Yazar "Ozden, Mustafa" seçeneğine göre listele

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    A Comparative Study for Localization of Forgery Regions in Images
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Ozden, Mustafa; Sahin, Canberk
    As computer technologies and image processing software have advanced, it has become progressively easier to produce simple fake or forged images by altering digital images without leaving any discernible trace. There is a significant need to detect manipulated regions in images in crucial fields such as politics, law, and forensic medicine. In this study, we propose a method that combines the traditional techniques, such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), with the advantages of deep learning methods to detect manipulated regions in forged images. The proposed method involves designing an architecture where DWT and DCT are used in parallel with DenseNet based Convolutional Neural Network (CNN). To evaluate the effectiveness of this method, we implemented three alternative approaches: one that uses only DCT and CNN, another that uses only DWT and CNN, and a third that employs only CNN without either transformation. In total, four different methods were tested on eight datasets, and their performance was compared using metrics such as accuracy, precision, recall, dice similarity coefficient, and F1 score. The results from these comparisons clearly indicate the effectiveness and high classification accuracy of the proposed method. By leveraging the combined strengths of traditional image processing techniques and advanced deep learning algorithms, the proposed method demonstrates superior capability in detecting manipulated regions in forged images, thus offering a robust solution for applications in forensic field.
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    Adaptive neuro fuzzy control of a high gain bidirectional power converter for photovoltaic-hydrogen renewable electric vehicles with enhanced lifespan and reliability
    (Elsevier Gmbh, 2026) Ertekin, Davut; Ozden, Mustafa
    The demand for green energy and application of hydrogen or photovoltaic (PV) for electrical vehicles (EVs) are enhancing steadily each day. DC-DC converters are critical power conversion systems that regulate voltage and current levels for battery packs in electric vehicles (EVs) powered by fuel cells (FCs) or PV panels and set the voltage for electric motor through an inverter circuit. The longevity of renewable energy sources (RESs) such as the FCs and PV arrays is heavily influenced by the current drawn by the DC-DC converter. Additionally, the converter topology must be cost-effective, minimize voltage and current stresses on semiconductor devices, offer ease of control, and provide flexible voltage outputs to meet the dynamic demands of the battery pack. This study introduces a switching DC-DC power converter designed specifically for FC-based electric vehicles (FCEVs), controlled by an innovative adaptive neuro fuzzy controller (ANFC). The high gain of the proposed converter enables the energy obtained from FCs and PV cells to be stored in a high-voltage battery pack and subsequently used to drive the electric motor and other electric vehicle components (such as lighting, heating, or cooling). This implies that, in an electric vehicle, it is sufficient to use only the proposed power converter instead of employing separate DC-DC converters for different energy sources such as PV or FCs. Subsequently, the stored energy can be used to operate the motor by providing the input voltage to the inverter. This approach makes the overall system more efficient and cost-effective. At the end of the simulation studies, it was observed that the proposed controller successfully ensures the control of the DC-DC converter, that no overshoot or oscillation occurs at the converter output, that an extremely short settling time of 0.016 s is achieved, and that a very low steady-state error of 0.7 is obtained. Experimental results for the proposed power converter are presented, thereby validating the theoretical findings.
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    Detection of Tobacco Use through Motion Analysis from Camera Images
    (Ieee, 2025) Karabay, Zilan Aze; Ozden, Mustafa
    Tobacco use is a widespread form of addiction affecting the health and lives of millions worldwide annually. This thesis proposes an innovative system based on deep learning techniques for automatic and objective detection of tobacco use. The proposed system consists of two main stages. In the first stage, a robust deep learning model using MediaPipe Pose is developed to detect hand-mouth interactions from video or image data. This model can accurately detect these interactions in real-time. In the second stage, a separate deep learning model is designed to estimate and classify hand movements. This model identifies hand movements by detecting key points of the hand skeleton. The hand movement estimation model can classify specific hand movements associated with smoking behavior (e.g., holding a cigarette, bringing it to the mouth, inhaling) with high accuracy. The performance of the developed system has been evaluated through comprehensive experiments and tests. Tests conducted on different dataset demonstrate that the proposed approach can detect tobacco use with high accuracy rates (above 95%). Moreover, the system's ability to operate in real-time and provide fast response times offers a significant advantage for practical applications. The thesis presents the technical details of the proposed system, including the deep learning architectures used, datasets, preprocessing steps, data augmentation techniques, and experimental results. Additionally, potential future applications of the system, its impact on smoking cessation efforts, and possible improvements are discussed.
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    Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Ozden, Mustafa; Ertekin, Davut; Siano, Pierluigi
    A crucial aspect of DC-DC converters employed in renewable energy sources such as fuel cells is their ability to exhibit substantial increases in DC voltage and maintain an efficient structure while minimizing input current ripple. These factors play a pivotal role in enhancing the longevity of these energy sources and ensuring their compatibility with high-voltage AC and DC grids. This study introduces a high-gain DC-DC step-up converter that incorporates a continuous input current cell and a switched capacitor voltage-boosting output cell to address these requirements. The control process of this proposed converter is executed using an artificial neural network based on the Levenberg-Marquardt learning algorithm. The primary difference in this research lies in obtaining the artificial neural network-based controller directly from the circuit's characteristic equations, rather than generating it through another controller. A feedback control strategy has been formulated, where the artificial neural network consistently produces duty increment values based on the reference voltage. Additionally, the network's input includes not only the reference signal but also the circuit input voltage and output current value. As a result, the stability of the circuit's output voltage is maintained against variations in input voltage and load changes. A laboratory-designed workbench underwent testing, and the experimental results substantiated the theoretical inquiries and simulation outcomes.
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    Neuro-fuzzy-SVPWM switched-inductor-capacitor-based boost inverter for grid-tied fuel cell power generators, design and implementation
    (Pergamon-Elsevier Science Ltd, 2024) Ertekin, Davut; Ozden, Mustafa; Deniz, Adnan; Toprak, Muhammed Zeyd
    Hydrogen energy shows promise as a renewable energy source for various applications like battery and electric vehicle charging stations, as well as grid connections. However, high current ripple from fuel cells (FCs) and inadequate voltages for grid use pose challenges. This study presents a novel solution using neural fuzzy network control in a high-gain DC-DC boost converter to address these issues. The suggested converter charges in parallel and discharges in series, minimizing the current ripple range in the fuel cell network. Additionally, the switchcapacitor cell efficiently increases the output voltage. In this study, a Neuro-fuzzy system with 9 rules is trained meticulously over 50 epochs using hybrid optimization and grid partition methods, achieving a low training error of 0.045 with 522,064 samples. The neural fuzzy network, employing the weighted average method for Defuzzification, produces duty cycle values from 0.02 to 0.5 in response to input signals. Additionally, an innovative Space Vector Pulse Width Modulation (SVPWM) approach within the inverter circuit enhances voltage generation precision and power quality for grid delivery, notably reducing current ripple and ensuring stable power supply. This combined with the neural fuzzy network in the converter efficiently converts hydrogen energy into AC voltage for seamless grid integration, revolutionizing boost converter efficiency and advancing hydrogen energy utilization across various energy sectors.
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    Optimizing Battery Cooling with Reinforcement Learning: A Dynamic Control Strategy for Energy Storage Systems
    (Ieee, 2025) Aksoy, Necati; Cakil, Fatih; Ozden, Mustafa
    The growing reliance on energy storage systems (ESS) in residential, vehicular, and industrial applications necessitates efficient thermal management solutions to enhance performance and longevity. This study proposes a reinforcement learning (RL)-based cooling control system for optimizing the temperature regulation of battery banks. Unlike traditional rule-based or static cooling strategies, the proposed method dynamically adjusts coolant flow rates using an Expected SARSA agent, which learns an optimal control policy through interactions with a custom-designed environment model. The model accurately simulates thermal absorption and coolant flow dynamics, allowing for precise and adaptive cooling regulation. The performance of the RL agent was evaluated across different training durations, demonstrating that extended training significantly improves stability, reward consistency, and energy efficiency. Compared to conventional cooling strategies, the RL-based system ensures more adaptive, efficient, and reliable thermal management, making it a promising solution for next-generation energy storage applications.
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    The Design and Practical Realization of an Adaptable Grid Integrating Hydrogen Fuel Cell Setup With a Fuzzy-Logical Controller-Based SVPWM Boosted Inverter
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Ertekin, Davut; Baltaci, Kubra; Toprak, Muhammed Zeyd; Celebi, Mehmet; Ozden, Mustafa; Siano, Pierluigi
    The primary and fundamental requirement for a fuel cell (FC) stack is its reliable operation under various operating conditions. When FC stacks are used as the input voltage source with high ripple currents, the overall lifespan of the FC system decreases. Hence, power converter configurations need to minimize the current ripples originating from these sources. Additionally, the generated voltage from the FC stack is often lower than the required voltage level for grid connection. This paper presents a fuzzy logic controller (FLC)-equipped high-gain single-switched DC-DC boost converter. The proposed power converter topology utilizes an improved switched inductor and switched capacitor configuration to minimize input current ripples and enhance the voltage gain. The switched inductor cell is designed in such a way that its inductors charge and discharge simultaneously, effectively minimizing the input current ripple. Additionally, the proposed DC-DC boost converter utilizes a switched capacitor cell to double the generated voltage. The FLC offers real-time visualization and digital signal processing capabilities, and it is compatible with MATLAB software. For grid connection purposes, a space vector pulse width modulation (SVPWM)-based switching system is recommended, utilizing a full bridge inverter. The SVPWM technique is implemented by representing the desired output voltage with an equivalent vector VREF rotating counterclockwise, integrated with a digital signal processing (DSP)-based controller. The DSP microcontroller employed in this study operates at an 80 Mb/sec sampling speed and offers several advantages, including the ability to perform complex calculations, implement advanced control algorithms, and process signals in real-time. These capabilities contribute to enhanced performance, efficiency, and accuracy. Laboratory studies have been conducted to validate the accuracy and effectiveness of the theoretical investigations.