Doktora Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5800

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Ağaç Yapılı ve Ayrıt Kapasiteli Hub Yerleşim Problemleri için Model ve Çözüm Metodolojilerinin Geliştirilmesi
(Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü, 2022) Kayışoğlu, Betül; Kayışoğlu, Betül; Akgün, İbrahim
In this dissertation, we study two different extensions to hub location problems, namely, Multiple Allocation Tree of Hubs Location Problem (MATHLP) that result from incorporating a tree topology requirement for the hub network and Multiple Allocation Arc Capacitated Hub Location Problem (MACHLP) that result from imposing capacities on the arcs. We consider both problems in a multiple allocation framework and try to minimize total flow cost by locating p hubs. Unlike most studies in the literature that use complete networks with costs satisfying the triangle inequality to formulate the problems, we define the problems on non-complete networks and develop a modeling approach that does not require any specific cost and network structure. Our proposed approach provides more flexibility in modeling several characteristics of real-life hub networks. We solve the proposed models using CPLEX-based algorithm and Gurobi-based algorithm with NoRel heuristic. For MATHLP, we develop Benders decomposition-based heuristic algorithms and for MACHLP, we develop a heuristic algorithm based on simulated annealing. We conduct computational experiments using problem instances defined on non-complete networks with up to 500 and 400 nodes for MATHLP and MACHLP respectively. The results indicate that the proposed solution methodologies are especially effective in finding good feasible solutions for large instances. Keywords: hub location problem, tree of hubs location problem, arc capacitated hub location problem, benders-type heuristics, simulated annealing
Akıllı Mikro-Şebekelerde Kontrol Stratejilerinin Geliştirilmesi
(Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü, 2021) Yoldaş, Yeliz; Yoldaş, Yeliz; Önen, Ahmet
This thesis concerns the transformation of aged power systems to modern power systems that include microgrids with renewable energy sources and energy storage systems. The integration of renewable energy sources brings excellent opportunities to provide better reliability and efficiency. However, the uncertainty and intermittent nature of renewable energy sources may potentially degrade the stability and quality of the electrical grid. Therefore, the aim of this dissertation is to maintain the supply-demand balance in microgrids while minimizing the cost in real time operation. A microgrid energy management system that can optimize the dispatch of the controllable distributed energy resources in grid-connected mode of a pilot microgrid on a university campus in Malta was developed to achieve this goal. Three different methods were used in this study: mixed integer linear programming (MILP), MILP based rolling horizon control and Q-learning, Designing intelligent method for the real-time energy management of the stochastic and dynamic microgrid is the primary goal of this research. Moreover, the detailed mathematical models of the network model and of the technical model are considered for the economic and environmental operation of the microgrid system to solve the optimization problem under more real-world conditions. The objective is to minimize the total daily operation costs, which include the degradation cost of batteries, the cost of energy bought from the main grid, the fuel cost of the diesel generator, and the emission cost. The optimization problem is modeled as a finite Markov decision process (MDP) by combining network and technical constraints, and a Q-learning algorithm is adopted to solve the sequential decision subproblems. The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming (MINLP) problem into a series of single-stage problems so that each subproblem can be solved using Bellman's equation. To prove the effectiveness of the proposed algorithm, three different case studies are taken into consideration. A predictive control framework is also proposed to provide optimal operation with minimum cost. This method allows the consideration of operational cost values, demand with uncertainty, generation units' profiles with uncertainty, and constraints related to the network model and technical model. The stochastic and deterministic cases are conducted to validate the efficiency of the approach.
Amorf Bor Malzemelerin Simülasyonu
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yıldız, Tevhide Ayça; Durandurdu, Murat
Boron-based materials and their technological applications have great interests in many scientific and technological areas from materials science to medicine. This doctorate thesis was prepared for the purpose of investigating the atomic structure, electrical and mechanical properties of different boron based amorphous materials by using an ab-initio molecular dynamics technique. The results obtained via a computational method were presented in three main chapters. In the Chapter 3, the influence of hydrogenation on the atomic structure and the electronic properties of amorphous boron nitride (ɑ-BN) was examined. The structural evaluation of ɑ-BN and the hydrogenated (ɑ-BN:H) models revealed that their short-range order was mainly similar to each other. Hydrogenation suppressed the formation of twofold coordinated chain-like structures and tetragonal-like rings and leaded to more sp2 and even sp3 bonding. Furthermore, hydrogenation was found to have an insignificant impact on the electronic structure of ɑ-BN. Secondly, in the Chapter 4, an amorphous boron carbide (a-B4C) model was generated. The pentagonal pyramid-like motifs were found to be the main building units of B atoms in a-B4C and some of which yielded the development of B12 icosahedra. On the other hand, the fourfold-coordinated units were the leading configurations for C atoms. a-B4C was a semiconducting material and categorized as a hard material. In the Chapter 5, amorphous boron carbides (BxC1-x, 0.50x0.95) were systematically created. With increasing B/C ratio, more closed packed materials having pentagonal pyramid motifs form. All models were semiconducting materials. Some amorphous compositions were proposed to be hard materials. Keywords: Amorphous, Hydrogenation, Boron Nitride, Boron Carbide, Ab-initio molecular dynamics technique
Amorf Malzemelerin Modellenmesi ve İncelenmesi
(Abdullah Gül Üniversitesi, 2019) ERKARTAL, MUSTAFA; Erkartal, Mustafa; Durandurdu, Murat
Bu doktora tezinin amacı ab-initio moleküler dinamiği simülasyonları (AIMD) yoluyla, metal-organik çerçeve yapılardaki (MOF) basınca bağlı amorfizasyonu (PIA) ve ayrıca diğer faz geçişlerini araştırmaktır. Hesaplardan elde edilen sonuçlar üç ana bölümde rapor edilmiştir. Birinci bölümde, MOF-5'in yüksek basınç davranışını araştırmak için ab initio simülasyonları yapıldı. Önceki deneysel bulgulara benzer şekilde, simülasyonlar sırasında 2 GPa'da bir PIA gözlendi. Bu faz geçişi, tersinir olmayan bir birinci dereceden bir dönüşüm olup, geçişe yaklaşık% 68'lik bir hacim çöküşü gözlenmektedir. Dikkat çekici bir şekilde, geçiş yerel bozulmalardan kaynaklanmaktadır ve önceki önerilerin aksine, bu faz geçişi boyunca herhangi bir bağ kırınımı ve oluşumu gözlenmemektedir. Ayrıca, amorf durum çerçeve yapının elektronik bant aralığı kayda değer bir ölçüde daralmaktadır. Bu projenin ikinci kısmı için, ZIF-8'in geniş bir basınç aralığında yüksek basınç davranışını araştırmak için AIMD simülasyonları yapıldı. Sıkıştırma altında, ZnN4 tetrahedral ünitelerindeki büyük deformasyonlar, 3GPa civarında kristal-amorf bir faz geçişine yol açar. Amorflaşma süreci boyunca, Zn-N koordinasyonu korunur. Çalışılan basınç aralığında başka bir faz değişikliği bulunmadı, ancak sistemin olası tahrip oluşu 10GPa'nın üzerinde bulundu. Uygulanan basınç, amorfizasyondan hemen önce kaldırıldığında, imidazolat ligandlarının dönüşleri (salınım hareketi), bir kristal-kristal faz geçişine neden olmaktadır. Gerilme rejiminde ise -2.75GPa'a kadar herhangi bir faz geçişi tespit edilmezken, bu basınç üzerinde yapı tahrip olmaktadır. Bu araştırma projesinin son bölümünde, ZIF polimorflarının (ZIF-1, ZIF-2 ve ZIF-3) basınç altında geçişleri kapsamlı bir şekilde simüle edildi. ZIF-1, -2 GPa (gerilme bölgesi) ve 10 GPa (sıkıştırma bölgesi) arasında ardışık bazı kristal-kristal ve kristal-amorf faz geçişleri gösterir. Öte yandan, ZIF-2 ve ZIF-3, nispeten düşük sıkıştırma rejiminde hızlı kristal- amorf ve büyük olasılıkla amorf-amorf geçişler gösterirken, bütün ZIF'ler gerilme bölgesinde -3 GPa civarında tahrip olmaktadır.
Anormallik Tespiti için Veri Madenciliği
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2020) Kaçmaz, Rukiye Nur; Yılmaz, Bülent
Gastroentereloji uzmanları için kolon anormalliklerinin tespit edilmesi en zor görevlerden birisidir. Kolonoskopi herhangi bir anormalliği izlemek için kolondan video veya görüntüler kaydetmenin en yaygın yöntemidir. Bununla birlikte işlem sırasında elde edilen görüntü veya videolar, kolonoskopi probunun ya da kapsülün hızlı hareketinden kaynaklanan hareket gürültüsü, kapsülde ve probda ışık kaynağından kaynaklanan yansıma gürültüsü (YG), yetersiz veya aşırı aydınlatmadan kaynaklanan uygun olmayan kontrast gürültüsü, mide öz suyu, baloncuklar veya kalıntılar içermektedir. Bu tarz gürültüler içeren görüntülere bilgi taşımayan çerçeveler adı verilmektedir. Hastalık tespiti işlemi ise bilgi içeren olarak adlandırılan temiz görüntüler ile yürütülmektedir. İlk çalışmada tekstür tabanlı otomatik polip tespitinde YG'nin etkisini ve YG'yi ortadan kaldırmak için kullanılan görüntü enterpolasyonunun kullanımı araştırıldı. Bu amaçla, çeşitli boyutlarda sonradan YG eklenen ve interpolasyon uygulanan görüntülerden ve YG içermeyen görüntülerden çeşitli tekstür özellikleri elde edildi. Polipleri kolon arka planından ayırt etmek için, uygulanan en yakın komşular, bilineer ve bikübik interpolasyon yöntemlerinin, tekstür özellikleri ve sınıflandırma performansı açısından herhangi bir farklılığa neden olup olmadığı test edildi. İkinci çalışmada temel amaç, bilgi taşımayan çerçeveleri tespit etmede geleneksel makine öğrenmesi ve transfer öğrenme yaklaşımlarının performanslarının karşılaştırılmasıydı. Makine öğrenmesi bölümünde, gri seviye eş oluşum matrisi, gri seviye koşu uzunluğu matrisi, komşuluk gri ton farkı matrisi, odak ölçüm operatörleri ve basıklık, standart sapma ve çarpıklık olarak üç adet birinci derece istatistik kullanıldı. Sınıflandırma aşamasında rastgele orman, destek vektör makineleri ve karar ağacı yaklaşımları kullanılmıştır. Transfer öğrenme bölümünde derin sinir ağları olarak AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet-18, ResNet-50, NasNetMobile ve MobileNet tercih edildi. Son çalışma, bilgi taşıyan çerçevelerde Crohn's, ülseratif kolit, kanser ve polip gibi kolon anormalliklerinin saptanmasını içermiştir. Bu çalışmanın amacı, öncelikle sağlıklı çerçeveleri hastalıklılardan ayırmak ve hem geleneksel makine öğrenmesi hem de transfer öğrenme yaklaşımlarını kullanarak hastalık türlerini belirlemekti. İkinci çalışmada kullanılanlarla aynı tekstür özellikleri, sınıflandırma yaklaşımları ve transfer öğrenme yöntemleri kullanılmıştır.
Biyoçipler için Mikro Biyomalzemelerin ve Hücrelerin Görüntü İşleme Yöntemleri ile Otomatik Olarak Sayılması ve Analizi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Çelebi, Fatma; İçöz, Kutay
Quantification of tumor cells is essential for early cancer detection and progression tracking. Multiple techniques have been devised to detect tumor cells. In addition to conventional laboratory instruments, several biochip-based techniques have been devised for this purpose. Our biochip design incorporates micron-sized immunomagnetic beads and micropad arrays, necessitating automated detection and quantification not only of cells but also of the micropads and immunomagnetic beads. The primary function of the biochip is to simultaneously acquire target cells with distinct antigens. As a readout technique for the biochip, this study devised a digital image processing-based method for quantifying leukemia cells, immunomagnetic beads, and micropads. Images were acquired on the chip using bright-field microscopy with image objectives of 20X and 40X. Conventional image processing methods, machine learning methods, and deep learning methods were used to analyze the images. To quantify targets in the images captured by a bright-field microscope, color- and size-based object recognition and machine learning-based methods were first implemented. Secondly, color- and size-based object detection and object segmentation methods were implemented to detect structures in bright-field optical microscope images acquired from the biochip. Third, segmentation of the minimal residual disease (MRD) using deep learning. Implemented biochip images comprised of leukemic cells, immunomagnetic beads, and micropads. Moreover, mesenchymal stem cells (MSCs) are stem cells with the capacity for multilineage differentiation and self-renewal. Estimating the proportion of senescent cells is therefore essential for clinical applications of MSCs. In this study, a self-supervised learning (SSL)-based method for segmenting and quantifying the density of cellular senescence was implemented, which can perform well despite the small size of the labeled dataset.
Blokzincir Tabanlı Eşten-Eşe Enerji Ticareti Uygulamaları
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Seven, Serkan; Alkan, Gülay Yalçın
This thesis explores the potential of innovative peer-to-peer (P2P) energy trading schemes for virtual power plants (VPPs) using blockchain technologies, smart contracts, and decentralized finance (DeFi) instruments. Traditional centralized approaches have limitations in terms of transparency and security, which can hinder the successful implementation and operation of VPPs and P2P energy trading systems. The dissertation begins by reviewing the current state of energy sources within the global energy landscape. Understanding the existing landscape provides valuable insights into the potential benefits and challenges of implementing P2P energy trading within VPPs. The focus of the dissertation is to develop and analyze innovative P2P energy trading schemes for VPPs that integrate blockchain technologies and facilities to enhance transparency, security, and automation of energy transactions. Furthermore, DeFi instruments, specifically decentralized exchange (DEX), are used as a novel approach instead of auction methods to determine P2P energy buying and selling prices. Along with blockchain technologies, optimization is used to maximize the economic benefits of peers. The sequential decision problem of the trading schemes is solved with mixed integer linear programming (MILP). In addition, machine/deep learning models are utilized to overcome the drawbacks of conventional mathematical programming like MILP. These models can accelerate the decision-making processes by learning from the optimization results obtained. Overall, frameworks for the successful integration of P2P energy trading within and among VPPs are developed to validate the effectiveness and feasibility of the proposed P2P energy trading schemes through case studies and simulations using realistic data sets and blockchain platforms.
Bor Esaslı Nano Yapıların Modellenmesi ve İncelenmesi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Tahaoğlu, Duygu; Durandurdu, Murat; Alkan, Fahri
Polyhedral boron clusters and their applications have been subject to research in many fields such as medicine, materials science, catalytic applications, energy studies, etc. These molecules owe their popularity to their exceptional 3D stable structures, as well as their various sought-after properties in many applications. This doctoral thesis was prepared within the focus of a computational investigation of different polyhedral borane and carborane clusters by using DFT methods. The results of our studies were reported in two main chapters (Chapters 3 and 4). In the first part (Chapter 3), theoretical evaluation of relative stabilities and electronic structure for [BnXn]2− clusters were provided. The structural and electronic characteristics of [BnXn]2− clusters were examined by comparison with the [B12X12]2− counterparts with a focus on the substituent effects (X = H, F, Cl, Br, CN, BO, OH, NH2). The effects of the substituents were discussed in relation to their mesomeric (±M) and inductive (±I) effects. The results showed that the icosahedral barrier can be reduced through substitution by destabilizing the [B12X12]2−cluster with symmetry-reducing ligands or ligands with +M effects rather than stabilizing the larger clusters. In the second part (Chapter 4), the investigation of the photophysical properties of carborane-containing luminescent systems was presented. The o-CB-Anth system is known to exhibit a dual-emission property by radiating in the visible region from two low energy conformations with local excited (LE) and hybridized local and charge transfer (HLCT) characters, however, it shows a very low emission quantum yield in solution state similar to many other CB-luminescent systems. In this section, the excited-state potential energy surface (PES) of o-CB-Anth and o-CB-Pent were investigated in detail and the effect of a low-lying CT on the low quantum yield was discussed.
Bor Zengini Amorf Malzemeler
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) KARACAOĞLAN, Ayşegül Özlem; Karacaoğlan, Ayşegül Özlem; Durandurdu, Murat
In the scope of this thesis, boron-rich amorphous materials having different boron concentrations (B1-xNx, B1-xOx and B1-xSix) were created as a result of rapid cooling of their liquid state with the help of an ab initio molecular dynamics technique. Their structural, electrical and mechanical properties were exposed in detail. In all boron rich materials, the coordination number of B was found to increase steadily with increasing B content. Similarly N and Si atoms also attained high coordinated motifs with increasing B content. However, the coordination number of O atoms remained null for all compositions. Chemical segregations and hence phase separations were witnessed in most amorphous configurations. The materials with high boron ratios, as expected, consisted of B12 icosahedrons. In addition, the formation of nano-sized B7, B10, B14 and B16 clusters was observed in some boron-rich compounds. Each computer-generated material exhibited a semiconducting character. The mechanical properties (Bulk, Young and Shear moduli) were perceived to increase with increasing B content. Some amorphous compositions were proposed to be hard materials on the basis of their Vickers hardness estimation.
Derin Öğrenme Tabanlı Kompozit Malzemelerin Ultrasonik Tomografi Görüntülerinden Kusurların Tespiti ve Sınıflandırılması
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Gülşen, Abdulkadir; Güngör, Burcu; Kolukısa, Burak
This 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.
Derin Öğrenme Yaklaşımlarıyla Küçük Hücreli Dışı Akciğer Kanserinde Tümör Karakterizasyonu
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2021) Bıçakcı, Mustafa; Yılmaz, Bülent
Küçük Hücreli Dışı Akciğer Kanseri (KHDAK) akciğer kanserlerinin büyük çoğunluğunu oluşturur ve adenokarsinom (ADC) ve skuamöz hücreli karsinom (SqCC) olmak üzere iki önemli alt tipi vardır. Genel olarak, bu iki alt tip mikroskobik olarak belirlenen morfolojik kriterler dikkate alınarak birbirinden ayrılır. Ancak, kötü morfoloji bunu oldukça zorlaştırır. Alt tipe özel tedavi yöntemleri için bu tür çalışmalar önemlidir. Bu tezde, pozitron emisyon tomografi (PET) görüntüleri kullanılarak KHDAK'nin alt tiplerinin sınıflandırılması üzerinde derin öğrenme (DÖ) yöntemleri incelenmiştir. İlk çalışmada, DÖ yöntemlerinin temelini oluşturan yapay sinir ağları (YSA) kullanılarak %73 doğru sınıflandırma başarısı elde edilmiştir. İkinci çalışmada, PET görüntülerinden alınan bölütlenmiş tümör kesitleri kullanılarak birkaç DÖ modeli incelenmiştir. Sonuçta, %95 F skoru ile VGG16 ve VGG19 en başarılı modeller olmuştur. Bu çalışmanın sonunda kesit bazlı çalışmalar bırakılarak hasta bazlı çalışmalara geçilmiştir. Üçüncü çalışmada, hasta bazlı dilimlerin birleştirilmesiyle oluşturulan üç boyutlu (3B) verilerin kullanımı yeterli başarıyı sağlamamıştır. Dördüncü çalışmada, PET görüntülerinin doğrudan kullanıldığı, tümör kısımlarının kırpılarak kullanıldığı ve bölütlenmiş tümör parçalarının kullanıldığı üç farklı deney yapılmıştır. Bu çalışma, peritümoral alanların sınıflandırmada olumlu etkisini ortaya koymuş ve VGG19 %74 F skoru değerine ulaşmıştır. Beşinci çalışmada, transfer öğrenme ve hassas ayar çalışmaları başarısızdı. CNN ve ResNet tabanlı sığ ağları içeren son çalışma %71 F skoru ile umut verici olmuştur.
Dönüşüm İçindeki Kırsal Miras Alanları Üzerine Bir Sürdürülebilirlik Modeli; Kayseri Bağpınar Örneği
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Timur, Bahar Elagöz; Asıliskender, Burak
This thesis aims to develop a model for the resilience and sustainability of rural heritages against transition risks and contribute to rural heritage conservation by creating living heritage sites. Additionally, the thesis explains how habitus and rural heritage are dynamically interconnected by emphasizing their organic relationship. Rural heritages, primarily constructed with traditional building techniques, architectural habits, and local materials, inherently reflect the everyday life practices shaped by their users' habitus. These unique lifestyles provide insights into the cultures of communities, aiding in the understanding of larger societies. Therefore, any demand for changes in habitus and everyday life practices directly threatens rural heritage areas. The study highlights the threats posed by the transition of habitus on rural heritage due to emerging demands for change and discusses the risks it poses to rural heritage sites. Sometimes, as rural-to-urban migration increases, and at other times, changes in rural habitus and everyday life practices due to technology and modern life emerge. Understanding this balance of relationships and developing sustainable conservation approaches by calculating the risks through vulnerability is the main objective of this thesis. In this context, a sustainability model (RUHET) has been developed for rural heritage in transition, and conservation strategies through vulnerability assessments have been explained. The model was applied to the rural heritage area of Bağpınar in the Melikgazi district of Kayseri province, and the results were presented.
El Protezleri için EEG ve EMG Sinyalleriyle Algı Kestirimi ve Tork Kontrolü
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Karakullukcu, Nedime; Yılmaz, Bülent
Upper extremity prostheses vary based on patients' articulation levels and movement methods. They can be cosmetic, operate mechanically with shoulder movement, or be controlled by myoelectronic and electroencephalography (EEG) signals. However, unnatural prosthesis control burdens users mentally. This thesis seeks to enhance bionic hand prosthesis control using EEG and electromyography (EMG) signals, coupled with users' visual weight perception, aiming to reduce physical and mental discomfort associated with mechanical prostheses. The prototype hand's preconditioning evaluates objects' weight visually, aiming to reduce shoulder force and mental load while holding an object. EEG and EMG signals from subjects were processed for real-time implementation. In the first stage, a study focused on operating the prosthesis using the motor intention waves of prosthesis users, and the machine learning approaches' classification success (detection of the intention to activate the prosthesis) was examined using EEG data from 30 healthy participants. The second stage recorded EEG and EMG signals from 31 participants during reaching, lifting, and placing an object, employing various classifications for object weight. In the real-time classification of multi-channel EEG signals from 20 healthy individuals using Fourier-based synchrosequeezing transform (FSST) and singular value decomposition (SVD) approaches, the system aimed to control the stiffness of the wrist part of the prosthesis. Consequently, the system could detect the weight of the object perceived by the user while using the prosthesis, allowing for the preconditioning of the prosthesis based on this weight when the user wants to hold and move the object.
Endüstriyel Ortamlarda Enerji Hasatlayan Çoğul Ortam Kablosuz Algılayıcı Ağları
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2020) Tekin, Nazlı; Güngör, Vehbi Çağrı
Sert kanal koşullarına sahip olan Endüstriyel Kablosuz Algılayıcı Ağ'larda (EKAA), enerji verimli ve güvenilir kablosuz iletişim sağlamak büyük önem taşımaktadır. Ağ güvenirliğini sağlarken aynı zamanda ağın ömrünü uzatmak da zor bir problemdir. Bu çalışmanın amacı, EKAA'ların ömrünün eniyilenmesidir. Bunu yaparken, endüstriyel ortamlar için uygun olan iç mekan güneş, termal ve titreşime dayalı Enerji Hasatlama (EH) yöntemleri tanımlanmış ve bunların ağ ömrüne katkıları araştırılmıştır. Uygulama güvenilirliğini ve EH yöntemlerini birlikte değerlendirerek, ağ ömrünü eniyilemek için yeni bir Karma Tamsayılı Programlama (KTP) modeli formüle edilmiştir. Ayrıca, Kablosuz Çoğul Ortam Algılayıcı Ağ'larında (KÇOAA) iletişim, büyük veri boyutu nedeniyle fazladan enerji tüketimine sebep olur. Bu nedenle, büyük veri boyutunu iletimden önce azaltmak önemli hale gelir.Bu amaçla, iletişim ve enerji dağıtım hesaplamalarını dikkate alırken, sıkıştırıcı algılama ve görüntü sıkıştırma gibi veri boyutu küçültme yöntemlerinin endüstriyel ağ ömrü üzerindeki etkisi değerlendirilir. Öte yandan, özellikle çok sayıda algılayıcılar bulunduran ağlar için KTP modelini uygun bir zamanda çözmek bir hayli zordur. KTP'nin zaman karmaşıklığı sorununun üstesinden gelmek için sezgisel tabanlı yöntemler geliştirilmiştir.
Evsel Atık Sulardan Enerji ve Su Geri Kazanımında Hibrit Membran Proseslerin Geliştirilmesi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Özcan, Özlem; Uzal, Niğmet; Şahinkaya, Erkan
This thesis study aims to develop a hybrid innovative membrane-based process that maximizes circular benefit with the recovery of energy, nutrients, and water from municipal wastewater (MWW). This process was designed to be a sustainable alternative to the widely used advanced biological wastewater treatment plants (WWTP). For this purpose, the wastewater samples from the pre-sedimentation tank effluent of the Kayseri WWTP were used in laboratory-scale membrane-based process applications. In the first stage of the study, pre-concentration studies were performed to concentrate the organic matter and nutrients in the wastewater using the chemically enhanced primary sedimentation+direct ceramic microfiltration (CEPS+DCMF) process. Wastewater concentrated up to 8 times in the CEPS+DCMF process was fed to the anaerobic fluidized bed ceramic membrane bioreactor (AnFCMBR), which is the second stage of the study. The performance of the reverse osmosis (RO) process was evaluated for nutrient recovery performance in permeates of AnFCMBR and CEPS+DCMF processes. Chemical precipitation was performed on RO concentrate samples to recover struvite. With the innovative membrane-based hybrid wastewater treatment process, a net energy recovery potential of 0.126 kWh/m3 was attained by operating the AnFCMBR process at 6 hours hydraulic retention time, while an energy requirement of 0.08 kWh/m3 was attained and thus, an energy-positive process for treating MWW has been developed.
FDG-PET Görüntülerindeki Tümörlerin Makine ve Derin Öğrenme Tabanlı Analizi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Ayyıldız, Oğuzhan; Yılmaz, Bülent
Analysis of a tumor is essential in treatment planning and evaluation of treatment response. Positron Emission Tomography (PET) is a vital imaging device for clinical oncology in understanding the metabolic structure of the tumor. In this thesis, three separate studies investigating the application of machine, deep learning and statistical approaches on FDG-PET images from patients with non-small cell lung cancer (NSCLC) and pancreatic cancer. The first study aimed at performing a survey on subtype classification of NSCLC by using different texture features, feature selection methods and classifiers. Images from 92 patients and several clinical and metabolic features for each case were used in this study along with histopathological validation for the tumor subtype labeling. Stacking classifier resulted in 76% accuracy. The aim of our second study was to adapt an atrous (dilated) convolution-based tumor segmentation approach (DeepLabV3) on FDG-PET slices with maximum standard uptake value (SUVmax). MobileNet-v2 pretrained on ImageNet served as the backbone to DeepLabV3. The classification layer was interchanged with the Tversky loss layer which helped improve model's performance while the dataset was imbalanced. Images from 141 patients were employed and augmentation was performed in each training phase. Dice similarity index was obtained as 0.76 without preprocessing and 0.85 with preprocessing. The last study focused on determining the features to be used in the prognosis of pancreatic adenocarcinoma on FDG-PET images from 72 patients. Well-known texture, metabolic and physical features were extracted from tumor region that was determined with the help of random walk segmentation algorithm. On these features time-dependent ROC curve analysis was performed for 2-year overall survival (OS) prediction, and, in the univariable analyses, tumor size, energy, entropy, and strength were found to be significant predictors of OS. Keywords: PET/CT, NSCLC, Machine learning, Deep learning, Radiomics, Semantic segmentation
Fiyat Koruması, Dönem Ortası ve Dönem Sonu Geri Ödemesi Altında Ücretlendirme Stratejileri
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Yıldız, Barış; Sütçü, Muhammed
In this thesis, we examine a selling environment where a manufacturer-controlled retailer and an independent retailer sell a slow-moving A item. The manufacturer offers the independent retailer price protection against reductions in the wholesale price. The price set by the independent retailer is assumed to be determined by Retail Fixed Markdown (RFM) policy. The manufacturer adopts a periodic-review pricing strategy and each retailer observes price-dependent stochastic demand. We employ Multinomial Logit (MNL) models to forecast customers' preferences based on retail prices. We construct stochastic programming models to determine the manufacturer's pricing strategy in the presence of four distinct price commitment contracts which differ in the supplementary privileges combined with price protection. We also propose a variant Stochastic Dual Dynamic Programming (SDDP) algorithm to determine the manufacturer's approximately optimal pricing strategy by getting around three curses of dimensionality. We observe the impact of critically important contractual parameters on the price, the market shares and the expected true profits. We also evaluate the performance of the proposed algorithm and compare the price commitment contracts in terms of the contractual parameters for which it is crucial to choose a compromise value to ensure high enough profitability for both retailers.
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
DAcTTs 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.
Genetik ve Enfeksiyon Hastalıklarının Tespiti için Makine Öğrenmesi Yöntemleri
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Işık, Yunus Emre; Aydın, Zafer
Completion of the whole human genome in the 2003 has led to various advances in many fields, particularly in biology, genetics, health sciences, treatment, and pharmacology. In the following years, spread of faster and cheaper sequencing technologies has enabled us to extract and analyze genetic profiles of individuals digitally. Consequently, individual-specific forecasting and personalized treatment and precision medicine-, what once seemed like science fiction, have become more and more real. In both approaches, one of the crucial steps is identifying the presence of diseases using individual-specific genetic data. This thesis aims to comprehensively and comparatively evaluate the predictive performance of machine learning methods for Behçet's disease and respiratory infections. Additionally, feature selection methods were employed to identify the genetic factors (such as SNPs and genes) associated with disease presence for both diseases. Furthermore, the usability of selected features depending on biological pathway-driven active subnetworks listed in the literature was analyzed for the prediction of Behçet's disease. For the respiratory infection prediction problem, on the other hand, the prediction performance of features calculated by single-sample gene set enrichment analysis (ssGSEA) was evaluated using different machine learning methods. As the data types used in both experiments were different (genome-wide association studies data, gene expression profiles), the performance of machine learning approaches on different data types was also observed. It is hoped that the findings of both experiments will contribute to future machine learning based disease prediction studies.
GG (Genişletilmiş Gerçeklik)'in Mimari Tasarım Eğitimine Etkisi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Kıdık, Ayşegül; Asiliskender, Burak
The '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.

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