WoS İndeksli Yayınlar Koleksiyonu

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

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Start date: 2025Publisher: search.filters.publisher.IEEE

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Now showing 1 - 10 of 11
  • 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
    Enhancing Complex Disease Group Scoring with Mirgedinet: A Multi-Algorithm Machine Learning Framework Based on the GSM Approach
    (IEEE, 2025-06-25) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, Malik
    Integrating biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. This work investigates the application of a multi-algorithm machine learning framework based on the Grouping-Scoring-Modeling (G-S-M) approach for improving the prediction of complex diseases. The study identifies the primary gene and miRNA interactions in various complex diseases with the help of miRGediNET, which is a machine-learning based tool that integrates data from three biological databases. Traditional methods have only focused on independence between features; the G-S-M method focuses on aggregating genes based on biological interactions, pinpointing the scoring of gene groups for a disease, and modeling its predictive capability using advanced machine learning algorithms. In this research paper, seven algorithms, including Support Vector Machine, Decision Tree, and CatBoost, were applied to eight datasets extracted from the GEO database. This framework proved very robust in ranking gene clusters, thus predicting critical biomarkers while doing 100-fold randomized cross-validation within the evaluation. The results indicate this approach's high potential for refining disease and supporting research for choosing the best algorithm that can provide biological insights and computational advances.
  • Conference Object
    High Performance and Resource Efficient Low Density Parity Check Decoder Design
    (IEEE, 2025-06-25) Unal, Burak
    Low Density Parity Check (LDPC) codes have gained popularity in communication systems due to their capacity-approaching error correction performance. In this study, a highperformance LDPC decoding algorithm with extremely low resource usage is proposed. Among the hard decision class of LDPC decoders, Gallager B (GaB) provides high-performance hardware due to its computational simplicity. However, GaB suffers from poor error-correction performance. In this study, a new intrinsic computation technique for GaB called Intrinsic Gallager B (IGaB) is introduced to improve error correction performance. Our simulation results show that the IGaB algorithm provides better error correction performance compared with GaB. GaB and IGaB algorithms are implemented on Field Programmable Gate Array (FPGA) to compare hardware performance.
  • Conference Object
    Exploring Microbiome Signatures in Autism Spectrum Disorder via Grouping-Scoring Based Machine Learning
    (IEEE, 2025-06-25) Temiz, Mustafa; Ersoz, Nur Sebnem; Yousef, Malik; Bakir-Gungor, Burcu
    The rapid increase in omic data production increased the importance of machine learning (ML) methods to analze these data. In particular, the use of metagenomic data in the diagnosis, prognosis and treatment of diseases is becoming widespread. Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that occurs in early childhood and continues lifelong. The aim of this study is to increase ML performance, reduce computational costs and achieve successful classification performance using a small number of metagenomic features. In addition, disease prediction is performed; ASD associated biomarkers are determined using the microBiomeGSM on metagenomic data. Classification is performed at three different taxonomic levels (genus, family and order) using the relative abundance values of species. The best performance metric (0.95 AUC) was obtained at the order taxonomic level using an average of 416 features with microBiomeGSM. The identified ASD-related taxonomic species are presented.
  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Fine Tuning DeepSeek and Llama Large Language Models with LoRA
    (IEEE, 2025-06-25) Uluirmak, Bugra Alperen; Kurban, Rifat
    In this paper, Low-Rank Adaptation (LoRA) finetuning of two different large language models (DeepSeek R1 Distill 8B and Llama3.1 8B) was performed using the Turkish dataset. Training was performed on Google Colab using A100 40 GB GPU, while the testing phase was carried out on Runpod using L4 24 GB GPU. The 64.6 thousand row dataset was transformed into question-answer pairs from the fields of agriculture, education, law and sustainability. In the testing phase, 40 test questions were asked for each model via Ollama web UI and the results were supported with graphs and detailed tables. It was observed that the performance of the existing language models improved with the fine-tuning method.
  • 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.