Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design.

In this study, it is aimed to increase the projectile velocity by changing some parameters of the reluctance launcher. Studied parameters are the initial position of the projectile, the coating of the coil with ferrite, the coil length and the barrel outer diameter. A 3D Maxwell model of the reluctance launcher was generated to examine the effect of the change of these parameters on the projectile velocity. After the 3D model of the launcher was generated, analysis was done for each parameter. During the analysis for a parameter, the other parameters were kept constant. As a result of the analysis of the projectile position, it was determined that the projectile velocity is the highest when the projectile position is -2. It has been determined that the velocity of the projectile increases if the coil is coated with ferrite. It has been determined that the projectile velocity is the highest when the coil length is 7 cm. It has been determined that the highest projectile velocity is obtained when the barrel outer diameter is 18 mm. Using these results, the Maxwell model of the improved launcher was generated. The projectile velocity in the Maxwell model of the initial launcher is 19.24 m/sec. The projectile velocity of the improved launcher in the Maxwell model is 25.8 m/sec. By improving the launcher, a velocity increase of 34.09% was achieved. Later, this launcher was built and the parameters were investigated experimentally. In the experimental work, the projectile velocity of the initial launcher was measured as 19.11 m/sec, and the projectile velocity of the improved launcher was measured as 24.9 m/sec. As a result of the experimental work, a velocity increase of 30.29% was obtained by improving the launcher.

In this paper, the 3D model of a reluctance electromagnetic launcher was implemented using Maxwell program. The effect of the different projectile geometries was examined via the model. The projectile geometry with the highest velocity was determined. After constructing 3D model of the launcher, projectiles with different geometries were built and finally various projectiles with different geometries were analyzed. As a result, it was determined that the tubular projectile with 4 mm radius hole was the fastest among the tubular projectiles. Among the projectiles with notches, the projectile with 4 notches was launched faster than the others when the parameter was the number of notches, and the projectile with 0.5 mm notch radius was faster than the others when the parameter was the notch radius. In this paper, the highest velocity was reached the tubular projectile with 4 mm radius that was built with the 1050 material. The value of this velocity was 24.12 m/s.

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%. As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.

Fotovoltaik sistemler aracılığıyla güneş enerjisinden elektrik üretimi hem dünyada hem de Türkiye'de büyük bir ilgi görmektedir ve güneşten elektrik üretimi yapılabilmesi için güneş enerji santralleri kurulmaktadır. Bu çalışmada Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi binasına çatı tipi güneş enerji santralinin tasarımı, performans incelenmesi ve ekonomik analizi yapılmıştır. Güneş enerji santrali kurulacak binalar incelenmiş, uygun çatı alanına PVsyst ve Helioscope programları aracılığıyla çalışmalar yapılmış ve elde edilen sonuçlar karşılaştırılmıştır. Kurulması düşünülen güneş enerji santrali Sivas Cumhuriyet Üniversitesi Mühendislik Fakültesi binasının elektrik ihtiyacını karşılayabilecek şekilde planlanmıştır. PV modül sayısı 624 ve panellerin üreteceği güç 343 KWp olarak hesaplanmıştır. Santralin ilk yılda üreteceği toplam enerji miktarı 506.101 KWh’dır. Santralin Helioscope ve PVsyst programları kullanılarak elde edilen üretim öngörüleri, güncel elektrik fiyatları ve Elektrik Piyasasında Lisanssız Elektrik Üretim Yönetmeliği kullanılarak yapılmıştır. Tesis kurulumu için, 2025 yılı piyasa fiyatı göz önüne alındığında ve pazar araştırması yapıldığında güneş enerjisi santralinin maliyetinin 122.480 $ + KDV olarak ve yıllık işletme giderlerinin de 1.400 $ olduğu hesaplanmıştır. Kurulacak olan santralin ömrünün 25 sene olduğu varsayılmış, gerekli analizler ve hesaplamalar sonucunda 2 yıl 7 ayda amorti edebileceği sonucuna ulaşılmıştır.