Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Enhancing Mode Transition Dynamics in Non-Inverting Buck-Boost Inverters for PV Systems(Institute of Electrical and Electronics Engineers Inc., 2026) Keskinkilic, E.; Tekgun, B.Quasi-single-stage inverters (QSSIs) are notable for their simple structure and bidirectional operation capability in applications such as photovoltaic (PV) systems. Among these QSSI, the non-inverting buck-boost inverter (NIBBI) or four-switch buck-boost inverter (FSBBI) is often preferred due to its ability to perform both step-down and step-up operations. However, when traditional control is used, achieving a smooth transition and efficient conversion becomes challenging as the output voltage approaches the input voltage. The pulse width ratio limitations and non-idealities of active and passive components are the cause of this. In this paper, a comparative analysis of the mode transition techniques in FSBBI is presented using methods available for DC/DC converters. System efficiency and output voltage signal quality are selected as performance metrics. A 2-kW FSBBI is installed and controlled using single, two, modified two, three, and four-mode techniques. Simulation and experimental studies were conducted to validate the results. Based on these studies, the four-mode control technique was observed to be the most effective in eliminating dead zone effects, reducing total harmonic distortion (THD), and achieving the highest system efficiency in a PV system where a battery powers the AC load. Experimental results indicate that the four-mode modulation attained an efficiency of 95.49% with a THD of 2.97%. © 1986-2012 IEEE.Book Part Citation - Scopus: 55Energy Harvesting and Battery Technologies for Powering Wireless Sensor Networks(Elsevier Inc., 2016) Tuna, Gürkan; Güngör, Vehbi ÇağrıDue to the advances in wireless sensor networks (WSNs), factory and plant process automation systems are being reinvented. WSN-based industrial applications often cost much less than wired networks in both the short and long terms; automation engineers are empowering existing solutions with the new capabilities of WSNs. On the other hand, since industrial wireless sensor networks (IWSNs) consist of thousands of nodes, the problem of powering the nodes is critical. Power to the nodes is usually provided through primary batteries and this necessitates replacement when the batteries are depleted. However, the replacement may not be cost-effective or even feasible in most industrial applications.Though advancements in integrated circuit technologies help in saving more energy by leading to lower energy consumption levels, they do not eliminate the use of battery power. In this regard, energy harvesting technologies play a key role in extending the battery lifetime of the nodes. Wireless sensor nodes within industrial plants can operate from energy harvested from available energy sources such as heat, mechanical motion or vibration, indoor lighting, electromagnetic fields, and air flow. In this chapter, a review of existing energy storage technologies and various energy-harvesting techniques is given. The chapter then discusses open research issues in these topics. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 3ATGRUVAE: Reducing Noise and Improving Forecasting Performance in Stock Data(Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Akkaş, Huseyin; Kolukisa, Burak; Bakir-Güngör, BurcuNowadays, to maximize their income, investors and researchers try to predict the future prices of stocks in the market using artificial intelligence algorithms. However, noise in stock price fluctuations negatively a ffects t he accuracy of the forecasts. To this end, Attention Based Variational Autoencoders with Gated Recurrent Units (ATGRUVAE) method is developed to remove the noise in stock price fluctuations a nd compared with variational, basic and noise removing autoencoders. Exper-iments are conducted using historical stock prices of well-known companies such as Apple, Google and Amazon and 9 different indicator values derived from these stock prices. The noise cleaned stocks are then trained and tested on Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Linear Regression (LR) models. The results show that the proposed ATGRUVAE model outperforms all three models and demonstrates its ability to capture complex patterns in stock market data. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2Beyin Dalgalari ve Baş Hareketiyle Gerçek Zamanli Robotik Araba Kontrolü(Institute of Electrical and Electronics Engineers Inc., 2018-11) Oztürk, Nedime; Yilmaz, Bulent; Onver, Ahmet YasinEmotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications. © 2019 Elsevier B.V., All rights reserved.