Prof. Dr. Mehmet Reşit Tolun danışmanlığındaki tezler
15 tez · Başkent University, Aksaray University, Çankaya University
Gerçek zamanlı görev zamanlayıcı metotlarının uzay araçları simülasyonları üzerinde karşılaştırılması.
Günümüzde gerçek zamanlı gömülü sistem uygulamaları önemli rol oynamaktadır. Uydular ise uzay çevresel şartlarına dayanıklı gerçek zamanlı gömülü uygulamalardır. Ticari veya askeri bir uydu projesi yaklaşık üç yüz milyon dolar seviyesinde maliyetleri vardır bu sebepten birçok uydu üreticisi fırlatmadan önce uydularını doğrulama ihtiyacı duyarlar ve uydu simülatörleri en çok tercih edilen doğrulama altyapıları olarak öne çıkarmıştır. Özellikle Uydu merkezi bilgisayarında koşan uydu uçuş yazılımlarını doğrulamak önem kazanmıştır. Bu tezde gerçek zamanlı görev zamanlayıcılarına odaklanılmıştır. Altyapımıza uygun tek işlemcide koşan sabit öncelikli görev zamanlayıcılardan Round Robin (RR), Rate Monotonic (RM) ve Event Driven (ED) seçilmiştir. Çalışmamızda bu görev zamanlayıcılar işlemci kullanım performanslarına göre karşılaştırılmıştır. Görev zamanlayıcıları RTEMS işletim sisteminde 10 Hz ile çalışan bir kapalı döngü simülasyon altyapısında koşturulmuştur. Görev zamanlayıcıların performans karşılaştırılması için iki adet görev belirlenmiştir bunlar Yönelim Belirleme ve Kontrol Sistemi kontrolcüsü ile MIL-STD 1553 veri yolu kontrolcüsü görevleridir. Yapılan testlerde üç görev zamanlayıcısı ile bu iki görev koşturulmuş ve elde edilen sonuçlar birbirine yakın değerler çıkmıştır. Değerlendirme sonucu RR ve ED görev zamanlayıcıları seçilmiştir. RR uygulama kolaylığı ve ED'nin tasarımcıya tam kontrol sağlaması bu görev zamanlayıcılarını seçmemize büyük etkendir.
Model tabanlı geliştirme teknolojisinin hava aracı yazılımlarında kullanımı ve sertifikasyonu
Teknolojik gelişmelerin hızla ilerlemesi pek çok sektörde olduğu gibi havacılık sektöründe de ihtiyaç duyulan ürün ve hizmetler ile ilgili alternatiflerin artmasına imkân vermiştir. Bu durum üreticiler arasında sıkı bir rekabet ortamının oluşmasına neden olmuştur. Artan rekabet koşullarında hava aracı üreticileri, müşteri isteklerine daha kısa zamanda ve daha az maliyetle cevap vermek için yeni teknoloji arayışlarına girmişlerdir. Bu teknolojilerden birisi de, üreticilere sağladığı takvim ve maliyet avantajı nedeni ile son yıllarda havacılık sektöründe oldukça popüler hale gelen Model Tabanlı Geliştirme (MTG) teknolojisidir. MTG teknolojisi, geleneksel yazılım geliştirme yaklaşımına alternatif olarak geliştirilmiş, sistem ve yazılım seviyesindeki faaliyetlerin iç içe geçtiği yeni bir yazılım geliştirme yaklaşımıdır. Bu çalışmada, MTG teknolojisi ile geliştirilen hava aracı yazılımlarının yaşam döngüsü boyunca tamamlanması gereken faaliyetler, üretilmesi gereken veriler, hava aracı sertifikasyon süreci ve uçuş emniyeti kapsamında dikkat edilmesi gereken konular, bu konulara yönelik öneriler ve MTG teknoloji ile ilgili önemli noktaların sorgulanmasında havacılık sektörüne fayda sağlayabilecek soru listeleri sunulmuştur. Uygulama bölümünde ise, MTG teknolojisinde yapılması gereken model kapsama analizi ile yapısal kapsama analizi karşılaştırması yapılmış ve çalışmada anlatılan diğer önemli konulardan örnekler verilmiştir.
MPEG-4 ve H.264 video şifrelemesi için AES tabanlı algoritma
Nowadays data security has become the most global issue after global warming. Expectations about a world war in the field of data seem on the horizon. The internet connects all the people through many ways, such as e-mails or voice massages or even video calls. However, the flow of data between the source and destination which is the basic operation of the Internet, can by interrupted by another user or intruder. In order to prevent this intervention there are security algorithms meant to encrypt all the bits from starting point of flow up to the end point. The most widely used flow of data in the Internet is video streaming which takes about 80% of the data flow. In this thesis AES algorithm is used to encrypt the video streaming data. The present research aims to maintain a secure path for a video between the source and the destination by modifying and optimizing the existing AES data securtity algorithm. Amongst the analysed parameters are, PSNR, encryption time, and overhead bit through encryption with compression.
AES algoritmalarını kullanarak güvenli ses
This thesis study encryption \decryption text and voice by using AES algorithm, then comparing between them to know which one is better by using three standards. Speed, time duration and security. In this thesis we used five operation modes first ECB - Electronic Code Book. However the second one CBC - Cipher Block Chaining. The third one OFB - Output Feedback. The forth operation mode CFB - Cipher Feedback. The last operation mode is, CTR - Counter Mode. We cannot say which mode operation is the best. Because each of them have advantages and disadvantages but for encryption \decryption text counter mode is better than other mode operation. Because it is faster and simple for using more than other mode operations. After encrypted and decrypted audio by using the AES algorithm. Obtained different results however needed more time than text for encryption and decryption .in order to audio have more data than text Therefore, need more time than text for encryption and decryption (AbdelrahmanAltigani, Bazara Barry, 2013). According to speed for encrypt and decrypt text is faster than audio in order to for encryption and decryption audio have many data (AbdelrahmanAltigani, Bazara Barry, 2013).But text has less data if compared to voice. Then less data need less time. According to security audio is more secure than text and also more difficult to hack by hackers. Utilized Matlab code in this thesis in order to it has the capacity to plot graphical inclusive. use MATLAB is more appropriate than other packages like java and C#,in order to it has a large a collection of tools and libraries; moreover, for programming, it have not necessary for beginning from zero in order to MATLAB previously has functions for performing limited missions whilst some programs like C# should needs for beginning from zero.
İndüktif motorlarda uzman sistem ile vibrasyon analizi
This thesis presents an expert system for induction motor fault detection based on vibration analysis by using corvid expert system. Vibration signals of induction motors on four different actuating mechanism are collected with a specific vibration measuring device. The device evaluates the values with three harmonics in frequency domain. Expert system provides the recommendations as maintenance activity or the reason of the vibration by using vibration values. This system is tested and validated on four type of actuating mechanisms. Obtained results show that this system can detect faults in early stages with high accuracy and reliability. Thus, it provides malfunction and failure prevention and improves overall performance and efficiency of industrial systems.
3 serbestlik derecesine sahip (RRR) robotun yörünge planlaması ve uygulaması
In this study, the path planning of the RRR robot with 3 degrees of freedom has been made and has been applied on the model robot arm produced with 3d printer. Time optimization has been made by using Genetic Algorithms during path planning. Since the first three joints affect the position in the Cartesian space, the robot arm with the RRR structure which has the three rotating joints, was used as the model. Firstly, the forward kinematic analysis of the robot arm was performed. Denativ-Hartenberg method, which is the most widely used analysis method, was chosen as the forward kinematic analysis method. Inverse kinematic analysis was performed with the information obtained from the forward kinematic analysis. As a result of inverse kinematic analysis, the joint variables required for the model robot to reach a point in Cartesian space were obtained. Kinematic analyzes were performed to planning path in the joint space of the robot. Time optimization was performed by using Genetic Algorithms in order to complete the movements of the robot arm as soon as possible while planning path. The velocity and acceleration equations obtained for each joint as a result of path planning in joint space were used as objective functions in optimization. The limits of the objective function are the velocity and acceleration values of the servo motors used in the joints of the robot arm. As a result of optimization, it was found that each joint can complete the movement as soon as possible. The model robot arm was produced with the 3d printer and the experimental set was created. The data obtained as the result of optimization were compared with the data obtained from experimental studies. At the end of this study, time optimization was made by using Genetic Algorithms and a model which completed the movement in the shortest time was created that could be applied to any robot.
Fotovoltaik panel hatalarının termal kamera ve UV led ile tespiti
In this thesis, hot spot failures, panel surface fractures, and yellowing problems on 40 Watt polycrystalline photovoltaic (PV) panel were analysed through indoor laboratory experiments and outdoor fieldwork. The outdoor field tests were performed with a 40 Watt PV panel on a clear and sunny day in the garden of Ortaköy Vocational School of Aksaray University. It is observed that the output power value of the panel decreases in the open circuit fault which was created by disconnecting the series-connected cells within the panel. In addition to the observed change in the power value, temperature increase value due to broken glass surfaces and disconnected cells on the panel was observed with a thermal camera. In the indoor laboratory experiments, artificial shading and yellowing parts detection studies were performed with UV LED and photodiode which is designed as a rectangular prism and placed at 45-degree angles. To perform these works on the photovoltaic (PV) panel, the user interface was developed and the graphical values of the manually scanned panel were plotted on the screen. Besides, colour changes on the PV panel were tried to be determined at different spectrum values. Blue, green, yellow and red LED have been used to do this. It is understood from the figure that the blue led spectrum is most effective method to obtain PV diagnosis. According to the results obtained, colour changes on the PV panel could not be obtained in colours other than blue. Additionally, common faults in PV systems which are shading situation, line to line faults, panel soiling problem, increasing resistance value problem between panels, problems caused by bird droppings or tree leaves and branches, and the effect of temperatures on the photovoltaic (PV) panels were simulated, analysed, and P-V and I-V graphics of the panels were drawn.
Akdeniz meyve sineğinin (Ceratitis capitata) akıllı sistem ile tespit edilmesi
Nowadays, the most critical agriculture-related problem is the harm caused to fruit, vegetable, nut, and flower crops by harmful pests, particularly the Mediterranean fruit fly, Ceratitis capitata, named Medfly. Medfly's existence in agricultural fields must be monitored systematically for effective combat against it. Special traps are utilised in the field to catch Medflies which will reveal their presence and applying pesticides at the right time will help reduce their population. A technologically supported automated remote monitoring system should eliminate frequent site visits as a more economical solution. This paper develops a deep learning system that can detect Medfly images on a picture and count their numbers. A particular trap equipped with an integrated camera that can take photos of the sticky band where Medflies are caught daily is utilised. Obtained pictures are then transmitted by an electronic circuit containing a SIM card to the central server where the object detection algorithim runs. This study employs a faster region-based convolutional neural network (Faster RCNN) model in identifying trapped Medflies. When Medflies or other insects stick on the trap's sticky band, they spend extraordinary effort trying to release themselves in a panic until they die. Therefore, their shape is badly distorted as their bodies, wings, and legs are buckled. The challenge is that the deep learning system should detect Medflies of distorted shape with high accuracy. Therefore, it is crucial to utilise pictures that contain trapped Medfly images with distorted shapes for training and validation. In this academical study, the success rate in identifying Medflies when other insects are also present is 94.05%, achieved by the deep learning system training process, owing to the considerable amount of purpose-specific photographic data. This rate may be seen as quite favourable when compared to the success rates provided in the literature.
Uçak motorlarının baroskopik incelemesinde derin öğrenme kullanılarak hasar tespiti
Aircraft engine inspection is a key pillar of aviation safety by maintaining adequate performance standards to ensure the airworthiness of an engine. In addition, it is vital for asset value retention. Borescope inspection is currently the most widely used aircraft engine visual inspection method. However, borescope inspection is a time consuming, subjective, and complex process which heavily depends on the experience of the inspector as well as on their concentration level during inspection. On the other hand, cost saving of airlines and maintenance, repair, and overhaul (MRO) centers expose pressure and workload on inspectors. These make an automated system to support damage detection during borescope inspection necessary to avoid potential risks. Deep learning has found very wide application and has proven to be very successful during the last 10-15 years in the image recognition domain. In this research, we suggest a deep learning based automated damage detection framework from aircraft engine borescope inspection images. Faster R-CNN based deep learning model with Inception v2 feature extractor is utilized for the architecture. Due to the limited number of images, data augmentation and other overfitting methods are employed. The framework supports crack, burn, nick and dent damage types across all modules of turbofan engines. It is trained and validated with moderate to complex borescope images obtained from the field. The framework achieves 92.05% accuracy for nick or dent, 92.64% for crack and 81.14% for burn damage classes. Moreover, it achieves 88.61% average accuracy.
Phylogenetic supertree construction using constraint programming
PHYLOGENETIC SUPERTREE CONSTRUCTIONUSING CONSTRAINT PROGRAMMINGAlkım ÖzaygenIn biology, a phylogenetic tree, or phylogeny, is used to show the genealogicrelationships of living things. It is a codification of data about evolutionary history. The treeof life shows the path evolution took to get to the current diversity of life and can help usalso to search for the genealogy of disparate living organisms.In this thesis our aim is to provide a different approach for the construction of TheTree of Life. That is, we will propose a constraint programming solution to the decisionproblem of constructing a supertree that is compatible with a collection of givenphylogenetic trees that share some species, which we will encode as constraint satisfactionproblems.
Content analysis of un-structured local internet news websites
In today?s world data is a real power. In order to get advantage of the data power, analysis of the data is very important. Social incidents have been analyzed for more than a century. In order to understand social incidents better, data has big importance. Localized social analysis can be easily done by analyzing the local Internet content. In this study methods for analysis of the local news websites are discussed. On the other hand, a solution is introduced to overcome problems of un-structured website designs such as Turkish character set problems, non standard development techniques, unrelated contents such as advertisements and comments. An algorithm and code was developed to filter and index news website content. As a result code was implemented in a website and proved to be running.
A survey of artificial intelligence techniques for capability maturity model integration (CMMI)
Our purpose in this thesis is to investigate the current artificial intelligence applications in scope of the CMMI process areas. Firstly, research is made regarding the CMMI Model. Then, current studies about CMMI process areas by using artificial intelligence techniques were examined.The overall aim of the thesis is to perform a survey about artificial intelligence techniques conjunction with Capability Maturity Model Integration (CMMI) process areas. As a result, future work evaluation in artificial intelligence applications with CMMI was performed.
Makine öğrenmesi teknikleri kullanılarak sybil botların tespit edilmesi
Bu çalışma, NSL-KDD veri seti kullanılarak ağ tabanlı anomali tespiti amacıyla çeşitli makine öğrenmesi algoritmalarının performansını karşılaştırmalı olarak değerlendirmeyi amaçlamaktadır. NSL-KDD, saldırı türlerini dört ana başlıkta (DoS, Probe, R2L, U2R) toplayan, etiketli ve dengeli yapısıyla denetimli öğrenme yöntemleri için uygun bir veri seti olarak ele alınmıştır. Çalışma kapsamında veri seti üzerinde öncelikle istatistiksel analizler ve veri keşif çalışmaları gerçekleştirilmiş, ardından veri ön işleme adımları uygulanmıştır. Bu süreçte kategorik değişkenler sayısal forma dönüştürülmüş, eksik veriler temizlenmiş ve azınlıkta kalan sınıflar SMOTE yöntemiyle dengelenmiştir. Özellik seçimi için Mutual Information (MI) yöntemi kullanılarak en bilgilendirici 15 değişken belirlenmiş ve model eğitimi bu özellikler kullanılarak gerçekleştirilmiştir. Sonrasında tüm değişkenler kullanılarak modeller tekrar eğitilmiş ve sonuçlar kıyaslanmıştır. Modelleme aşamasında Lojistik Regresyon, Naive Bayes, Random Forest, K En Yakın Komşu (KNN), Destek Vektör Makineleri (SVM), AdaBoost ve Yapay Sinir Ağı (ANN) algoritmaları kullanılmıştır. Her model için hiper parametre optimizasyonu GridSearchCV veya RandomizedSearchCV yöntemleriyle yapılmıştır. Modellerin başarısı doğruluk (accuracy), kesinlik (precision), duyarlılık (recall) ve F1 skoru gibi değerlendirme metrikleri kullanılarak analiz edilmiştir.Elde edilen sonuçlar, NSL-KDD veri seti üzerinde bazı modellerin özellikle DoS gibi baskın sınıflarda yüksek doğruluk sağlarken, azınlıkta kalan R2L ve U2R saldırı türlerinde performans düşüşleri yaşandığını göstermektedir. Bu durum, dengesiz veri setlerinde kullanılacak yöntemlerin dikkatli seçilmesinin gerekliliğine işaret etmektedir.
Automatic threat detection in X-ray images using deep learning
Automated object detection in X-ray baggage screening is critical for maintaining security and operational efficiency in high-throughput environments such as airports. Traditional methods often struggle with the unique challenges of X-ray imagery, including overlapping objects and low contrast. Recent developments in deep learning, and the YOLO (You Only Look Once) architecture specifically, have been especially promising for real-time object detection. This thesis benchmarks the performance of the newest YOLO variants— YOLOv8, YOLOv9, and YOLOv10—on three X-ray baggage datasets that are widely used—CLCXray, PIDXray, and SIXray. The comparison is done based on important parameters such as detection accuracy, inference speed, and computational cost to evaluate their applicability in real-time applications. Comprehensive experiments are implemented to evaluate their performance in object detection in dense and complicated scenes with an emphasis on the trade-off between detection accuracy and processing time. Results indicate that YOLOv10 performs best overall with higher accuracy and quicker inference at low computational complexity. YOLOv8 and YOLOv9 also show competitive performance with benefits under certain circumstances. Results indicate the efficacy of recent YOLO models in meeting the requirements of real-world X-ray baggage screening systems and their readiness for deployment in operational security settings. This research provides a detailed analysis of cutting-edge object detection models, contributing immensely to the knowledge of their application in real-world scenarios and paving the way for the creation of more sophisticated automated security systems.
Türkiye'deki enerji üretiminin verisel analizi ve model geliştirmesi
Bu çalışma, Türkiye'nin elektrik enerjisi üretimi, tüketimi ve dağıtım sistemlerini kapsamlı bir şekilde ele almaktadır. Elektrik enerjisi üretiminde yenilenebilir ve yenilenemez enerji kaynaklarının mevcut durumu analiz edilerek, bu kaynakların enerji arz güvenliğine ve çevresel sürdürülebilirliğe etkisi tartışılmıştır. Türkiye'nin enerji tüketim verileri yıllara göre detaylı bir şekilde değerlendirilmiş, bölgesel ve sektörel farklılıklar istatistiksel yöntemler kullanılarak incelenmiştir. Enerji dağıtım altyapısının mevcut durumu ve karşılaşılan zorluklar analiz edilmiş, özellikle yenilenebilir enerji kaynaklarının elektrik dağıtımına entegrasyonunun potansiyeli ortaya konulmuştur. Veri analizi süreçlerinde doğrusal regresyon modelleri ve diğer istatistiksel yöntemler kullanılmış; bu sayede enerji üretim ve tüketim trendleri analiz edilmiş ve geleceğe yönelik tahminler yapılmıştır. Elde edilen sonuçlar, Türkiye'nin enerji ithalatına bağımlılığını azaltacak, enerji verimliliğini artıracak ve çevresel etkileri minimize edecek stratejik öneriler sunmaktadır. Çalışma, hem enerji sektörüne hem de politika yapıcılara yenilikçi çözümler ve sürdürülebilir enerji yönetimi için yol gösterici bir rehber niteliğindedir.