Özhan, Davut
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Ozhan, Davut
Job Title
Doktor Öğretim Üyesi
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Department of Electronics and Automatization / Elektronik ve Otomasyon Bölümü
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Scholarly Output
3
Articles
3
Citation Count
56
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0
3 results
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Now showing 1 - 3 of 3
Article Citation - WoS: 32Citation - Scopus: 38Design and Implementation of a New Contactless Triple Piezoelectrics Wind Energy Harvester(Pergamon-elsevier Science Ltd, 2017) Özhan, Davut; Cottone, Francesco; Uzun, Yunus; Orfei, Francesco; Mattarelli, Maurizio; Ozhan, Davut; Department of Electronics and Automatization / Elektronik ve Otomasyon BölümüThe features of the new designed and constructed harvester are examined. The harvested power of three piezoelectric layers having different masses (i.e. different natural frequencies) has been explored. These layers have the same length around the harvester body, whereas a permanent magnet (PM) attached to the shaft rotates by low speed wind and this PM repels these three piezoelectric layers with a 120 phase shift. Since PM and the PMs located to the tip of the layers do not contact, this system improves the lifetime of the harvester. The measured harvested power in the low wind speeds (i.e. 1.75 m/s) is of the order of 0.2 mu W. The waveform includes many subharmonic and superharmonic components, hence the total harmonic distortion (THD) is found around 130%, which is fairly high due to nonlinear effects. Although the system shows an high THD, the 20% of the signal can be rectified and stored in the capacitor for the use of harvested energy. A scenario has also been created for a resistive load of R-L, =1 M Omega and 100 k Omega for various wind speeds and it has been proven that the harvester can feed the load at even lower wind speeds. In addition, extra power beyond the usage of the load can be stored into the capacitor. The proposed harvester and its rectifying unit can be a good solution for the energy conversion procedures of low-power required machines. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.Article Citation - WoS: 5Citation - Scopus: 5Design and Implementation of a Maximum Power Point Tracking System for a Piezoelectric Wind Energy Harvester Generating High Harmonicity(Sustainability, 2021) Özhan, Davut; Özhan, Davut; Bizon, Nicu; Lopez-Guede, Jose Manuel; Department of Electronics and Automatization / Elektronik ve Otomasyon BölümüIn this work, a maximum power point tracking (MPPT) system for its application to a new piezoelectric wind energy harvester (PWEH) has been designed and implemented. The motivation for such MPPT unit comes from the power scales of the piezoelectric layers being in the order of μW. In addition, the output generates highly disturbed voltage waveforms with high total harmonic distortion (THD), thereby high THD values cause a certain power loss at the output of the PWEH system and an intense motivation is given to design and implement the system. The proposed MPPT system is widely used for many different harvesting studies, however, in this paper it has been used at the first time for such a distorted waveform to our best knowledge. The MPPT consists of a rectifier unit storing the rectified energy into a capacitor with a certain voltage called VOC (i.e., the open circuit voltage of the harvester), then a dc-dc converter is used with the help of the MPPT unit using the half of VOC as the critical value for the performance of the control. It has been demonstrated that the power loss is nearly half of the power for the MPPT-free system, the efficiency has been increased with a rate of 98% and power consumption is measured as low as 5.29 μWArticle Citation - WoS: 6Citation - Scopus: 16Automatic Detection of Brain Tumors With the Aid of Ensemble Deep Learning Architectures and Class Activation Map Indicators by Employing Magnetic Resonance Images(Elsevier, 2024) Turk, Omer; Ozhan, Davut; Acar, Emrullah; Akinci, Tahir Cetin; Yilmaz, Musa; Türk, Ömer; Özhan, Davut; Department of Computer Engineering / Bilgisayar Mühendisliği Bölümü; Department of Electronics and Automatization / Elektronik ve Otomasyon BölümüToday, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.
