Bilgisayar Mühendisliği

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Browsing by Author "Altundoğan, Turan Göktuğ"

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    Performance analysis of EEG signal processing based device control applications
    (Institute of Electrical and Electronics Engineers Inc., 2018-09) Altundoğan, Turan Göktuğ; Karaköse, Mehmet; Altundoğan, Turan Göktuğ; Fakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
    Nowadays, many types of devices are controlled by electroselenography (EEG) signals. In the literature and in daily life, related studies with EEG controlled devices are increasing day by day. EEG based control applications are applied on many devices such as robot arm, robot, vehicle and unmanned aerial vehicle (UAV). EEG based control procedures usually involve taking, pre-processing, classifying EEG signals, and applying the resulting command to the controlled device. In this study, a performance analysis was carried out by examining the control application studies using EEG signals in the literature. In this analysis study, firstly all studies related to the subject in the literature are examined and the devices, methods, signal processing techniques and classification algorithms used in these studies are handled separately. Appropriate electrode selection for the type of device used in device control applications using EEG signals and type of interaction for command extraction from EEG signal appears to be an important step. In this respect, performance correlations between the types of EEG devices used in the literature studies and the electrode choices used in these studies were compared. Since there are a variety of preprocessing steps for EEG signals, this study provides comparisons based on EEG signal preprocessing techniques. Artificial neural networks (ANN), support vector machines (SVM) and K nearest neighbours (Knn) are used to classify the works in the literature. In this study, comparative studies based on classification methods used in literature studies are also included. As a result, in this study, the studies in the literature for the device control using the EEG signal are examined, compared, interpreted and evaluated, and the points to be considered in the designs to be performed in this area are given.
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    An approach for online weight update using particle swarm optimization in dynamic fuzzy cognitive maps
    (Institute of Electrical and Electronics Engineers Inc., 2018-09-20) Altundoğan, Turan Göktuğ; Karaköse, Mehmet; Altundoğan, Turan Göktuğ; Fakülteler > Mühendislik Ve Doğa Bilimleri Fakültesi > Bilgisayar Mühendisliği Bölümü
    Fuzzy cognitive maps (FCM) is a method to update a given initial vector to obtain the most stable state of a system, using a neighborhood of weights between these vectors and updating it over a series of iterations. FCMs are modeled with graphs. Neighbor weights between nodes are between -1 and 1. Nowadays it is used in business management, information technology, communication, health and medical decision making, engineering and computer vision. In this study, a dynamic FCM structure based on Particle Swarm Optimization (PSO) is given for determining node weights and online updating for modeling of dynamic systems with FCMs. Neighborhood weights in dynamic FCMs can be updated instantly and the system feedback is used for this update. In this work, updating the weights of the dynamic FCM is a PSO based approach that takes advantage of system feedback. In previous literature suggestions, dynamic FCM structure performs the weight updating process by using rule-based methods such as Hebbian. Metaheuristic methods are less complex and more efficient than rule-based methods in such optimization problems. In the developed PSO approach, the initialize vector state of the system, the weights between the vector nodes, and the desired steady state vector are taken into consideration. As a fitness function, the system has benefited from the convergence state to the desired steady state vector. As a stopping criterion for PSO, 100 * n number of iteration limits have been applied for the initial vector with n nodes. The proposed method has been tested for five different scenarios with different node counts.