Fen Bilimleri Enstitüsü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12573/192

Browse

Now showing 1 - 20 of 180

Accelerating computer algorithm by using GPU
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Yalçın, Salih; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Travelling 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.
An accurate investigation of the mechanical response and damage model of aluminum 7068
(Abdullah Gül Üniversitesi, 2018) Karaveli, Kadir Kaan; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Karaveli, Kadir Kaan
The promising combination of high strength, high toughness, low density and corrosion resistivity have made aluminium (Al) alloys the material of choice in various applications, from buildings to aerospace, for decades. Especially, Al 7068 alloy is one of the recently developed materials used mostly in defence and automobile industries due to their exceptional mechanical properties. In this master thesis, the mechanical response and Johnson-Cook damage model of Al 7068-T651 alloy was investigated. Specifically, different Johnson-Cook damage parameters were determined for different application areas considering the maximum, minimum and average results. These damage parameters can be used for accurate Finite Element Analysis simulations. To determine these damage parameters tensile tests were conducted on notched and smooth specimen son both rolling direction and perpendicular to the rolling direction. The notch radius were selected as smooth, 0.4 mm, 0.8 mm and 2 mm to provide different stress triaxiality values and observe the mechanical response at these triaxiality values. Tensile tests were repeated seven times to obtain the accurate results. The final cross-sectional areas of fractured specimens were calculated through optical microscopy. The effects of stress triaxiality factor and rolling direction on the mechanical properties of Al 7068-T651 alloy were successfully investigated. All damage parameters were calculated via LevenbergMarquardt optimization method. Overall, three different Johnson-Cook damage parameters based on minimum, average and maximum equivalent strain values were calculated. These Johnson-Cook ii damage parameters can be utilized for the accurate damage simulations of different applications in Finite Element Analysis, which is a computational technique and is used to obtain approximate solution of several engineering problems
Adaptive online torque sharing function to mitigate torque ripple in switched reluctance motors
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Genç, Ufuk; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Electrical 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.
Advancing machine learning analysis of non-coding RNA: A novel approach of negative sequence generation
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Orhan, Mehmet Emin; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı
Many supervised machine learning models have been developed for the classification and identification of non-coding RNA (ncRNA) sequences. These models play a significant role in the diagnosis and treatment of various diseases. During such analyses, positive learning datasets typically consist of known ncRNA examples, some of which may even be confirmed with strong experimental evidence. However, there is no database of validated negative sequences for ncRNA classes or standardized methodologies for generating high quality negative samples. To overcome this challenge, a new method for generating negative data called the NeRNA (Negative RNA) method has been developed in this study. NeRNA generates negative sequences using known ncRNA sequences and their octal representations, similar with frame shift mutations found in biology but without base deletions or insertions. In this thesis, the NeRNA method was tested separately with four different ncRNA datasets, including microRNA (miRNA), transfer RNA (tRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA). Additionally, a species-specific case study was conducted to demonstrate and compare the performance of the study's miRNA predictions. The results of 1000-fold cross-validation on machine learning algorithms such as Decision Trees, Naive Bayes, Random Forest classifiers, and deep learning algorithms like Multilayer Perceptrons, Convolutional Neural Networks, and Simple Feedforward Neural Networks showed that models developed using datasets generated by NeRNA exhibited significantly high prediction performance. NeRNA has been published as an easy-to-use, updatable, and modifiable KNIME workflow, along with example datasets and required extensions that can be downloaded and utilized. NeRNA is designed specifically as a powerful tool for RNA sequence data analysis.
AHP-based evaluation of the suitability of public facilities: The case of Melikgazi, Kayseri
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Yılmaz, Elif; AGÜ, Fen Bilimleri Enstitüsü, Sürdürülebilir Kentsel Altyapı Mühendisliği Ana Bilim Dalı
Public facilities in urban areas, such as those for health and education, are expected to meet various humanitarian requirements. It is important to ensure that these facilities are suitable in all aspects in the urban areas. The aim of this thesis is to evaluate the suitability of public facilities proposed by zoning plans in the study area of Melikgazi District, Kayseri Province, by integrating Analytic Hierarchy Process and Geographic Information Systems. To evaluate the suitability, health facilities, green areas, kindergarten areas, primary school areas, secondary school areas, high schools and mosque areas proposed in the zoning plan were analyzed by considering the main criteria and sub-criteria determined within the scope of population density, transportation facilities and technical infrastructure services. The criteria were reclassified with Geographic Information Systems using the Analytic Hierarchy Process to calculate weight values for the Weighted Overlay and Weighted Sum analyses. The analyses identified non-suitable areas, suitable areas, and very high suitable areas. The study area was evaluated comparatively for each public facility using Weighted Overlay and Weighted Sum analyses to identify areas with suitable results and those in need of new public facilities. The results indicate that the primary school and mosque areas have suitable results, but other public facilities are still needed in areas close to the center with high population density
Analysing network traffic and detecting network threats by using the algorithms of machine learning
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Küçükkoç, Abdurrahman; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
As information technologies progress, the possibilities of access to information increase and therefore it becomes difficult to ensure the security of information. Today, with the use of information systems in all areas of life, network threats have also increased. The increase in individual access to and use of the internet has also brought network threats. In addition, the latest developments in information technologies, developing global communication networks, the internet of things aiming to connect all objects with networks, cloud technologies, the spread of mobile internet and the renewal of devices have brought network threats and uncertainties. Network threats increase the security vulnerabilities in the information and communication systems of individuals and organisations day by day. This situation causes systems to malfunction, economic damage and cyber security to be jeopardised. In order to contribute to individuals, institutions and organisations, our thesis aims to protect information systems against network threats, to ensure data confidentiality, integrity and accessibility, to detect network threats in advance and to take measures against these threats. We believe that by analysing heterogeneous network traffic, which includes most network attacks on the Internet, and using machine learning algorithms, we will reach a result close to reality in the detection of network threats. In line with this result, we will be able to take precautions against network threats in information systems and structures
Antimicrobial peptide activity prediction using machine learning methods
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Söylemez, Ümmü Gülsüm; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this thesis, using the multiple properties of the peptides we aimed to develop machine learning approaches that can predict the antimicrobial activities of the peptides. We have created two datasets for the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately. In our first study, ten different physico-chemical properties of the peptides were calculated, and used as features of the peptides. Following the data exploration and data preprocessing steps, a variety of classification models were build with 100-fold Monte Carlo Cross-Validation; and the performance of these models were evaluated. In the second study, we proposed a novel method called AMP-GSM. The method was tested for three datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. In the last study, using motif matching score with the models of activity against Gram-positive and Gram-negative bacteria, we created novel peptides and predicted the target selectivity of these peptides. The studies presented in this thesis advance the field of computational research by making it easier to predict the possible antimicrobial effects of peptides and to design AMPs in wet laboratory environments.
Architecture for machines; production spaces in transition
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Pekdemir Başeğmez, Merve; AGÜ, Fen Bilimleri Enstitüsü, Mimarlık Ana Bilim Dalı
The industry has experienced a new revolution. In this revolution defined as Industry 4.0, smart systems have started to be used in production. Thanks to smart production technologies, a new method has been created in which production can continue non-stop, and workers can monitor the entire process remotely with the help of smart robots and machines. Production spaces designed following this technology are defined as smart factories. While machine spaces where unmanned production methods are applied are being designed in the revolution, on the other hand, humans and production are trying to come together again. This study first investigates how human and machine cooperation changes space. The technologies that have caused revolutions in production since the first invention of the revolution, the steam engine, and the factories transformed by these technologies have been examined. Then, the first smart factories and production spaces in which smart production was carried out in the last revolution of the industry were analysed. With the help of references from the past of production in the context of human, space, and city, the present and future are discussed, and new concepts and codes are produced. Finally, "Plug-in production" proposal is developed for the factory architecture and production environment.
An assessment on the Turkish construction industry's approach to innovative facade cladding materials
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Batmaz, Nurefşan; 0000-0001-6212-4330; AGÜ, Fen Bilimleri Enstitüsü, Mimarlık Ana Bilim Dalı
Innovation is generally indicated as the adoption of an idea or behavior which is new to an organization to help to change the current state for the better. In the competitive business environment of a globalized economy, the significance of innovation is rising day by day. The desire to enhance their products and services and reduce costs made construction innovation a challenge among construction companies. The implementation of innovation in building materials and technologies involves the opportunity of creating innovative conditions for architectural design and affects contemporary architectural practices. Within these advancements in building materials and technologies, the expectations from facade design and systems are also influenced inevitably. In this thesis, it is aimed to understand the place of the concept of innovation in the construction industry and the perception of the construction companies in the process of introducing new materials or products to the Turkish construction industry. In accordance with this aim, in this research, facade cladding materials are chosen due to the availability of different systems, materials, and application methods, and the companies that produce facade cladding products are focused. On the basis of this approach, a case study with a product investigation and a survey is conducted, then evaluated and discussed at the end of the study.
Automated processing and classification of medical thermal images
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Özdil, Ahmet; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
The aim of this dissertation is to develop computer aided methods for processing and evaluating medical infrared thermal images. Throughout this study three problems were evaluated. The first problem was to automatically classify the body part and pose in the thermal images. In this study there were four classes; upper-lower body parts with back-front views. The first step included the segmentation of the background with Otsu’s thresholding method applying histogram equalization. Next, DarkNet-19 architecture was used to extract features from images and these features were reduced using PCA and tSNE methods. Finally reduced feature sets were used for classification. The second problem was to automatically classify liver steatosis from using thermal images. In this study, the classification problem was tested on an anatomical region of interest from abdominal images corresponding to the liver. Deep learning and texture analysis methods were employed for feature extraction, and then the selected feature sets were used for classification. The third problem was to quantify thermograms of multiple sclerosis (MS) patients for better assessment of the disease and monitoring the therapy. Thermal images of two patients and a healthy control from lower limbs were evaluated during experiments, and localized quantification of the effect of MS on the feet of the patients using thermal images method was proposed. The proposed method was fully correlated with the evaluations of physician. It is shown that medical thermal imaging has high potential in many fields of medicine as a non-invasive method for pre-diagnosis and follow-up.
An autonomous heterogeneous multi-robot system design for early fire detection
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Serin, Ömer Faruk; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
The 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.
BBSome regulates ARL13B-dependent joint elongation of two distinct cilia in Caenorhabditis elegans
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Turan, Merve Gül; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı
Cilia or flagella are interchangeably used to refer to the hair-like organelles extending from the cell surface to communicate with environmental signals or triggers. Cilium, the singular form of cilia, and its components are well-conserved structures throughout evolution and are divided into motile and primary cilium. The primary cilia of different cells are seen to form joint cilia by extending in parallel. For instance, PHA and PHB primary cilia in C. elegans protrude from the ends of the dendrite but extend parallel to one another and intersect in the middle portion of the cilia, reaching the same length. Nevertheless, the molecular mechanisms underlying how parallel cilia get similar lengths remain mysterious. In this thesis, we used C. elagans as a model organism to examine the molecular mechanism associated with the cilia direction. We generated various single, double, and triple mutants to examine PHA and PHB cilia for phenotype and length. We found that a Joubert syndrome protein, ARL13B, is required for determining cilia direction in PHA & PHB cilia and ASE & ASI cilia.
A BIOMIMETIC APPROACH FOR SMALL DIAMETER VESSELS: BILAYERED VASCULAR GRAFTS MADE of ALGINATE and POLY(-CAPROLACTONE)
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2019) GÜRDAP, Seda; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı
Cardiovascular diseases (CVDs) still remain one of the leading causes of morbidity and mortality across the world. A typical symptom of CVDs is the vascular occlusion. There are many strategies for treatment such as angioplasty, stent application and bypass grafting. Although synthetic blood vessels are successfully used in bypassing of the medium (>10 and<6) and large sized (10 mm) vessels, they have high failure problem for the replacement of small diameter ( 6 mm) vessel because of early thrombosis formation. Tissue engineering, mimicking the structural, mechanical and cell growth characteristics of the native vessels is a promising treatment method for CVDs. In this study, it was aimed to fabricate a bilayered vascular scaffold by combining thermally induced phase separation and electrospinning methods. First, alginate porous layer was produced as the inner layer with the average pore diameter of approximately 100 μm to enable endothelial cell attachment and proliferation. Then, the inner layer was covered with electrospun polycaprolactone (PCL) membrane to strength the endurance of vascular graft. The mechanical test showed that the bilayered vascular scaffold has a close mechanical characteristic to native vessels with elastic modulus of 2 .45 1.7 MPa and estimate burst pressure of 0,18 MPa. Also, heparin was chemically immobilized to scaffold to elongate the release time, which can result in reduced thrombosis. In addition, cross-linked scaffold lost 21% of its mass for 6 weeks showed the moderate degradation level that can support the neotisue formation via cell migration to the scaffold, while the scaffold is synergistically degraded. According to the results, the materials prepared by biomimetical approach revealed that they have a great potential to be used as a synthetic vascular graft.
Biosynthesis of high value-added carotenoids by engineered microorganisms
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Arslansoy, Nuriye; AGÜ, Fen Bilimleri Enstitüsü, Biyomühendislik Ana Bilim Dalı
Carotenoids are pigment molecules that play an important role in coloring plants, algae, and other organisms. These molecules exhibit various biological activities such as anticancer, antiviral and antioxidant activities. They have a huge market size and are mainly used in the food, feed, and cosmetic industries. The current supply chain for carotenoids is mostly relied on the extraction from plants and/or chemical synthesis for certain carotenoids. However, these strategies have various bottlenecks and disadvantages such as being affected by climate change, more difficult and costly extraction processes, and environmental issues. These can be overcome with microbial biosynthesis, which not only addresses the previous problems but also provides advantages of producing in a short time and scale-up for industrial production. In this research, we aimed to biosynthesize the high value-added carotenoids by engineered microorganisms. The genome of a native producer of zeaxanthin diglucoside, identified as endophytic Pseudomonas sp. 102515, was first edited by CRISPR-Cas9 to knock out zeaxanthin glucosyltransferase (CrtX), lycopene β-cyclase (CrtY) and beta-carotene hydroxylase (CrtZ). This led to ΔcrtX, ΔcrtY and ΔcrtZ mutant strains of Pseudomonas sp. 102515. On the other hand, overexpression plasmids carrying crtW, CaZEP and CaZEP-CaCCSm40 genes were constructed and transformed to ΔcrtX mutant to synthesize astaxanthin, violaxanthin and capsanthin/capsorubin. HPLC analysis of extracts from mutant strains and overexpression strains revealed that all the engineered strains produced the corresponding carotenoids such as zeaxanthin, β-carotene, and lycopene. Thus, this study paved the way for the biosynthesis of valuable carotenoids in the engineered endophytic bacteria
BLOCKCHAIN BASED DATA SHARING PLATFORM FOR BIOINFORMATICS FIELD
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2020) ADANUR, Beyhan; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Recently, panomics studies attempt to identify new and actionable biomarkers by combining -omics data with other data types. In this context, there is a need to develop secure platforms that take into account ethical aspects and solve privacy and ownership issues as well as data sharing for an accurate analysis of -omics data. These days, blockchain technology has picked up significant attention in genomics, since it offers a new solution to these problems from a different perspective. In this thesis, we proposed a hybrid platform called GenShare, which is based on blockchain, homomorphic encryption and intel software guard extension (SGX) to provide efficient genomic data sharing, to perform statistical analysis and other similar processes on genomic data. While the proposed model solves security-privacy issues using homomorphic encryption and SGX, it solves other issues by using a combination of Hyperledger Fabric and Ethereum networks. In this study, Hyperledger Fabric network, which is the first phase of the GenShare model, setup is made and the performance of the network is tested with a different number of workloads. At the end of our performance evaluations, we concluded that the GenShare model has a potential to speed up the process of collecting and sharing data and it offers an efficient platform for the participants.
Blockchain based peer-to-peer energy trading applications
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Seven, Serkan; 0000-0003-2611-720X; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
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.
Blockchain-based energy applications: DSO perspective
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Yağmur, Ahmet; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
This 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.
The boron-rich amorphous materials
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) KARACAOĞLAN, Ayşegül Özlem; AGÜ, Fen Bilimleri Enstitüsü, Malzeme Bilimi ve Makine Mühendisliği Ana Bilim Dalı
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.
Calcined clays as supplementary cementing materials for sustainable concrete
(Abdullah Gül Üniversitesi, 2019) ARGIN, GİZEM; AGÜ, Fen Bilimleri Enstitüsü, Sürdürülebilir Kentsel Altyapı Mühendisliği Ana Bilim Dalı; ARGIN, GİZEM
The Portland cement, which is used as a binder material in concrete, has an important share for CO2 emission worldwide. For this purpose, supplementary cementing materials are used to be substituted with Portland cement in specific amounts. The usage of industrial by-products such as fly ash, silica fume, and slag as supplementary cementing materials seems advantageous. However, potential in availability of good quality by-products in a local scale have led to the search for feasible alternatives to these materials. The aim of this study is to evaluate the clay samples obtained from two different deposits in Turkey after calcination in terms of their use as supplementary cementing material. Chemical, mineralogical and thermal characterization of clay samples were made before and after calcination at various temperatures. Pozzolanic activity and reaction kinetics of the clay samples were evaluated with and without limestone addition by thermal analysis and isothermal calorimetry, respectively. Water requirement and strength activity index of calcined clays selected depending on their pozzolanic activity were also determined. The pozzolanic activity of clay containing a relatively higher amount of kaolinite mineral was determined to be higher. Clays calcined at 700 ºC showed the highest pozzolanic and strength activity whereas a calcination temperature of 1100 ºC results in a relatively lower activity. The limestone addition improved the pozzolanic activity, and the heat evolution during hydration. As BET surface area increased, the water requirement for calcined clay also increased.
CAMERA BASED SHEET MEASUREMENT SYSTEM FOR LASER CNC MACHINES
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2018) UMAR, Aamish; AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
Laser CNC machines are widely utilized for cutting metal sheets of varying thickness and materials. The sheets to be cut can be of varying dimensions and be placed at any desired area of the cutting table. The operator needs to assign the starting point of the laser manually along with the dimensions of the metal sheet in order to start the cutting process. The process of assigning the starting point and dimension of sheet are time consuming and can take few minutes before every cutting process and it sums up to hours by the end of daily cutting jobs, an automated process of sheet measurement can save considerable amount of time and speed up the process. In this thesis, a camera based system for automatic sheet measurement which includes measurement of starting point assignment, orientation, length and breadth has been developed. The algorithms have been implemented keeping in mind the importance of speed since the processing has to be done in real time and needs to be as fast as possible. The implemented algorithms can find all required parameters in about two seconds. The techniques utilized for its implementation have been discussed. The robustness of the system has been compared with other traditional methods of sheet measurement and orientation detection. The implemented system was tested on a real laser CNC machine over a period of six months and the test results have been discussed. Also, a camera based intrusion detection system for laser CNC machine has been developed in order to make it safe for human during operation. Patent application made for the implemented system.

Browse

Browsing Fen Bilimleri Enstitüsü by Language "eng"