques. The second part is related to estimating the current intention of a human participant via HMM models and to reshaping the current intention into another intention. The system is tested in a real experimental environment including humans and robots, and the results in the recorded videos are given at the end of the paper.Öğe Estimating and reshaping human intention via humanrobot interaction(2016) Durdu, Akif; Erkmen, İsmet; Erkmen, Aydan MüşerrefHumanrobot interaction (HRI) is studied in two important research areas, intention estimation and intention reshaping. Although there are many studies in the literature that define human intention, new research examines the reshaping of human intentions by using robots in HRI. In this paper, 2 different robot movements are tested in a real environment in order to reshape current human intention. The hidden Markov model (HMM) is used to estimate human intention in our intelligent robotic system. The algorithmic design of the system comprises 2 parts: the first part tracks the moving objects in the environment, and the second part estimates human intention and reshapes the estimated current human intention by using intelligent robots. In the first part, a feature vector consisting of the headings of the human posture and the locations of the humans and robots is created by using video processing techniques. The second part is related to estimating the current intention of a human participant via HMM models and to reshaping the current intention into another intention. The system is tested in a real experimental environment including humans and robots, and the results in the recorded videos are given at the end of the paper.Öğe Formation Morphing of Multi-Robots Using Graph Theory: Fugitive Chasing(INT ASSOC ENGINEERS-IAENG, 2015) Erdogan, Kemal; Korkmaz, Mehmet; Durdu, Akif; Yilmaz, Nihat; Topal, SebahattinIn this study, it is considered the case of chasing escapers using graph theory method with multi robots, which are connected to each other. In this simulation study the fugitives are trying to escape from a campus region which has five possible gates for entrance and exit. Because of not determining the possible escape ways of fugitives, it is hard to obtain useful results from police or security chase in a quick time. In this approach, some security robots are waiting in stand-by position to get a new command for positioning in a specified area. Happening of any undesired case, robots are informed by an operator which gate is the alarmed gate. At this point, multi-robots are positioned by using graph theory if the communication range is appropriate to connect each other. This imaginary scenario is supposed to take place in the campus of Selcuk University. There are five gates in the campus that are used for vehicle and pedestrian entrance and exit. For any unusual events similar to this simulation like shooting or robbery, security robots would be alarmed. After alarm case, the backup team would be sent to the gates to start the aerial chasing of escapers.Öğe Imitation and Learning of Human Hand Gesture Tasks of the 3D Printed Robotic Hand by Using Artificial Neural Networks(IEEE, 2016) Ergene, Mehmet Celalettin; Durdu, Akif; Cetin, HalilIn this study social learning and skill acquisition of a robotic hand via teaching and imitation was aimed. The subject of Human-Robot collaboration, which includes the theme of this paper, is a common field of experiments in our age of technology. Many disabilities can be defeated or many other things, which a human being would not be able to do, can be done with the help of this technology. As an example, a robotic hand can be a light of hope of a person who does not have a hand or wants to hold an object remotely over the internet. So that in our paper it is explained how a robotic hand can learn via imitation. In the experiment a robotic hand, which was printed by a 3D printer, was used and controlled wirelessly by a computer that recognizes human hand gesture via image processing algorithms. The communication between the computer and the robot is provided with a Bluetooth module. First of all, the image processing algorithms such as filtering and background subtraction were applied to the frames of the camera and extracted the features. Secondly, the process of teaching and testing of Artificial Neural Networks (ANNs) was made for the recognition of the hand and the gestures. After that, recognized actions were imitated by the robotic-hand hardware. Eventually, the learning of the robot via imitation was achieved with some small errors and the results are given at the end of the paper.Öğe Intention Recognition Using Leap Motion Controller and Artificial Neural Networks(IEEE, 2016) Erdogan, Kemal; Durdu, Akif; Yilmaz, NihatIntention recognition is an important topic in the field of Human Robot Interaction. If the robot is wanted to make counter movements just in time according to human's actions, a robotic system must recognize the intention of the human necessarily. In this paper, a method for a robotics system to estimate the human's intention is presented. In our method, the information is provided from the sensor called as leap motion controller device. The decision about the tendency of human intention is made by Artificial Neural Network. A scenario has been designed that a human subject tries to pile the boxes on each other. The main point for this robotic system and the scenario is to recognize the intention as which box would be held by the subject.Öğe A New Approach to Mobile Robot Navigation in Unknown Environments(IEEE, 2018) Abafogi, Motuma; Durdu, Akif; Akdemir, BayramSeveral algorithms have been developed to help guide mobile robots in unknown environments. Various kinds of Bug algorithms are available and each one these algorithms has an advantage over the others under different circumstances. This paper introduces a new approach, the Diligent-Bug (D-Bug) algorithm, which is developed to enable a collision free navigation of robots in an unknown 2-dimensional environment. Static obstacles of arbitrary shapes have been considered to evaluate the developed algorithm. This algorithm also enables robots to avoid getting stuck in both local and global loops.Öğe A new method for skull stripping in brain MRI using multistable cellular neural networks(SPRINGER LONDON LTD, 2018) Yilmaz, Burak; Durdu, Akif; Emlik, Ganime DilekThis study proposes a new method on "detecting brain region in MRI data". This task is generally named as "skull stripping" in the literature. The algorithm is developed by using the cellular neural networks (CNNs) and multistable CNN structures. It also includes a contrast enhancement and noise reduction algorithm. The algorithm is named as multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). Three different case studies are performed for measuring the success of the algorithm. Also a fourth case study is performed to evaluate the supporting algorithm, the CEULICA. First two evaluations are performed by using well-known MIDAS-NAMIC and Brainweb databases, which are properly organized Talairach-compatible databases. The third database was obtained from the research and application hospital of Necmettin Erbakan University Meram Faculty of Medicine. These MRI data were not Talairach-compatible and less sampled. The algorithm achieved 0.595 Jaccard, 0.744 Dice, 0.0344 TPF and 0.383 TNF mean values with the Brainweb T1-weighted images and 0.837 Jaccard, 0.898 Dice, 0.0124 TPF and 0.1511 TNF mean values with the MIDAS-NAMIC T2-weighted images. The algorithm achieved 0.8297 Jaccard, 0.9012 Dice, 0.0951 TPF and 0.1225 TNF mean values and achieved with the obtained data the best values among the other algorithms. As a result, it can be claimed that algorithm performs best with the non-Talairach-compatible MRI data due to its nature of performing at cellular level.Öğe An Operant Condıtıonıng Approach For Large Scale Socıal Optımızatıon Algorıthms(Selçuk Üniversitesi Mühendislik Fakültesi, 2020) Çeltek, Seyit Alperen; Durdu, AkifThe 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.Öğe Path planning of mobile robots with Q-learning(IEEE, 2014) Cetin, Halil; Durdu, AkifRobotic systems which rapidly continue its development are increasingly used in our daily life. Mobile robots both draw the map where they may move in their environment and reach to the determined target in the shortest time by going on the shortest way in their prepared map. In this paper, Q-learning-based path planning algorithm is presented to find a target in the maps which are obtained by mobile robots. Q-learning is a kind of reinforcement learning algorithm that detects its environment and shows a system which makes decisions itself that how it can learn to make true decisions about reaching its target. The fact that a mobile robot truly finds targets that are located on different points in a few sample maps by processing our proposed Q-learning-based path planning algorithm is shown at the end of the paper.Öğe Petri-Net Based Control of Six Legged Spider Robot(IEEE, 2015) Karakurt, Tolga; Durdu, Akif; Dursun, Emre HasanA legged robot inspired by spider is needed to access to survivor in search and rescue operations. This paper proposes to control system is based on petri net for six legged spider robot which is used for search and rescue operations. The robotic system is tested by using different walking algorithms. Control of the robot is provided by communication ports on computer. The performance of the robot is calculated entirely, as depending on the movement of six legs on rough terrain. Functional algorithms are created to be moved the robot flexible under difficult conditions such as rough terrain, pit. Also, these algorithms which provide moving of robot at various speeds according to structure of legs are presented. The robot controlled in the project is named as TKSPIDER1 and each leg of it has three servo motors.Öğe Position Control by using PD Type Fuzzy Logic: Experimental Study on Rotary Servo System(IEEE, 2016) Dursun, Emre Hasan; Durdu, AkifIn this paper, real-time position control of rotary servo system is performed by using fuzzy logic. SRV-02 DC servo system produced by Quanser is equipped with a DC motor. Servo system is loading by using various metallic weights and performance analysis are made according to international performance criteria. When making comparison of performance, it is seen that controller which is designed via fuzzy logic shows better results on position control than PID control which is a conventional control method and commonly used industrial applications.Öğe REAL-TIME IMPLEMENTATION OF ORB-BASED PROBABILISTIC 3D MAPPING(ST JOHN PATRICK PUBL, 2017) Korkmaz, Mehmet; Durdu, Akif[Abstract not Available]Öğe Reshapıng Human Intentıon On Human-Machıne Interactıon By Usıng Holograms(Selçuk Üniversitesi Mühendislik Fakültesi, 2020) Erdoğan, Kemal; Durdu, Akif; Ceylan, RahimeDecision making could be critically important for people in some situations. People have intentions to choose a side if it is time to make decision. These intentions are strongly related to the knowledge and experience. But sometimes outer effects could reshape their intentions easily. In this paper, an experimental work is studied for Human-Machine Interaction in which if holograms could change or affect the human intention. And also the question which asks whether people trust on a hologram agent while making decision or not is researched. To study this research, a memory game application is developed and this application is run on Microsoft Hololens device. Hololens is used to maintain the Augmented Reality (AR) environment with holograms. An algorithm with a Finite State Machines (FSM) is developed to manage the response of hologram agent while giving hint to the confused users. The accuracy of the hints changes nonlinearly. 3 different game stages are trained on users to see how they are affected by both virtual and real world noises. According to the results, intention of majority of users was affected by the hologram while making the decisions. Also it is observed that some users who were concentrated too much to memorize the order of objects did not realize the hologram, and some few could not understand the actions of hologram.Öğe Robot Imitation of Human Arm via Artificial Neural Network(IEEE, 2014) Durdu, Akif; Cetin, Halil; Komur, HasanIn this study, a robot arm that can imitate human arm is designed and presented. The potentiometers are located to the joints of the human arm in order to detect movements of human gestures, and data were collected by this way. The collected data named as "movement of human arm" are classified by the help of Artificial Neural Network (ANN). The robot performs its movements according to the classified movements of the human. Real robot and real data are used in this study. Obtained results show that the learning application of imitating human action via the robot was successfully implemented. With this application, the platforms of robot arm in an industrial environment can be controlled more easily; on the other hand, robotic automation systems which have the capability of making a standard movements of a human can become more resistant to the errors.
