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Öğe Can obstructive apnea and hypopnea during sleep be differentiated by using electroencephalographic frequency bands? Statistical analysis of receiver-operator curve characteristics(Tubitak Scientific & Technological Research Council Turkey, 2011) Ucar, Erdem; Sut, Necdet; Gulyasar, Tevfik; Umut, Ilhan; Ozturk, LeventAim: To investigate whether electroencephalographic (EEG) frequency bands are applicable in distinguishing abnormal respiratory events such as obstructive apnea and hypopnea in patients with sleep apnea. Materials and methods: The polysomnographic recordings of 20 patients were examined retrospectively. EEG record segments were taken from C4-A1 and C3-A2 channels and were analyzed with software that uses digital signal processing methods, developed by the study team. Percentage values of delta, theta, alpha, and beta frequency bands were evaluated through discriminant and receiver-operator curve (ROC) analysis to distinguish between apneas and hypopneas. Results: For the G4-A1 channel, delta (%) provided the highest discriminative value (AUG = 0.563; P < 0.001); on the other hand, alpha (%) gave the lowest discriminative value (AUG = 0.519; P = 0.041). Likewise, whereas for the C3-A2 channel delta (%) gave the highest discriminative value (AUG = 0.565; P < 0.001), alpha produced the lowest discriminative value (AUG = 0.501; P = 0.943). Conclusion: As a result of discriminant analysis, the accurate classification rate of hypopneas was 44.8% and the accurate classification of obstructive apneas was 63.5%. Of the 4 frequency bands, the most significant was delta. The predictive values were not at significance level.Öğe DETECTION OF DRIVER SLEEPINESS AND WARNING THE DRIVER IN REALTIME USING IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES(Lublin Univ Technology, Poland, 2017) Umut, Ilhan; Aki, Ozan; Ucar, Erdem; Ozturk, LeventThe aim of this study is to design and implement a system that detect driver sleepiness and warn driver in real-time using image processing and machine learning techniques. Viola-Jones detector was used for segmenting face and eye images from the cameraacquired driver video. Left and right eye images were combined into a single image. Thus, an image was obtained in minimum dimensions containing both eyes. Features of these images were extracted by using Gabor filters. These features were used to classifying images for open and closed eyes. Five machine learning algorithms were evaluated with four volunteer's eye image data set obtained from driving simulator. Nearest neighbor IBk algorithm has highest accuracy by 94.76% while J48 decision tree algorithm has fastest classification speed with 91.98% accuracy. J48 decision tree algorithm was recommended for real time running. PERCLOS the ratio of number of closed eyes in one minute period and CLOSDUR the duration of closed eyes were calculated. The driver is warned with the first level alarm when the PERCLOS value is 0.15 or above, and with second level alarm when it is 0.3 or above. In addition, when it is detected that the eyes remain closed for two seconds, the driver is also warned by the second level alarm regardless of the PERCLOS value. Designed and developed real-time application can able to detect driver sleepiness with 24 FPS image processing speed and 90% real time classification accuracy. Driver sleepiness were able to detect and driver was warned successfully in real time when sleepiness level of driver is achieved the defined threshold values.Öğe Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography(Hindawi Ltd, 2016) Umut, Ilhan; Centik, GuvenThe number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.Öğe Effects of using a force feedback haptic augmented simulation on the attitudes of the gifted students towards studying chemical bonds in virtual reality environment(Taylor & Francis Ltd, 2017) Ucar, Erdem; Ustunel, Hakan; Civelek, Turhan; Umut, IlhanThe aim of this study is to identify the effects of force feedback haptic applications developed in virtual reality environments (VREs), which is an important field of study in computer science and engineering, on gifted students' attitudes towards chemistry education in learning process. A 3D 6 DOF (Degree of Freedom) haptik device (Phantom Omni) has been used to develop the algorithm in this study. It can be used to transmit force and motion using a haptic device. Visual C++ was choosen as the software development environment. OpenGL and Haptic Device Application Programming Interface have been used for rendering graphics. At the 3D image creation state Wrap 1200, which is a kind of head-mounted display, has been chosen. The sample of this study consists of 52 students identified as gifted and are attending 6th and 7th grades at the Istanbul Science & Art Center in Istanbul. The experimental group studied chemical bonds using an application developed by using a force feedback haptic device in VRE and the control group studied it by traditional teaching methods. The study reveals that there is a relation between using force feedback haptic applications which are developed in VREs and gifted students' attitudes towards educational programs.Öğe The factors effecting students' PC game types preferences(Elsevier Science Bv, 2012) Ustunel, Hakan; Meral, Mustafa; Ucar, Erdem; Umut, IlhanThe aim of this study is to investigate the attitude of students' pc game and theme preferences (action, strategy, sports etc.). The data were collected from 722 students at the age of 11-14 in Istanbul and was analyzed using by Weka 3.7.0 that is a popular suite of machine learning software. At this point some classifiers and learning algorithms were used for determining which students play which types of games. the study reveals that there is a relation between students PC game types preferences and their demographic characteristics and the independent variables. The software predicts the game types truly in acceptable ratio.Öğe Monitoring of electricity generation from exhaust waste heat and wireless data recording from a mobile phone in real driving conditions of a vehicle(Springer Heidelberg, 2023) Akal, Dincer; Umut, IlhanIn this study, a system is designed to generate electrical energy from the exhaust waste heat of vehicles using a thermoelectric generator. Electronic hardware that can communicate wirelessly, firmware, and mobile software specific to the system have been developed to control and monitor this system. The system comprises hexagonal aluminum components, thermoelectric generators, a cooler, sensors, software, and electronic hardware. The easily removable hexagonal modular aluminum component is designed to transmit heat from the exhaust pipe to thermoelectric generators. It used a thermoelectric generator (TEG-SP1848) on each edge of this hexagonal component and a heatsink to cool the generator. The voltage and current values of the electrical energy produced in the observations made under real driving conditions are recorded on the SD card on the system. In addition, system-specific mobile software has been developed by the work team. With this software, the system can be controlled, as well as visualizing the instantaneous parameters of the system. According to the results obtained from the test drives, electrical energy was obtained at a maximum voltage of 9.8 V and a current of 0.32 A. This electrical energy from the exhaust waste heat can be stored in the vehicle's existing battery. In this way, since the alternator used for the vehicle's electricity generation will be activated less, fuel savings will be achieved in the engine, and harmful exhaust emissions will be reduced. In addition, the electrical energy obtained by this method can be stored in an external battery independent of the vehicle battery and used for various purposes. In contrast, the vehicle is stationary or has a portable battery.Öğe A novel thermoelectric CPU cooling system controlled by artificial intelligence(Gazi Univ, Fac Engineering Architecture, 2024) Umut, Ilhan; Akal, DincerFigure A shows the components of the additional TEC unit designed in addition to the CPU cooling fan. In order to realize the heat transfer by conduction between the TEC unit and the CPU, the aluminum plate (TEC Module Interface Connection), seen in (Figure A), is designed. On the plate used in this TEC unit, there are thermoelectric module (TEC-12706), heatsink and fan. Since the temperature of the thermoelectric cooler will always be lower than the CPU temperature, effective cooling will be ensured.Purpose: Temperature rise in computers is an undesirable situation that occurs depending on the processor load. Due to excessive temperature rise in the Central Processing Unit (CPU), computers shut down and system damage occurs over time. In this study, a new thermoelectric cooling system is designed to reduce the temperature in the CPU. In addition, 3 different artificial intelligence models were created for the dynamic control of the system and their successes were compared.Theory and Methods: The new cooling system is designed using a thermoelectric module. It is to remove the excess heat by conduction and convection by taking advantage of the temperature difference between the thermoelectric cooler and the CPU we add to the system. Since the temperature of the thermoelectric cooler will always be lower than the CPU temperature, effective cooling will be provided. A special electronic circuit and software have been developed for the control of the cooling unit. Three different artificial intelligence models (artificial neural network, random forest, and k-nearest neighbor) were created to dynamically control the additional cooling system and their successes were compared. Artificial intelligence determines the power and fan speed of the thermoelectric cooling system. It performs this control by evaluating all parameters (different values such as CPU frequency, voltage, number of processes) instead of a specific CPU load or a specific temperature value.Results: While the CPU temperature was 41 & DEG;C at maximum load, this temperature was reduced to 310C thanks to the designed thermoelectric cooling system. All methods provided a high classification success in training. However, the classification success of the artificial neural network method (97.973%) is higher than the random forest (97.297%) and the k-nearest neighbor (96.306%). Conclusion: In the standard CPU fan, the CPU temperature at maximum load was 41 & DEG;C and the maximum energy consumed by the fan for cooling was 8 Watts. Thanks to the developed thermoelectric cooler system, the CPU temperature was reduced to 31 & DEG;C and the energy difference for this process was maximum 12 Watts, at maximum load.Öğe PSGMiner: A modular software for polysomnographic analysis(Pergamon-Elsevier Science Ltd, 2016) Umut, IlhanPurpose: Sleep disorders affect a great percentage of the population. The diagnosis of these disorders is usually made by polysomnography. This paper details the development of new software to carry out feature extraction in order to perform robust analysis and classification of sleep events using polysomnographic data. The software, called PSGMiner, is a tool, which visualizes, processes and classifies bioelectrical data. The purpose of this program is to provide researchers with a platform with which to test new hypotheses by creating tests to check for correlations that are not available in commercially available software. The software is freely available under the GPL3 License. Method: PSGMiner is composed of a number of diverse modules such as feature extraction, annotation, and machine learning modules, all of which are accessible from the main module. Using the software, it is possible to extract features of polysomnography using digital signal processing and statistical methods and to perform different analyses. The features can be classified through the use of five classification algorithms. PSGMiner offers an architecture designed for integrating new methods. Comparison with existing methods: Automatic scoring, which is available in almost all commercial PSG software, is not inherently available in this program, though it can be implemented by two different methodologies (machine learning and algorithms). While similar software focuses on a certain signal or event composed of a small number of modules with no expansion possibility, the software introduced here can handle all polysomnographic signals and events. Conclusions: The software simplifies the processing of polysomnographic signals for researchers and physicians that are not experts in computer programming. It can find correlations between different events which could help predict an oncoming event such as sleep apnea. The software could also be used for educational purposes. (C) 2016 Elsevier Ltd. All rights reserved.Öğe The Role of Enjoyment in the Effect of Music on Cognitive Functions(Wiley, 2022) Kurtaran, Nurcan Erdogan; Ozturk, Levent; Kurtaran, Mehmet; Umut, Ilhan[Abstract Not Available]Öğe Techno-Economic Evaluation of a Hybrid PV-Wind Power Generation System(Taylor & Francis Inc, 2013) Dursun, Bahtiyar; Gokcol, Cihan; Umut, Ilhan; Ucar, Erdem; Kocabey, SureyyaThis article investigates the possibility of providing electricity from solar/wind hybrid systems for a remote location with no electricity connection in the city of Edirne in the westernmost province of Turkey in order to decrease the high cost of utilizing only stand-alone diesel system and to achieve considerable fuel savings. In this study, National Renewable Energy Laboratory's (NREL) HOMER (Hybrid Optimization Model for Electric Renewables) software is used to perform the techno-economic feasibility of such hybrid systems considering both solar data and wind data. Additionally, the contribution of solar and wind on energy production, cost of energy, and total operating hours of diesel generator is examined for the optimal hybrid configurations. Furthermore, fuel savings and reduction in carbon emissions of different hybrid systems are investigated. Finally, suitability of utilizing hybrid solarwind energy system over stand-alone diesel system is discussed mainly based on different solar global irradiances, wind speed, and diesel price.Öğe Using Artificial Intelligence Methods for Power Estimation in Photovoltaic Panels(Univ Namik Kemal, 2022) Akal, Dincer; Umut, IlhanThe limited reserves of fossil resources, the fluctuations in their prices and the damage they cause to the environment have led countries to seek alternatives to primary energy resources. Solar energy, which is an unlimited and environmentally friendly resource, is a powerful alternative to other energy sources. The majority of the European Union countries offer various opportunities to consumers in electricity generation from solar energy with many incentive mechanisms and ensure their widespread use. In many parts of the world, interest in renewable energy sources such as solar, wind, hydrogen and geothermal is also growing. In addition to all these, researches are continuing to use alternative energy sources and to make energy production more efficient. The radiation value required to obtain electricity from solar energy varies according to the weather conditions during the day and seasonal characteristics. The climatic conditions in the area where solar power plants are installed directly affect the output power and energy cost to be obtained from photovoltaic panels. Estimating the output power produced from photovoltaic panels according to environmental conditions, guiding companies in the installation of solar energy systems, obtaining maximum energy, energy management and efficient operation of the system are of great importance. In this study, feedforward back propagation artificial neural networks and KNN (K-Nearest Neighbors) methods were used to estimate power values using the data (Temperature, Humidity, Pressure, Radiation) obtained from the installed photovoltaic panels. Thus, the panel values obtained under real field conditions were trained with both methods and the results were compared. As a result, the power values of the panel were classified using the artificial neural network model developed with the highest accuracy of 98.7945%. It has been seen that the machine learning models used for solar energy estimation developed within the scope of this study have high performance and can produce results very close to the real values. In addition, it was concluded that both artificial intelligence models developed in locations with different characteristics according to the determined load demand can be used.Öğe Verdi A (432 Hz) versus Standard A (440 Hz) in Music-related Electrical Activity of Brain: An EEG Analysis of Sultaniyegah Agir Semai Composed by Hammamizade Ismail Dede Efendi(Wiley-Blackwell, 2016) Ozturk, Gulnur; Bulut, Erdogan; Umut, Ilhan; Ozturk, Levent[Abstract Not Available]Öğe Vestibular Evoked Myogenic Potentials and Electroencephalography in the Presence of Musical Versus Non-Musical Verbal Stimuli(Wiley, 2017) Kizil, Tugba; Bulut, Erdogan; Umut, Ilhan; Ozturk, Gulnur; Ozturk, Levent[Abstract Not Available]
