Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5799
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Master Thesis İnsan Bağırsak Mikrobiyotasından Hastalık Biyobelirteçlerinin Tespiti için Makine Öğrenmesi Temelli Sistem Geliştirilmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Koçak, Ayşegül; Güngör, Burcu; Yousef, MalikThe human gut microbiota consists of a diverse ecosystem of organisms, encompasses billions of species. Recently developed next-generation sequencing methods have enabled researchers to examine the microbiota in greater detail, leading to new insights into its functions and dysfunctions. This study aims to identify metagenomic biomarkers (Microorganism-Enzyme Pairs) for colorectal cancer (CRC). The tool that we used allows for the analysis of microorganisms and enzymes within the gut microbiota. It achieves this by initially clustering enzymes based on their correlations with species and subsequently utilizing these clustering results to evaluate the ability of groups to differentiate between patient and healthy cohorts. By integrating species and enzymes, it is possible to identify pathogen microorganisms and enzyme clusters, that have the potential to distinguish cases (individuals with CRC) from controls (healthy individuals). The identified enzyme clusters and associated species could potentially act as biomarkers for colorectal cancer (CRC), enabling early diagnosis and more effective treatment. This approach holds promise for further exploration of the gut microbiota and its importance in human health and illness. Keywords: Bioinformatics, Machine Learning, Colorectal Cancer DiagnosisMaster Thesis Meme Kanseri Histopatoloji Görüntülerinde Evrişimsel Sinir Ağları Kullanarak Tümör Tespiti(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Şahbaz, Zeki; Aksebzeci, Bekir HakanBreast cancer is one of the most common cancer types among women worldwide. Early detection significantly increases the chances of survival and effective treatment, making advancements in diagnostic methodologies crucial. This study aims to improve the detection of tumor cells in breast cancer histopathology images using deep learning and image processing techniques. Significant modifications have been made to the hyperparameters, including the tumor bounding box size, batch size, optimization algorithms, learning rate, and weight decay. These changes focus on determining the best parameters of the Faster R-CNN model. A comprehensive analysis of different parameters was conducted using the Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) dataset. The analysis identified the best settings for model performance, shows by improvements in precision, recall, and F-score. Our research contributes to the field of medical image analysis by identifying critical factors that affect the accuracy of tumor detection, contributing to the development of more accurate diagnostic tools.Master Thesis Yarı-Tek-Aşamalı Dört-Anahtarlı Alçaltıcı-Yükseltici Evirici için Pürüzsüz Mod Geçiş Tekniğinin İncelenmesi ve Geliştirilmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Keskinkılıç, Ebubekir; Keskinkılıç, Ebubekir; Tekgün, BurakIn recent decades, given the world's inevitable energy scarcity, increasing energy demand and green energy concerns, high efficiency energy conversion has become more important and attractive than ever, and researchers have directed their interest to energy-efficient converters. Inverters are a commonly utilized type of converter, which can be classified into two categories: single and two-stage inverters. Considering the inherent drawbacks of traditional inverters, a quasi-single-stage inverter (QSSI) has emerged. The QSSI uses a DC-DC converter to shape the rectified version of the desired AC waveform in the first stage and, in the second stage, it switches only once to alternate the polarity. It stands forward in terms of efficiency, control simplicity, and system stability. Among QSSI, a non-inverting buck-boost converter has drawn attention due to its capability to perform both step-up and down modes and its bidirectional power transfer feature. In the first stage of the QSS non-inverting buck-boost converter; smooth transitions between the buck and boost modes and efficient conversion cannot be achieved by the traditional two-mode control method when the output voltage level is close to the input voltage level due to various limitations, non-idealities, and disturbances. Many methods have been applied and studied in the literature to minimize or eliminate the effects of the region which is called the 'dead zone'. In this thesis study, further efficiency and THD improvement for the QSSI is targeted by employing a four-mode control method. The study incorporates a comparative study of the dead zone effects on inverter systems, which have not been previously documented in the literature. Moreover, it places a priority on optimizing efficiency and minimizing distortion in various applications—ranging from motor control and solar energy systems to grid-tied wind turbines and switched-mode power supplies—by comparing existing methods with open-loop voltage control. In conclusion, the theoretical results are verified with experimental studies.Master Thesis Anahtarlamalı Relüktans Motorlarında Tork Dalgalanmasının Azaltılması için Uyarlanabilir Çevrimiçi Tork Paylaşım Fonksiyonu Geliştirilmesi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Genç, Ufuk; Tekgün, BurakElectrical machines play a crucial role in modern society by transforming electrical energy into mechanical energy and vice versa. These machines include various types of motors and generators, which are used in a wide range of applications such as electric vehicles, industrial automation, and renewable energy systems. One of the popular electrical machines is the switched reluctance machine (SRM), which is known for its high reliability and efficiency. The key advantages of the SRM include its simple structure, robustness, and low cost. The SRM does not require a permanent magnet or an excitation winding, making it an attractive option for high-volume, low-cost applications. Despite its advantages, the SRM also has some disadvantages that need to be considered. One of the main drawbacks of the SRM is being susceptible to torque ripple, which can result in vibration and noise. In order to overcome these disadvantages, advanced control methods have been developed for the SRM. One such control method is the torque sharing function, which distributes the load among the phases of the motor. This results in improved torque characteristics and reduced torque ripple. However, this control method also has some disadvantages, such as increased complexity and the need for more advanced sensors and controllers. Additionally, the torque sharing function may result in reduced efficiency, especially at high speeds. The purpose of this thesis study is to improve the torque ripple performance of SRM for a wide speed range through the proposed control approach. In conclusion, minimizing the torque ripple is a critical aspect of the operation of SRMs, and a range of control strategies and techniques can be used to achieve this goal. By reducing the torque ripple, SRMs can deliver improved efficiency, performance, and reliability, making them even more attractive for a wide range of applications.Master Thesis Bilgisayar Algoritmalarının GPU ile Hızlandırılması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yalçın, Salih; Alkan, Gülay YalçınTravelling Salesman Problem (TSP) is one of the significant problems in computer science which tries to find the shortest path for a salesman who needs to visit a set of cities and it involves in many computing problems such as networks, genome analysis, logistic etc. Using parallel executing paradigms, especially GPUs, is appealing in order to reduce the problem-solving time of TSP. One of the main issues in GPUs is to have limited GPU memory which would not be enough for the entire data. Therefore, transferring data from host device would reduce the performance in execution time. In this study, we present a methodology for compressing data to represent cities in the TSP so that we include more cities in GPU memory. We implement our methodology in Iterated Local Search (ILS) algorithm with 2-opt and show that our implementation presents 29% performance improvement compared to the state-of-the-art GPU implementation.Master Thesis Erken Orman Yangını Tespiti için Otonom Heterojen Çoklu Robot Sistemi Tasarımı(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Serin, Ömer Faruk; Güler, SametThe usage of autonomous multi-robot systems for human life-endangering applications is emerging. Early wildfire detection and firefighting are two example applications. In this study, a heterogenous multi-robot system is proposed for both fire detection and response. The system employs an unmanned aerial vehicle for beyond-visual line-of-sight observations and an unmanned ground robot for fire extinguisher carrying. The proposed method uses ultrawideband (UWB) communication and ranging modules for the relative localization of robots during their movements. A specially trained YOLOv7 object detection model is used for robustly detecting forest fires and smoke while a modified version of the Vector Field Histogram Plus (VFH+) algorithm on the ground robot is used for obstacle avoidance while navigating. The structural design of the system requires no odometry or mapping of the environment hence improving the applicability of the system while decreasing system complexity. Additionally, the proposed UWB localization system is shown to be robust in long-lasting operations unlike many odometry-based approaches which accumulate errors with time. Moreover, localization of the UAV is realized with only three independent UWB-based range measurements and the altitude information of the UAV. The system is tested both in a realistic simulation environment and in real experimental setups with multiple runs. Results showed that the proposed system is improvable for better detection and practical to implement even in a dense forest environment without the need for GPS sensors, odometer data, or magnetometer.Master Thesis Grafik Teorisi Tabanlı Trafik Işığı Yöntemi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Thahir, Adam Rizvi; Güngör, Vehbi Çağrı; Coşkun, MustafaTraffic congestion and delays caused in traffic light intersections can adversely affect countries in terms of money, time, and air pollution. With the advancement of computational power as well as artificial intelligent algorithms, researchers seek novel and optimized solutions to the traffic congestion problem. Most modern traffic light systems use manually designed traffic phase plans at intersections, and although this has proven to be relatively sufficient for today's traffic management systems, implementing a smarter traffic phase selection system is deemed to be more effective. Traditional approaches rely heavily on traffic history (static information), whereas Reinforcement Learning (RL) algorithms, which offer an 'adoptable'/dynamic traffic management system, are gaining increased research interest. Despite the usefulness of these RL based deep learning techniques, they inherently suffer from training time to apply them in real-world traffic management systems. This study aims to alleviate the training time problem of deep learning-based techniques, The research brings forth a novel graph-based approach that is able to use known occupancies of roads to predict which other roads in a given network would become congested in the future. Based on the predictions obtained, we are able to dynamically set traffic light times in all intersections within a connected network, starting from roads with known occupancies, and moving along connected roads that are anticipated to be congested. Predications are done using edge-based semi-supervised graph algorithms. Conducted simulations show that our approach can yield comparable average wait time to that of deep-learning based approach in minutes, compared to the much longer training time required by the deep-learning models. Keywords: Deep Learning, Reinforcement Learning, Traffic Flow, CongestionMaster Thesis Esnek Kağıt Tabanlı Kapasitif Sensör Kullanarak Solunum İzleme(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Solak, İrfan; İçöz, Kutay; Hah, DooyoungRespiration is an action known to be essential and crucial for life. Unfortunately, in some cases such as illnesses and accidents various respiratory problems can be experienced. It might be difficult to maintain normal respiration for the people who have respiratory diseases. It is known that respiration monitoring of people who have respiratory problems, albeit for different reasons, is important in terms of their treatment and maintaining their life quality. Current respiration monitoring systems are expensive and bulky. Many of these systems are only available at hospitals or in laboratories. Low-cost, easy to use and portable respiratory monitoring devices are needed. Having these motivations, we aimed to monitor respiration by designing and producing a paper-based sensor that is easy to manufacture, low-cost, and highly responsive. The sensor, which is the subject of this thesis project, has potential to be used for different purposes such as measuring the humidity in the environment. In this project, we focused on designing a system for people who have respiratory problems by providing respiration monitoring data. In addition, according to the data obtained, we are able to analyze the health status of the users. Therefore, this sensor can be used both for the detection of respiration diseases and monitor the status of the patients. In this way, respiration related unhealthy situation can be detected and treated immediately.Master Thesis Elektrik Dağıtım Şirketleri Perspektifinden Blockchain Temelli Enerji Uygulamaları(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Yağmur, Ahmet; Tonyalı, SametThis thesis discusses blockchain-based energy applications from the distribution system operator (DSO) perspective. Blockchain has a potential impact on emerging actors, such as electric vehicles (EVs), charging facility units (CFUs), Distributed Energy Resources (DERs) and microgrids of the electricity grid. Although, blockchain offers magnificent, decentralized solutions, owing to the reality of the existing grid structure, the central management of DSOs still plays a significant, non-negligible role. Numerous studies of proposed blockchain-based EV systems have investigated the energy costs of EVs, fast and efficient charging, privacy and security, peer-to-peer energy trading, sharing economy, selection of appropriate location for CFUs, and scheduling. Additionally, blockchain in DERs, microgrids and energy market investigated in literature. However, cooperation with DSO organizations has not been adequately addressed. Blockchain-based solutions mainly suggest an entirely distributed and decentralized approach for energy trading. However, converting the entire power system infrastructure is considerably expensive. Building a thoroughly decentralized electricity network is nearly impossible in a short time, particularly at the national grid level. In this regard, the applicability of the solutions is as significant as their appropriateness, especially from the DSO perspective, and must be examined closely. The blockchain applicability of the essential DSO services such as SCADA and AMI are analyzed in this study. Time series analysis applied to forecast future peak load of the grid in a pilot region. Reducing the peak load by using BC based demand side management mechanism scenario evaluated and total saving of grid investment is analyzed. We searched and analyzed DSO-based requirements for potential blockchain applications in the energy sector.Master Thesis Mesafe ve Görüntü Kullanan Dronlar ile Koordine Hedef Teşhisi ve Takibi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Alabay, Hüsnü Halid; Güler, SametRobot autonomy refers to the ability to carry out objectives by perceiving the environment and deciding on the actions required without human interruption. Although autonomous aerial robots offer big advantages in our daily life, online localization and control remain the biggest challenge lying ahead of aerial robot implementations. For single robot applications, GPS, and motion capture (mocap) systems can be utilized for outdoor and indoor applications, respectively. However, when it comes to multi-robot systems, the relative localization problem needs to be solved beyond the single robot localization problem. Furthermore, GPS signals are not available everywhere, and mocap systems limit the application space of multi-robot systems. Motivated by the industrial application scenarios, we address the relative localization and docking problem in multi-drone systems where drones do not utilize any external infrastructure for localization. We consider a two-drone system that aims at docking a target object which consists of an ultrawideband (UWB) distance sensor. The drones are equipped with UWB sensors and cameras and try to localize the target object and dock around it in a pre-defined configuration in the absence of GPS and magnetometer sensors and external infrastructures. We design an extended Kalman filter based on the dynamic model of the drone-target configuration that fuses the distance and vision sensor outputs. Particularly, we use the YOLO algorithm for the bearing detection between the drones and the target. Next, we devise and implement a switching-based distributed formation control algorithm and integrate it into the estimation algorithm. We demonstrate the performance of our algorithm in several simulation studies in a realistic Gazebo environment. Finally, we provide primary experimental results and a roadmap to the full implementation of the system.