Engineering can enhance students' theoretical knowledge and practical application skills when integrated into K-12 education. This approach encourages students to contemplate technology and engineering in more depth. Field programmable gate array (FPGA) technology training is becoming increasingly important, especially for vocational high school technical teachers. This study focuses on providing FPGA technology training to these vocational and technical teachers. The aim is to improve their theoretical understanding of FPGA digital design and practical application skills. By incorporating the latest technology into their courses, educators can offer students in-depth learning and practical experience in electronic engineering and digital design. The training program covered the basics of FPGA, its applications, and how it can benefit vocational high schools in laboratory environments. A total of 34 technical teachers participated in the training program. The effectiveness of the program was evaluated through pre- and posttraining questionnaires. The results showed that most of the questionnaire items were statistically and practically significant and varied according to the teachers' department, teaching experience, and level of education.

Günümüzde sürekli mıknatıslı senkron motorların (SMSM) tahrik sistemlerinde kullanımları giderek yaygın hale gelmektedir. Kontrollerindeki gelişimler bu motorların kullanıldığı servo sistemlerdeki konum ve hız takibinde iyileştirmeler sağlamaktadır. Bu çalışmada SMSM'nin uzay vektör modülasyonlu vektör kontrol yöntemini geleneksel PI kontrolörler kullanılarak simulinkte modellenmiştir. Bu model ayrıca geleneksel PI kontrolör yerine iki serbestlik dereceye sahip kesir dereceli PI (2-DOF FOPI) kontrolör kullanılarak modellenmiştir. Geleneksel PI ve 2-DOF FOPI kontrolör parametreleri karınca kolonisi optimizasyonu (KKO) ile belirlenmiştir. Modeller eşit şartlarda çalıştırılarak iki yöntemin performansları karşılaştırılmıştır. Elde edilen simülasyon sonuçları incelendiğinde, 2-DOF FOPI kontrolörün SMSM'nin alan yönlendirmeli kontrolünde geleneksel PI kontrolörden daha iyi performansa sahip olduğu görülmüştür.

Studies on academic performance prediction, a sub-branch of Educational Data Mining, have increased in recent years.Educational datasets in real environments often have class imbalanced and multi-class target variables.However, studies with these datasets are very few.In this context, in this study, with the ethical no decision of 23.05.2022-286783, using the data set of Marmara University (MU) Faculty of Technology (TF) students, a student graduation status estimation was made with the multiclass imbalanced educational dataset to identify the students at risk.1394 samples and 11 features were obtained through data preprocessing and feature selection (FS) stages.153 students belonging to 2016 were used for robustness control.3 different datasets containing 11, 7 and 5 features obtained with 7 different FS were created.Using 9 different sampling methods and 16 different machine learning algorithms, 750 different models were created.Models were checked for robustness.F1 Score and Repeated Stratified 5*5 fold-CV were used as success criteria.Hyperparameter settings were made with GridSearchCV.As a result, although ROS+RF was the most successful algorithm with an F1 Score of 0.9935, the most successful and most consistent models were the 7-featured None+ET, None+MLP, None+Bagging_DT and None+RF models.With these models, the decision support system web application was developed and presented to MU TF faculty members. Çok Sınıflı ve Dengesiz Eğitimsel

Quantum computation based on quantum physics is spreading rapidly with many interdisciplinary studies from machine learning to genetics. After the unveiling of real quantum computers, this process has been rising almost exponentially. We are about to experience an effect with quantum computers similar to the effect created by the process from the first electromechanical computers to very large‐scale integrated circuit computers. In the programming of quantum computers, we are in a similar place to programming classical computers at an early stage. There are various models in quantum computing, and the most widely used model is quantum gates. Therefore, an easy and practical understanding of the behavior of the gates used in the programming of quantum computers is of great importance. To realize this process, a virtual reality (VR)‐based teaching platform of quantum computer programming called QU‐VR Education Tool was designed by using Unity 3D VR. In the creation of this design, IBM's so‐called Quantum Composer platform and Microsoft Quantum Katas were taken as a reference. In this study, the design process and usage of a novel VR‐based platform are presented in detail.

Makerspaces are becoming increasingly important as a new approach to enable individuals to experience new technology applications and increase creativity in universities and other educational institutions. Such learning implementations offer many advantages like learning-by-doing and applying theoretical knowledge in engineering education into practical skills in an interdisciplinary environment. Despite the advantages, makerspaces still lack integration into the curriculum of engineering schools. For a quality engineering education, establishing maker workshops where students can experiment, design and practice as well as feel encouraged to open-ended development projects are required in addition to standard theoretical and laboratory applications. In this study, we present student views on the learning opportunities, challenges and contributions of the makerspace environment. In this context, IHA Makerspace established within the Faculty of Technology, Marmara University, Turkey, aims to provide high-level engineering experience to students by designing and prototyping effectively in a multidisciplinary development environment.

Today, efforts are made to develop and improve recommendation systems that will direct users to the right product according to their individual preferences during internet shopping. In this study, the recommendation system was designed with Autoencoders, which are one of the methods of deep learning and MovieLens dataset. While designing the system, various optimization algorithms, namely Gradient Descent, Gradient Descent with Momentum, RmsProp and Adam (Adaptive Momentum Optimization), were tried by using TensorFlow in the Python programming language. Moreover, the effect of increasing the amount of the data on the optimization algorithm was analyzed. Consequently, it was effectively demonstrated that the most successful one was the Adam algorithm with a test error of 1.363. It was also observed that decreasing the sparsity on the training data leads to a lower test error.

Quantum computers are expected to offer an effect similar to the influence of the early integrated circuit computers. According to current systems, it is predicted that they will play an effective role in the emergence of a stronger technology with an increasing speed. Some high-tech companies have quantum computer designs that they actively put into use from the research and development phase to the problem-solving phase and these computers use different architectures. Unlike the others, IBM launched a cloud-based software infrastructure in 2016 and first introduced its 5-qubit quantum computer, which consists of sequential quantum ports architecture, to the use of researchers and interested parties via its web servers. Programming is done by using quantum gates via a web interface called quantum composer. Significant progress has also been made in virtual reality technologies and virtual reality based browser platforms have been developed. In this paper, studies on the using and training of the IBM quantum composer platform through a browser designed on the basis of virtual reality are presented.

Electric Vehicles (EVs) are rapidly becoming the forerunners of vehicle technology. First electric vehicles were overlooked because of not having adequate battery capacity and because of low efficiency of their electric motors. Developing semiconductor and battery technologies increased the interest in the EVs. Nevertheless, current batteries still have insufficient capacity. As a result of this, vehicles must be recharged at short distances (approximately 150 km). Due to scheduled departure and arrival times EVs appear to be more suitable for city buses rather than regular automobiles. Thanks to correct charging technology and the availability of renewable energy for electric buses, the cities have less noise and CO2 emissions. The energy consumption of internal combustion engines is higher than of the electric motors. In this paper, studies on the commercial electric vehicle charging methods will be reviewed and the plug-in charging processes will be described in detail. This study strives to answer the questions of how plug-in charging process communication has performed between the EV and Electric Vehicle Supply Equipment (EVSE).

Quantum computers are becoming more and more powerful and practical each day with the perfection of the coherence time of superposition states and in terms of fidelity, error correction, power dissipation, and noise. Quantum computers have been used successfully in the solution of non-deterministic polynomial-time problems for a while. Using quantum algorithms to find ways to speed up the computing of some hard problems reveals a concept called quantum supremacy. With the improvement of quantum computers and a decrease in error rates, companies that develop quantum computers have offered cloud-based platforms to make their computers publicly available and to be programmed via the web. The cloud programming approach likewise influenced the programming of quantum computers. Here, we present that it is possible to use these platforms with VR sets, which are now becoming widespread in the field of education, and we try to introduce which tools are used in quantum cloud programming.

BACKGROUND: In the classical process, it was proven that ABPM data were the most significant attributes both by physician and ranking algorithms for dipper/non-dipper pattern classification as mentioned in our previous papers. To explore if any algorithm exists that would let the physician skip this diagnosis step is the main motivation of the study. OBJECTIVE: The main goal of the study is to build up a classification model that could reach a high-performance metrics by excluding ABPM data in hypertensive and non-diabetic patients. METHODS: The data used in this research have been drawn from 29 hypertensive patients without diabetes in endocrinology clinic of Marmara University in 2011. Five of 29 patient data were later removed from the dataset because of null data. RESULTS: The findings showed that dipper/non-dipper pattern can be classified by artificial neural network algorithms, the highest achieved performance metrics are accuracy 87.5%, sensitivity 71%, and specificity 94%. CONCLUSIONS: This novel method uses just two attributes: Ewing-score and HRREP. It offers a fast and low-cost solution when compared with the current diagnosis procedure. This attribute reduction method could be beneficial for different diseases using a big dataset.

Environmental as well as financial issues forces to develop clean, efficient, and sustainable vehicles which constitutes an integral part of our daily life for urban transportation. Nevertheless, exhaust emissions of conventional internal combustion engine vehicles are the major source of global warming lead greenhouse effect. One solution for this issue is hybridization/electrification of the vehicles. One of the most important tools which can help to test performances of technical solutions systematically is driving cycles representing real driving conditions for vehicle emissions testing and estimation. When the history of the driving cycles was reviewed, it can be seen that there were big changes from constructing synthetically to real world cycles and from emission-focused cycles to emission, pollution and fuel consumption focused cycles. And now, a new application such as hybridization and/or electrification has been added to driving cycles. Main aim of this study is to create a practical driving cycle for Bus Rapid Transit (BRT) vehicles. To do this, characteristic driving parameters such as speed, distance, time, acceleration have been determined first. Data acquisition from conventional vehicles running on Istanbul route was performed and then data were analysed. A driving cycle was developed by using Proportional Stratified Sampling (PSS) technique. Comparison between constructed driving cycle and the real-world data show that difference is less than 10%. And so, it can be concluded that proposed driving cycle was acceptable.

Life support devices developed for use in the treatment of respiratory insufficiency are called mechanical ventilators. Mechanical ventilators that mimic gas exchange between the respiratory system organs and the atmosphere using different methods are critical medical care devices that allow the patient to maintain respiratory functions. In this study, a mechanical ventilator prototype was developed for use in transferring intensive care infants between medical institutions. The ventilator prototype is designed to be used as a CPAP (Continuous Positive Airway Pressure) device for neonatal resuscitation if desired. Prototyping work has been carried out taking into account the safety precautions and sensitivity parameters specified in the relevant standards of the neonatal ventilators. The ventilator can be able to operate with pressure and volume control.

Gunumuz karayolu ulasim sistemlerinde trafik akisini duzenlemek uzere kullanilan sistemler genellikle geleneksel kontrol yaklasimi tabanlidir ve bu sistemlerin bircogu da zaman tabanli calisan acik cevrim denetim sistemleridir. Ozellikle buyuk sehirlerdeki olusan trafik kesmekesinin bu tip yapidaki sistemlerle cozumu imkânsiz hale gelmistir. Bu nedenle, geleneksel denetim yapilarinin yerine, mevcut yol ve kavsak kapa­sitelerinin optimum kullanimi ile trafik akisini hizlandirip gecikme surelerinin azaltilmasini saglayacak, kullanici-arac-altyapi-merkez arasinda cok yonlu veri alisverisi ile, izleme, olcme, analiz ve kontrol iceren, Akilli Ulasim Sistemleri’nin kullanilmasi zorunluluk haline gelmistir. Karmasik bircok yapiyi barindiran bu sistemlerin, insan faktorunu disarida birakan yapay zekâ teknikleri ile olusturulmus uzman sistemler kullanilarak denetimi gun gectikce yayginlasmaktadir. Bu calismada akilli ulasim sistemlerinin denetiminde kullanilacak yapay zeka teknikleri ve uzman sistemler hakkinda bilgi verilecektir.

Bu çalışmada, materyallerin kusur analizini gerçekleştirerek güvenli materyal kullanımı sağlayan tahribatsız test yöntemleri incelenmektedir. Tahribatsız test işleminde kullanılacak yöntemin, çalışma şartları açısından test edilecek materyal üzerinde uygulanabilir olması gerekmektedir. Örneğin girdap akımları yöntemi, çalışma şartları gereği ferromanyetik metalleri test edemezken, ferro olmayan metalleri test edebilmektedir. Bu şartlar göz önüne alınarak seçilen test yöntemi ile materyal düzenli aralıklarla tahribatsız muayene edilerek kusurlu materyal bölgeleri tespit edilmektedir. Bu şekilde güvenli materyal kullanımı sağlanarak büyük maddi kayıplar önlenmiş olur. Tahribatsız test yöntemlerinin bir kısmı güvenilir sonuçlar üretmesi açısından endüstride yaygın olarak kullanılmaktadır. Bu bağlamda manyetik kaçak akı, akustik emisyon, ultrasonik, girdap akımları ve radyografi yöntemleri bu çalışmada incelenmiştir. Yöntemlerin işleyiş prensipleri, literatürde yapılmış çalışmalar ve gerçekleştirilmiş deneyler incelenerek açıklanmıştır. Yöntemler karşılaştırmalı olarak seçilen kriterlere göre sınıflandırılmıştır. Tahribatsız test yöntemleri konusunda kapsamlı bir çalışma literatürde bulunmamaktadır. Bu çalışma ile geniş içeriğe sahip bir derleme ortaya çıkarılmıştır.

In this study, the design of embedded Sugeno fuzzy logic based controller for controlling non-linear liquid level process is realized. First, the system was activated on Matlab-Simulink platform and uploaded to Arduino Mega. With this study, activation of modern control methods on embedded systems is simplistically proven. When the control system operation was checked out, it has been observed that it was pretty successful.

Diabetes Mellitus (DM) is a high prevalence disease that causes cardiovascular morbidity and mortality. On the other hand, the absence of physiologic night-time blood pressure decrease can further lead to morbidity problems such as target organ damage both in diabetics and non-diabetics patients. However, the Non-dipping pattern can only be measured by the 24-hour ambulatory blood pressure monitoring (ABPM) device. ABPM has certain challenges such as insufficient devices to distribute to patients, lack of trained staff or high costs. Therefore, in this study, it is aimed to develop a classifier model that can achieve a sufficiently high accuracy percentage for Dipper/non-Dipper blood pressure pattern in patients by excluding ABPM data. The study was conducted with 56 Turkish patients in Marmara University Hypertension and Atherosclerosis Center and School of Medicine Department of Internal Medicine, Division of Endocrinology between the years 2010 and 2012. Our purpose was to find out if the proposed method would be able to detect non-dipping/dipping pattern through various data mining algorithms in WEKA platform such as J48, NaiveBayes, MLP, RBF. All algorithms were run to get accurate Dipper/non-Dipper pattern estimation excluding the attributes of ABPM data. The results show that Neural Network (MLP and RBF) algorithms mostly produced reasonably high classification accuracy, sensitivity and specificity percentages reaching up to 90.63% when the attributes were reduced. However in medical sciences, sensitivity is taken as a valid and reliable indication for diagnosis. Therefore, MLP had a highersensitivity percentage (83.3%) than others. Also, ROC values, which had the closest values to 1, were achieved by RBF for each selection mode. ROC was 0.872 for 10 fold CV mode and 0.856 for percentage split mode. Finally, ANN MLP and RBF algorithms were used, and was observed that RBF algorithm had the highest success rate in terms of sensitivity that was 83.3%. In medical diagnosis, a higher sensitivity performance is regarded as more valid indication of metric than a higher specificity. The proposed model could represent an innovative approach that might simplify and fasten the diagnosis process by skipping some steps in Dipper/non-Dipper diagnosis/prognosis.

The aim of this study was to design an expert system to predict the Non-Dipping or Dipping pattern by using several basic clinical and laboratory data through an artificial intelligence algorithm. Data Mining is a technique which extracts information from data sets by using a combination of both statistical analysis methods and artificial intelligence algorithms. Also in this study, the decision tree and naivebayes classification algorithms of this technique were used. Firstly, sixty-five patients (mean age 51±7 years, 40 females,) were included in the study. Systolic and diastolic dipping were found in 13 and 15 % of the patients, respectively. In the advancing process of the experiment, the number of instances were reduced, because of some missing data of the patients. The data sets were tested using the J48 decision tree algorithm. This classification algorithm was implemented on 56 instances, and also the number of attributes was reduced from 35 to 23. 66 % of the instances (37) were reserved for training and 44 % of the instances (19) were reserved for testing. When the algorithm was run, the Non-Dipper/Dipper pattern of the instances were correctly predicted in a rate of 73.6842 %. Model was built in 0.02 seconds. This pilot study shows that a machine learning algorithm can help in the prediction of diurnal blood pressure pattern relying on some basic demographic, clinical and laboratory data, with a reasonable accuracy.