WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Department: search.filters.department.Abdullah Gul UniversityPublisher: search.filters.publisher.IEEE

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Now showing 1 - 8 of 8
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Oscillator Phase Noise Impact on Monostatic/Bistatic Space-Borne Sub-THz ISAR
    (IEEE, 2025-05-21) Bekari, Ali; Gashinova, Marina; Bekar, Muge; Martorellai, Marco; Antonioni, Michail; Bekar, Ali; Martorella, Marco; Antoniou, Michail
    This study develops an oscillator phase noise model and analyzes its effects on the performance of spaceborne monostatic and bistatic Inverse Synthetic Aperture Radar (B-ISAR) systems operating at the sub-THz band. The B-ISAR study is of current importance as it can provide a basis for distributed space-based ISAR to enable persistent co-operative space domain awareness (Co-SDA).
  • Conference Object
    Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
    (IEEE, 2024-06-11) Guler, Samet
    Typical range-based wireless sensor network (WSN) localization approaches aim at estimating the sensor node positions by using a set of anchors with known positions. In some applications, assuming the knowledge of the anchors' positions may be impractical, and estimation of the sensors' positions in an arbitrary fixed frame may be sufficient. Considering such scenarios, we propose a WSN localization algorithm by single mobile anchor without self location information. The mobile anchor obtains distance measurements from the sensors while tracking a custom trajectory which is shown to improve the localization performance over time for high signal-to-noise ratio cases. By utilizing two stationary reference nodes within the WSN, the proposed framework generates sensor node position estimation up to translation and rotation with sufficient precision in the absence of global positioning aids. We foresee that the proposed framework can demonstrate benefits in several WSN applications ranging from internet-of-things to service robotics.
  • Conference Object
    Power Factor Improvement of a Permanent-Magnet Vernier Machine with Harmonic Injected Excitation Currents
    (IEEE, 2025-06-11) Karatepe, Hasan Can; Tekgun, Didem
    Permanent-magnet vernier machines (PMVM) are recognized for their high torque density but low power factor (PF) due to high inductive reactance. This paper presents a method for improving the PF of a PMVM by injecting additional harmonics into the excitation currents. This injection is done through the motor drive, unlike many proposed methods for enhancing PF, thus eliminating any modifications needed on the machine's geometric design. In this paper, different sets of harmonic injected currents are fed to a 14-rotor pole 12-slot PMVM with short-pitched coils on Finite Element Analysis (FEA) to demonstrate the effects of individual and combined harmonic currents. Corresponding performance characteristics of each simulation case, such as PF and torque density, are investigated. Simulation results indicate that PF can be improved by the proposed method of harmonic current injection. A comparison with a similarly sized permanent-magnet synchronous machine (PMSM) is made to demonstrate that the proposed method can be an alternative to widely used PMSMs.
  • Conference Object
    Citation - Scopus: 1
    NLP-Driven Fake News Detection: A Machine Learning Perspective
    (IEEE, 2025-05-23) Coban, Mert Korkut; Bakal, Gokhan
    The rapid spread of fake news poses a significant challenge, impacting public opinion, decision-making, and societal trust. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques for robust fake news detection. Using datasets such as ISOT Fake News, WELFake, and Football Fake News, the project employs advanced preprocessing methods and feature extraction techniques, including TF-IDF, Word2Vec, and GloVe. A comprehensive evaluation of machine learning models-Random Forest, Support Vector Machines (SVM), and Neural Networks-was conducted to identify the optimal configuration. Results demonstrate that Random Forest with TF-IDF excels in in-domain detection, achieving an F1-score of 99.70%, while Neural Networks paired with Word2Vec and GloVe embeddings outperform in cross-dataset scenarios. The study highlights the importance of dataset size, domain relevance, and feature representation in achieving high generalizability. These findings provide a scalable framework for combating misinformation on digital platforms.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Influence of Eccentricity Faults on IPM Motor Equivalent Circuit Characteristics
    (IEEE, 2025-06-11) Tekgun, Didem
    Interior Permanent Magnet (IPM) machines are preferred in various modern applications due to their high efficiency, compact design, and reliability. They are especially favored in electric vehicle (EV) powertrains but also play a key role in hybrid vehicles, electric motorcycles, industrial automation systems, robotics, and home appliances such as air conditioners and washing machines. Eccentricity is a critical and challenging issue since it causes an unbalanced airgap magnetic flux and forces, eventually resulting in vibration, noise, and a higher likelihood of motor malfunction over time. This study investigates the effects of eccentricity faults on the motor's magnetic flux density and corresponding equivalent circuit parameters through Finite Element Analysis (FEA). The results show that the two types of eccentricity, static and dynamic, produce noticeable variations in the airgap magnetic flux as well as in key equivalent circuit parameters. Specific equivalent circuit parameters are particularly sensitive to different eccentricity faults, making them key indicators for early fault detection.
  • Conference Object
    In-Pipe Electrical Machine Design for Smart Clean Water Grid Monitoring and Control Stations
    (IEEE, 2025-06-11) Erkan, Murat; Boynuegri, Ali Rifat; Tekgun, Burak
    This study presents the design of an electric machine intended to supply the electrical energy required for the operation of electronic devices and mechanical equipment that form part of a clean water smart grid network powered by renewable energy sources. The proposed machine is a permanent magnet synchronous generator (PMSG), specifically designed to operate under realistic physical and hydraulic conditions within clean water distribution infrastructure. The in-pipe turbine responsible for driving the rotor of the generator was selected based on findings from a symposium paper identified through a comprehensive literature review. The daily energy requirements of the smart grid's electronic and industrial mechanical components were both theoretically estimated and experimentally validated, leading to the selection of a suitable energy storage unit. Pressure data from the clean water distribution line located on the street of the design office was measured and recorded at one second intervals over a 24-hour period. Using this dataset, the optimal hydraulic conditions and time frame for battery charging were identified from the pressure-time profile. A representative duty cycle was then defined, and the performance analysis of the in-pipe permanent magnet synchronous generator was carried out accordingly.
  • Conference Object
    Citation - Scopus: 1
    A Dithered Carrier Level Shifted Sine Pulse Width Modulation Technique for EMI Reduction in Cascaded H-Bridge Multi-Level Inverters
    (IEEE, 2025-06-11) Unal, Semih; Tekgun, Burak
    Growing utilization of high-power equipment, particularly in renewable energy systems and electric vehicle applications, has increased the popularity of multi-level inverters (MLI), owing to their capacity to produce high-fidelity sine wave output, compactness, and readily modifiable control devices. Electromagnetic Interference (EMI) is a prevalent problem associated with MLI topologies. Passive EMI filters can easily eliminate this problem. Still, the bulky components used inside these filters lead to a rise in the system's overall size, weight, and production cost. This work presents a novel modulation technique called dithered carrier level shifted sine pulse width modulation (DCLS-SPWM) with the target of reducing electromagnetic interference in cascaded H-bridge multi-level inverters (CHB-MLIs). This method reduces EMI by diffusing harmonics, typically concentrated in lower frequency bands, into higher stages. In the case of DCLS-SPWM, the carrier signal frequency is dithered over a time interval while maintaining the same overall number of switching events. This destabilizes the steady-state conditions intrinsic to the modulation, resulting in a more uniform harmonic distribution. In this study, a 9-level CHB-MLI simulation is built using MATLAB-Simulink, where each module receives a 100V DC input. The efficacy of the proposed DCLS-SPWM method on harmonic reduction is analyzed and validated.
  • Conference Object
    Citation - Scopus: 1
    A Comprehensive Investigation into Strip Steel Defect Detection Using Traditional Machine Learning and Deep Learning Models
    (IEEE, 2025-05-23) Erkantarci, Betul; Kurban, Rifat; Bakal, Mehmet Gokhan; Kose, Abdulkadir
    The steel manufacturing sector places great importance on guaranteeing the quality of strip steel products, which has led to a thorough investigation of defect detection approaches. This work conducts a comparative analysis of traditional machine learning and deep learning models to determine their efficacy in detecting defects in strip steel. Our analysis is based on a dataset that includes a variety of images of strip steel surfaces showing different types of defects. In this work, we adopt image preprocessing techniques to improve the quality of input images prior to the application of classification methods. We employ traditional ML algorithms including Support Vector Machine and Random Forest, and deep learning model AlexNet Convolutional Neural Networks for effective defect classification. Consequently, we present comparative evaluations that highlight the strengths and weaknesses of each approach, considering accuracy scores.