Yazar "Erkaymaz, Hande" seçeneğine göre listele

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    Detection of directional eye movements based on the electrooculogram signals through an artificial neural network
    (Pergamon-Elsevier Science Ltd, 2015) Erkaymaz, Hande; Ozer, Mahmut; Orak, Ilhami Muharrem
    The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately. (C) 2015 Elsevier Ltd. All rights reserved.
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    Electrooculogram and Diplopia Controlled Fuzzy Direction Detect System
    (Ieee, 2014) Erkaymaz, Hande; Orak, I. Muharrem; Ozer, Mahmut
    Coding of the organ movements have been seen commonly in the hardware designing which is useful for humanity in scientific researches. 5 sense organs which reveal different data with cellular structures, is the most important focus point. Especially, Eye is known as photosensitive sensor organ of living creature. There is a potential difference between cornea and retina of eye. This potential difference is known as electrooculogram (FOG). Nowadays, FOG signals are commonly used in these hardware designs. Obtained potential differences from FOG signals have been converted coded directional movements. Thus, in this study, 4 basic direction movements have been tried to detect with a fuzzy controlled model. It is shown that the fuzzy controlled system as determine the direction can be used successfully. In addition, eye direction movements of squint person are obtained by the system.
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    Energy-exergy-ANN analyses of solar-assisted fluidized bed dryer
    (Taylor & Francis Inc, 2017) Ergun, Alper; Ceylan, Ilhan; Acar, Bahadir; Erkaymaz, Hande
    In this study, a temperature-controlled solar air collector was designed and tested for drying. Solar drying systems have two disadvantages. First one is the lack of ability to store energy and the second one is the lack of temperature control. This study presents the experimental analysis of an air collector that is able to keep the drying air temperature at 40 degrees C even in cases where the level of solar radiation received by the collectors changes. Most of the tests were performed at a solar radiation level ranging from 500 to 900W/m(2) and at an air flow of 3 to 5m/s. The system tested for drying three different crops separately performed 21h of a total of 27-h drying period at or above the temperature set of 40 degrees C. The thermodynamic analysis of the relationship between solar radiation, air temperature, flow, and the produced energy was performed. The relationship between productivity, energy produced, and set temperature was analyzed using distribution charts. Moreover, an artificial neural network model was used to estimate outlet air temperature from the solar collectors based on air flow, solar radiation, and outside air temperature.
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    EOG Based Intelligent Direction Detect System with Pre-Filtering Algorithm
    (Ieee, 2015) Erkaymaz, Hande; Ozer, Mahmut; Kaya, Ceren; Orak, I. Muharrem
    Nowadays, artificial movements have been obtained by utilizing other organs for paralyzed patients. Especially the usage of eye movements for giving message to outside world became popular as a scientific subject. In studies according to eye movements, the Electrooculogram (EOG) signal is used. In this study, the vertical and horizontal FOG signals taken from electrodes, placed around the eyes, have been modelled by using Artificial Neural Networks (ANN) which is one of artificial intelligent technique. The system can sense four main directions (Right, Left, Up and Down) at the same time it can also detect blinking movements. Firstly, the signals have been pre-filtered, amplified and classified by ANN. The performance of recommended model has been demonstrated by analyzing statistical accuracy and confusion matrix according to the features of obtained signal. It has been seen that eye movements can be successfully determined by designed model.