Yazar "Umut, Ilhan" seçeneğine göre listele

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    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, Ilhan
    In 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.
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    Novel Wearable System to Recognize Sign Language in Real Time
    (Mdpi, 2024) Umut, Ilhan; Kumdereli, Umit Can
    The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the na & iuml;ve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.