Authors:Ali AJDER Abstract: Ice load on transmission lines is a critical factor that affects their cost and operation. National standards specify how ice load is considered in the design of power lines and poles. These standards generally use empirical relations that assume that the ice load on each phase accumulates uniformly and cylindrically. However, field tests and fault records show that the actual ice load on conductors is often not cylindrical due to altitude, wind strength and direction, and terrain topography. This study firstly defines several parameters to describe asymmetrical ice load. This load can cause additional vertical force on the line, conductor swing angle deviation, and sag changes. Since empirical equations are only valid for cylindrical ice load, the cross-sectional shape of the conductor must be transferred to millimeter paper, and calculations performed using one of several numerical integral methods. The coefficients for asymmetric ice are calculated in kg⁄m (N⁄m) using an AutoCAD model in the numerical study. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Authors:Arzu KEVEN Abstract: There are a limited number of studies in the literature that include detailed exergy analysis of vehicle air conditioning systems. In this study, in order to increase the performance of the air conditioning system in vehicles, a detailed exergy analysis has been made with the assumption that different refrigerants are used. R-134A, R-E245cb2, R-404A, R-1234ze(Z), R-161, R-1234zd(E), R-513A, R-1234ze(E) and R-1234yf has been chosen as the refrigerant. In the analysis, a comparison has been made by considering the environment, performance and safety values. While the COP values of the cycles increase with increasing evaporator temperatures, the COP values decrease at increasing condenser temperatures. On the other hand, exergy efficiency decreases with increasing evaporator and condenser temperatures. Also it is aimed to evaluate all the elements of a vehicle air conditioning system with exergy analysis. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Authors:Roya Boodaghi MALİDARRE; Huseyin OZAN TEKİN, Kadir GUNOGLU, Hakan AKYILDIRIM Abstract: Gamma ray is an energetic radiation type that can ionize and thus damage living cells as it slows down and transfers its energy to cells. Because of this harmful effect cell should be protected. Besides developing new alternative to lead and lead based materials, it should be interesting to obtain shielding properties of skin. This paper presents a results on the shielding properties of skin. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Authors:Sumeyra MUTİ; Kazım YILDIZ Abstract: Abstract: Recently, there have been studies on the use of machine learning algorithms for price prediction in many different areas such as stock market, rent a house and used car sales. Studies give information about which algorithm is more successful in price prediction using different machine learning methods. The most commonly used method for price prediction is the linear regression model. In our study, we examined the effectiveness of the linear regression model for used car price prediction. In our study, we applied the linear regression model on a data set that includes the features and price information of vehicles in Turkey for the year 2020. As a result, when we selected 1/3 of the data set as the test data, we observed that the R2 score for the prediction success of our model was 73%. More successful results can be obtained with different data sets or a more detailed data preprocessing. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Authors:Elif Ebru ÇAKI; Celal Onur GÖKÇE Abstract: In this study a novel shape descriptor for object recognition is proposed. As a preprocessing stage, Canny edge detection [4] is applied to input images. Output of Canny edge detector, namely edge image, is sampled and various number of points are selected. Chosen points are input to the new shape descriptor. Proposed shape descriptor is composed of deviations from average range and average angle. Shape descriptor is used as a feature extractor output of which is fed to linear classifier. Linear classifier is trained using pseudo-inverse and gradient descent techniques. Full MNIST dataset is used to test the system and results are reported. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Authors:Önder YAKUT Abstract: Diabetes is getting more and more common around the world. People suffer from diabetes or live at risk associated with this disease. It is necessary to prevent health problems caused by diabetes, to reduce the risk of diabetes and to reduce a load of diabetes on the health system. Therefore, it is important to diagnose and treat diabetic patients early. In this study, Pima Indian Diabetes (PID) database was used to predict diabetes. Random Forest Classifier, Extra Tree Classifier and Gaussian Process Classifier machine learning methods have been used to predict whether individuals have diabetes or not. In this study, the method with the highest prediction accuracy was determined as the Random Forest Classifier. The accuracy of the recommended method was 81.71%. The proposed method was developed to assist clinicians in predicting diabetic patients using diagnostic measurements. The machine learning methods developed in this study were applied using Colab Notebook a Google Cloud Computing service. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Authors:Celal Onur GÖKÇE; Volkan DURUSU, Ridvan UNAL Abstract: In this study Proportional-Integral-Derivative (PID) control of brushed DC Motor is analyzed. The parameters of the PID controller are tuned with two different approaches, namely Ziegler-Nichols (ZN) and Particle Swarm Optimization (PSO). The system is tested under sinusoidal disturbance of varying frequencies in order to evaluate and compare disturbance rejection performances. It is shown that PSO approach has clearly higher performance compared with ZN approach for all disturbance frequencies. Simulations are done using Python programming language with trapezoid rule for differentiation and integration. Results are given in both figures and tables. Comments are done on results and future study is planned. PubDate: Fri, 31 Mar 2023 00:00:00 +030
Open Access journal
