Doktora Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5800
Browse
38 results
Search Results
Doctoral Thesis FUNCTIONALIZED LOW LUMO [1]BENZOTHIENO[3,2-B][1]BENZOTHIOPHENE (BTBT)-BASED MOLECULAR SEMICONDUCTORS FOR ORGANIC FIELD EFFECT TRANSISTORS(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Özdemir, Resul; Usta, HakanDAcTTs have provided an excellent π-framework for the development of high mobility p-type molecular semiconductors in the past decade. However, n-type DAcTTs are rare and their electron transporting characteristics remain largely unexplored. In the second chapter of this thesis, the first example of an n-type BTBT-based semiconductor, D(PhFCO)-BTBT, has been realized via a two-step transition metal-free process without using chromatographic purification. The corresponding TC/BG-OFET devices demonstrated μe (max) = ~0.6 cm2/Vs and Ion/Ioff ratio = 107-108. The large band-gap BTBT π-core is a promising candidate for high mobility n-type organic semiconductors and, combination of very large intrinsic charge transport capabilities and optical transparency, may open a new perspective for next-generation (opto)electronics. In the third chapter of this thesis, a series of BTBT-based small molecules, D(C7CO)-BTBT, C7CO-BTBT-CC(CN)2C7, and D(C7CC(CN)2)-BTBT, have been developed in “S-F-BTBT-F-S (F/S: functional group/substituent)” molecular architecture. Combining with D(PhFCO)-BTBT, a molecular library with systematically varied chemical structures has been studied herein for the first time for low LUMO DAcTTs, and key relationships have been elucidated. The molecular engineering perspectives presented in this thesis may give unique insights into the design of novel electron transporting thienoacenes for unconventional optoelectronics.Doctoral Thesis A reliable and secure communication design for underwater sensor networks concerning energy efficiency(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) UYAN, Osman Gökhan; Güngör, Vehbi ÇağrıUnderwater Acoustic Sensor Networks (UASNs) recently attract scientists because of its wide range of applications and emerging technology. A design challenge in UASN's is the limited network lifetime and poor reliability caused by limited battery supply of sensors and harsh channel conditions in underwater environment. Moreover, sensors might transmit sensitive data that must be disguised against eavesdropping attacks. To maintain a reliability level, packet-duplication and multi-path routing method are suggested, which renders eavesdropping attacks easier. For data security, cryptographic encryption is the most acclaimed method. However, encryption needs extra computations, which consume extra energy and cause a decrease in the network lifetime. As a countermeasure along with encryption against silent listening, fragmenting data and transmitting in pieces over different paths has been proposed. To address these challenges, an optimization framework has been developed to analyze the effects of multi-path routing, packet duplication, encryption, and data fragmentation on network lifetime. However, the solution time of the proposed optimization model is quite high, and sometimes it cannot come up with feasible solutions. To this end, in this study, different regression and neural network methods have been proposed to predict the energy consumptions of underwater nodes as supplementary methods to optimization models. Performance evaluations show that the proposed methods yield remarkably accurate predictions and can be used for energy consumption prediction in UASNs.Doctoral Thesis Su Altı Dalgıç Pompa Uygulamaları için Doğrudan Yol Vermeli Relüktans Motorunun Sistematik Olarak Tasarım Optimizasyonu ve Gerçeklemesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Tekgün, Didem; Alan, İrfanConsidering the electric drive systems constitute roughly 40% of global energy production, improving electric machine efficiencies provides important nationwide and global scale advantages. Among the electric motors used in the industry, a major portion of them are pump motors used for pumping underground waters and petroleum products. Especially the motors for submersible pump applications run at very low-efficiency levels because of the motor design issues and wrong selection of motor-pump configurations. Due to the features like robustness, low cost, and line start capability, induction machines (IM) are generally the first choice for pump applications. However, IMs work with low efficiency, especially at low and medium power levels. Line start synchronous reluctance machines (LS-SynRM) come to the scene as a reasonable alternative by having the line start capability and not having rare earth permanent magnets as well. The working principle of these machines is a combination of a reluctance machine and an IM. In LS-SynRM, a rotor cage is inserted in the rotor for the machine to start with the line voltage, but the rotor copper losses become zero when the machine operates at synchronous speed. Moreover, SynRMs have higher power and torque density. In this thesis study, it is aimed to reduce the overall cost of the submersible water pump system by designing and optimizing a LS-SynRM as a submersible pump motor with higher efficiency compared to conventional IMs. Increasing the efficiency of the pump motor used in industry will improve the overall system performance. Accordingly, it lowers energy and maintenance costs, and easy process control will be achieved. This way, while reducing energy consumption nationwide significantly, not only the natural resources will be protected, but also huge amounts of money will be saved.Doctoral Thesis Uzaktan Kontrollü İkincil Emisyon İyonizasyon Kalorimetri Modülleri ile Yüksek Enerjili Kozmik ve Gama Radyasyon Ölçümü(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Paran, Nejdet; Tekgün, Burak; Tıraş, EmrahThe demand for precise, robust, and reliable radiation-resistant particle detectors and ionization calorimeters intensifies, due to the escalating luminosity and unprecedented radiation conditions at particle colliders and accelerators. Secondary Emission (SE) Ionization Calorimetry is a novel technology designed to measure the energy of electromagnetic and hadronic particles, particularly in extreme radiation conditions. In this study, we have tested and investigated the development and radiation test of the novel SE modules. The modules were developed by modifying the conventional Hamamatsu single anode R7761 Photomultiplier Tubes. Three different voltage conditions for the same module were developed and the new modules were tested by using cosmic, gamma (Co-60) and neutron (AmBe) radiation sources. The results show that all three modes have good sensitivity to electromagnetic showers, and they are suitable for harsh radiation environments. This study also shows that SE module is a promising technology shedding light on future radiation-resistant nuclear and high-energy detectors. Here, we discuss the technical design, test characteristics and cosmic and particle interaction results of the newly developed SE modules. Since such detector systems are either in a high radiation area or in a closed room/box, remote mode changes allow us to continue the experimental process without interruption. By adding these signals to the interface where the modes are controlled, we can instantaneously observe the modes' effects.Doctoral Thesis Hastalık Tahmini ve Biyobelirteçlerin Tespiti için Makine Öğrenim Modellerinin Tasarımı ve Geliştirilmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Temiz, Mustafa; Güngör, Burcu; Yousef, MalikIn medical science, the prediction of diseases and the identification of biomarkers play an important role in the diagnosis and treatment of various health conditions. The recent proliferation of data mining techniques has accelerated the development of disease prediction systems. In particular, machine learning methods are an effective way to analyze medical data and identify patterns to predict the likelihood of the disease development. Machine learning methods also help to identify biomarkers. Recently, the increasing incidence and mortality rates of inflammatory bowel disease, colorectal cancer and type 2 diabetes have drawn researchers' attention to these research areas. The aim of this thesis is to reduce the number of features and improve the prediction performance of machine learning based on complex biological datasets with a large number of disease-related features, as well as to identify potential biomarkers. In this thesis, three different studies are presented. The first study predicts eleven different cancer subgroups using miRNA data and biological domain knowledge and identifies potential biomarkers for these diseases. The second study predicts three different diseases using metagenomic data and biological domain knowledge and identifies potential biomarkers. The third study uses metagenomic data related to colorectal cancer to conduct global and population-based comprehensive experiments with traditional feature selection methods to identify potential biomarkers. This thesis presents a promising avenue for early disease detection, facilitating expedited treatment protocols, improving human survival rates, and potentially alleviating economic burdens within these critical research domains.Doctoral Thesis Makine Öğrenmesi Tabanlı Ağ Anomali Tespiti(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Hacılar, Hilal; Güngör, Burcu; Güngör, Vehbi ÇağrıIntelligent technologies have led to a significant rise in internet users and applications. However, this rise in internet usage has also brought serious security challenges. Organizations rely on Network Intrusion Detection systems (NIDS) to protect sensitive data from unauthorized access and theft. To enhance the capabilities of IDS, Machine Learning (ML) and Deep Learning (DL) techniques have been increasingly integrated into these systems. In this context, anomaly-based network intrusion detection surpasses other detection mechanisms significantly in several instances. These systems analyze network traffic to detect suspicious activities, such as attempted breaches or cyberattacks. However, existing studies lack a thorough assessment of class imbalances, feature selection and extraction methods, hyperparameter optimization, and classification performance for different types of network intrusions: wired, wireless, and Software Defined Networking (SDN). Additionally, existing methods may achieve high accuracy; they may suffer from high training times, low detection rate (DR), and computational complexity. By combining metaheuristics and neural networks, it is possible to solve complex optimization problems that are challenging to solve using conventional methods. To address these challenges, this thesis study first evaluates different network intrusion datasets, such as wired, wireless, and SDN, together, considering class imbalance, feature selection, and hyperparameter optimization tasks. Secondly, it proposes a novel hybrid approach combining Deep Autoencoder (DAE) and Artificial Neural Network (ANN) models trained by a parallel Artificial Bee Colony (ABC) algorithm with Bayesian hyperparameter optimization.Doctoral Thesis Koloidal Nanomalzemelerin Akıllı Kendinden Dizilimi ile Nanofotonik Mimarilerin Tasarımı ve Uygulaması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Şenel, Zeynep; Erdem, TalhaDNA-driven self-assembly techniques offer precise control over the positioning of colloidal nanoparticles through specific Watson–Crick interactions, and its reversibility via controlling the temperature of medium. This thesis explores an alternative strategy to control DNA-functionalized nanoparticles' binding/unbinding process by leveraging laser radiation, inducing localized heating within the nanoparticles to facilitate disassociation. First, we demonstrate the active manipulation of the optical properties of DNA-assembled gold nanoparticle networks via external optical excitation. Specifically, irradiation with a green hand-held laser yields a substantial ∼30% increase in total transmittance, accompanied by a transition from opaque to transparent states observable in optical microscopy images. The reversibility of this process is demonstrated by the restoration of the nanoparticle network post-irradiation cessation, underscoring the efficacy of optical excitation in tailoring both the structure and optical characteristics of DNA-mediated nanoparticle assemblies. Second, we introduce a method to tailor DNA-driven self-assembly of semiconductor nanoparticles on glass by applying an external optical field. A green laser directs the assembly of DNA-functionalized red-emitting quantum dots (QDs) on DNA-functionalized glass, leaving uncoated spots owing to localized heating. This effect becomes prominent after three hours of radiation using a laser with an irradiance of 57.1 W/cm2. Experiments with different lasers and nanoparticle types confirm the role of laser-induced heating in preventing QD-glass bonding via DNA-DNA interaction. Secondary coating of previously uncoated spots with DNA-functionalized green-emitting QDs and dye-functionalized DNAs indicates a successful hierarchical self-assembly. Our findings highlight the potential of light-assisted DNA-driven self-assembly for diverse nanoparticle architectures, promising applications in optoelectronics and nanophotonics. Keywords: Programmable self-assembly, DNA-driven self-assembly, localized heating, colloidal nanoparticles, DNA conjugation.Doctoral Thesis Merkezi Olmayan Elektronik Sağlık Kaydı Yönetim Sistemi ve Makine Öğrenmesi Yöntemleri ile Hastalık Tahmini(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Dedetürk, Beyhan Adanur; Güngör, BurcuElectronic health records (EHRs) are vital to the advancement of healthcare and can help detect and prevent diseases early. However, EHR sharing faces challenges such as managing large data volumes, ensuring data privacy, security, and interoperability. This thesis aims to develop and analyze a blockchain-based EHR sharing system for disease prediction mechanism integration using SysML. The AguHyper platform, built by merging the InterPlanetary File System (IPFS) with Hyperledger Fabric, ensures the immutability of health records by storing hash values in the blockchain and encrypted records in IPFS. The system architecture and implementation configurations, including CouchDB and the Raft consensus mechanism, are thoroughly examined. The study also presents a novel hybrid approach called CSA-DE-LR, which integrates Differential Evolution (DE) and Clonal Selection Algorithm (CSA) with Logistic Regression (LR) to improve LR weights for precise categorization of cardiovascular diseases. The integration of the AguHyper with the CSA-DE-LR is explained in detail. At the end of our performance evaluations, we concluded that the AguHyper model has the potential to speed up the process of collecting and sharing data, and it offers an efficient platform for the participants.Doctoral Thesis GG (Genişletilmiş Gerçeklik)'in Mimari Tasarım Eğitimine Etkisi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Kıdık, Ayşegül; Asiliskender, BurakThe 'XR (Extended Reality) Impact on Architectural Design Education' dissertation comprehensively examines the integration and impact of Extended Reality (XR) technologies in architectural design experience. As the field of architectural design education struggles with the challenges presented by technological advancements, this research endeavors to explore the potential of XR technologies, which encompass Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), to redefine the design process and enhance the creative capacity of architecture students. In the rapidly evolving landscape of contemporary architecture, architectural design education is paramount in fostering future architects equipped to meet the dynamic demands of the profession. XR technologies have emerged as transformative tools that have the potential to revolutionize how architects engage with their projects, offering immersive and interactive environments for design exploration that have different realities. The methodology employed in this research is varied, combining comprehensive and systematic literature reviews with empirical case studies. This methodological synergy integrates theoretical insights from literature reviews with practical observations from real-world architectural projects, facilitating a comprehensive exploration of XR technologies within the context of architectural design studio education. The literature review encompasses a wide range of topics, including architectural design studio education, the fundamental principles of XR technology, and emerging trends in architectural education. These reviews provide the requisite theoretical framework for comprehending the implications of XR technologies on the design experience. Within the dissertation, systematic literature reviews are conducted on VR, AR, MR, and XR technologies, thereby shedding light on their integration into architectural design studio education. These reviews synthesize existing research findings, identify key trends, and address the challenges and opportunities associated with each technology. A case study approach offers a practical perspective, investigating real-world architectural projects and design studios embracing XR technologies. Through these case studies, the intricacies of XR integration are explored, the transformative effects on design experience are assessed, and exemplary practices in architectural design are showcased. Moreover, the dissertation discusses XR technologies in relation to conventional design education, thereby underscoring their potential to redefine architectural pedagogy. This research explores integrating XR technologies into architectural education to enhance students' creative capacities and redefine the design process. By incorporating XR technologies, architecture students gain the skills and knowledge necessary for sustainable development, fostering innovation, sustainability, and technological proficiency. XR technologies in education provide a quality learning experience that aligns with global sustainability goals, preparing students to contribute effectively to the achievement of Quality Education (Sustainable Development Goal 4). This research contributes to the ongoing discussion on the role of technology in shaping the future of architectural design education and practice. It sheds light on the transformative potential of XR technologies in architectural design education. Architects, educators, and students stand to gain valuable perspectives on harnessing XR technologies to enhance creativity and innovation in the architectural field.Doctoral Thesis Derin Öğrenme Tabanlı Kompozit Malzemelerin Ultrasonik Tomografi Görüntülerinden Kusurların Tespiti ve Sınıflandırılması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Gülşen, Abdulkadir; Güngör, Burcu; Kolukısa, BurakThis thesis introduces novel methodologies for enhancing defect classification and characterization in advanced composite materials by leveraging state-of-the-art machine learning (ML), deep learning (DL), and federated learning (FL) techniques within ultrasonic and acoustic emission (AE) inspection environments. First, a new ultrasonic dataset (UNDT), comprising 1,150 images from 60 distinct composite materials, is introduced. Applying transfer learning methods to both the UNDT and a publicly available dataset demonstrates the efficacy of advanced neural architectures—such as DenseNet121 and VGG19—achieving accuracy rates up to 98.8% and 98.6%, respectively. Next, the scope is extended to AE-based health monitoring by introducing an ensemble feature selection methodology to identify features strongly correlated with damage modes. By selecting amplitude and peak frequency for labeling and subsequently applying unsupervised clustering, the analysis confirms that both traditional AE features (e.g., counts and energy) and less commonly employed features (e.g., partial powers) correlate with distinct defect types. Finally, a novel FL framework is introduced to address the scarcity of publicly available, real-world ultrasonic datasets. This decentralized approach preserves data privacy while maintaining performance levels comparable to centralized methods, ensuring scalability and confidentiality in diverse data environments. Overall, these contributions significantly advance the field of NDT, offering robust defect classification and characterization. In doing so, the findings not only improve the accuracy and reliability of material integrity assessments but also lay a durable foundation for more secure, collaborative, and efficient NDT systems.