Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2022Scopus SJRQ3/r/bayburt/12105
  Design of an efficient vehicle-actuated signal control logic for signalized intersections

  Cakici, Ziya; Murat, Yetis Sazi; Aydin, Metin Mutlu

  The effectiveness of fixed-time management systems dramatically decreases in case of fluctuating traffic demands in signalized intersection approaches. The result is the utter waste of time in traffic to be followed by material, psychological, and ecological problems. In recent years, to minimize the negative impacts of these problems, many researchers have focused on Intelligent Transportation Systems (ITS). As it is known, one of the applications of ITS is called vehicle-actuated (traffic-actuated) management systems. Because the success of these types of applications is directly related to the created control logic, the selection of the most proper control parameters for the created control logic is an important issue. This study aims to create an effective control logic and flow chart for the vehicle-actuated management system. At the end of the analyses, it is seen that the created vehicle-actuated management system can adapt to fluctuations in traffic demands in signalized intersection approaches. Average vehicle delays, fuel consumptions, and exhaust emissions can be reduced significantly by the created system. In case of fluctuations in traffic demands in intersection approaches, it is concluded that the performance of the intersection may increase enormously because of the created vehicle-actuated management system.

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  AtıfScopus4·WoS6·OpenAlex7

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• MakaleAçık Erişim2019Scopus SJRQ2/r/bayburt/13561
  Prediction of critical fraction of solid in low-pressure die casting of aluminum alloys using artificial neural network

  Teke, C.; Colak, M.; Kiraz, A.; Ipek, M.

  Casting simulation programs are computer programs that digitally model the casting of an alloy in the sand, shell, or permanent mold and, then, the cooling and solidification processes. However, obtaining consistent results out of casting modeling depends on the incorporation of many accurate parameters and boundary conditions. Critical Fraction of Solid (CFS), which is one of the most important of these parameters, is defined as the point where solid dendrites do not allow any flow of the liquid metal in the mushy zone. Since the CFS value varies based on many factors, inconsistent results can be experienced in the modeling applications. In this study, the CFS value obtained during the solidification of various commercial aluminum alloys' casting process, carried out using low-pressure die casting method, is predicted by using Artificial Neural Network (ANN) method based on alloy type, grain refiner and modifier additions, initial mold temperature, and pressure level parameters. In the scope of the study, 162 experiments are conducted. The results obtained from the low-pressure die casting experiments using a special model designed for the study are validated by using SOLIDCast casting simulation. The CFS values are obtained in this validation range of 33% to 61%. These CFS values are used in the development of ANN models. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Square Error (MSE) are used to assess the prediction accuracy of the ANN models. Calculated values of MAE, MAPE, and MSE are 0.0188, 7.06%, and 0.0006, respectively. The results show that the proposed ANN model predicts a CFS value with high accuracy.

  AtıfScopus6·WoS6·OpenAlex4

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