Прегледај по Аутор "Stjepanović, Aleksandar"

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    Adaptive Modeling of Prediction of Telecommunications Network Throughput Performances in the Domain of Motorway Coverage
    (MDPI, 2021) Banjanin, Milorad K.; Stojčić, Mirko; Drajić, Dejan; Ćurguz, Zoran; Milanović, Zoran; Stjepanović, Aleksandar
    The main goal of this paper is to create an adaptive model based on multilayer perceptron (MLP) for prediction of average downlink (DL) data throughput per user and average DL data throughput per cell within an LTE network technology and in a geo-space that includes a segment of the Motorway 9th January with the access roads. The accuracy of model prediction is estimated based on relative error (RE). With multiple trainings and testing of 30 different variants of the MLP model, with different metaparameters the final model was chosen whose average accuracy for the Cell Downlink Average Throughput variable is 89.6% (RE = 0.104), while for the Average User Downlink Throughput variable the average accuracy is 88% (RE = 0.120). If the coefficient of determination is observed, the results showed that the accuracy of the best selected prediction model for the first variable is 1.4% higher than the accuracy of the prediction of the selected model for the second dependent variable. In addition, the results showed that the performance of the MLP model expressed over R2 was significantly better compared to the reference multiple linear regression (MLR) model used.
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    ANFIS MODEL FOR THE PREDICTION OF GENERA TED ELECTRICITY OF PHOTO VOLTAIC MODULES
    (2019) Stojčić, Mirko; Stjepanović, Aleksandar; Stjepanović, Đorđe
    The fact that conventional energy sources are exhaustive and limited are increasingly encouraging research in the field of alternative and renewable energy sources. The electricity generated by solar photovoltaic modules and panels occupies an ever greater percentage in total electricity production, so it is clear that photovoltaic systems are increasingly integrating with the existing electricity network into one system or functioning as autonomous systems. The aim of the research is to create a model based on the principles of the fuzzy logic and artificial neural networks that will perform the task of predicting the maximum energy of photovoltaic modules as accurately as possible. The prediction should facilitate work in planning production and consumption, system management, economic analysis. The most important methods used in the research are modeling and simulation. Input and output variables are selected and in the ANFIS (Adaptive Neuro Fuzzy Inference System) model a set of their values is presented. Based on them it comes to the function of dependency. The prediction rating of the created model was performed on a separate data set for testing and a model with the lowest average test error value was selected. The performance of the model was compared with the mathematical model through sensitivity analysis, which led to the conclusion that the ANFIS model gives more accurate results.
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    Model of Hybrid Electric Vehicle with Two Energy Sources
    (MDPI, 2022) Brtka, Eleonora; Jotanović, Gordana; Stjepanović, Aleksandar; Jausevac, Goran; Kosovac, Amel; Cvitić, Ivan; Kostadinović, Miroslav
    The paper proposes a Hybrid Electric Vehicle (HEV) design based on the installation of a fuel cell (FC) module in the existing Daewoo Tico electric vehicle to increase its range in urban areas. Installing an FC module supplied by a 2 kg hydrogen tank would not significantly increase the mass of the electric vehicle, and the charging time of the hydrogen tank is lower than the battery charging time. For design analysis, a model was created in the MATLAB/Simulink software package. The model simulates vehicle range at different HEV speeds for Absorbent Glass Mat (AGM) and Proton Exchange Membrane Fuel Cell (PEMFC) power sources. The greatest anticipated benefit derived from the model analysis relates to velocities ranging from 20 km/h to 30 km/h, although the optimal HEV velocity in an urban area is in the range of 30 km/h to 40 km/h. The results indicate that this conversion of Electric Vehicle (EV) to HEV would bring a benefit of 87.4% in terms of vehicle range in urban areas. Therefore, the result of the conversion in this case is a vehicle with sub-optimal characteristics, which are nevertheless very close to optimal.