Yazar "Bingol, Mehmet Safa" seçeneğine göre listele

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    DESIGN OF A PROPOSED NEURAL NETWORK FOR SOUND QUALITY ANALYSIS OF DIFFERENT TYPES FOR CAR SYSTEMS
    (Cefin Publishing House, 2024) Yildirim, Sahin; Bingol, Mehmet Safa
    Nowadays, in spite of advanced technology, there are still some sound problems on modern cars because of mechanical parts, oil lubrications, and electric motors. Due to these unwanted problems, it is necessary to design intelligent predictors such as artificial neural networks. In this investigation, a procedure of testing and evaluation on the sound quality of two types of cars are proposed and sound quality is analyzed through the cars road running test on the providing ground, which is carried out with varying running speed. To improve and predict the results of experimental approach analysis, a proposed neural network predictor is also designed to model of the system for possible experimental applications. The proposed neural network is a feedforward type network, which consists of multi hidden layers. Three different training algorithms are used for training the network. As basic factors for sound quality, only objective factors a considered such as loudness, sharpness, speech intelligibility, sound pressure level. The correlation between sound pressure level and other factors are discussed from a point of view of running speed dependency. Results of both computer simulations and experiments show that the neural predictor algorithm gives good results at accommodating different cases and provides superior prediction on two cars’s sound analysis. © 2024, Cefin Publishing House. All rights reserved.
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    DESIGN OF INTELLIGENT CRUISE CONTROLLER OF MOTOR VEHICLES
    (Cefin Publishing House, 2023) Yildirim, Sahin; Bingol, Mehmet Safa; Savas, Sertac
    Nowadays, due to traffic jam and many cars on traffic, it is very necessary to control the distance between cars and obstacle. Many car producers have been designed and manufacture Cruise Control Systems for cars. Reinforcement learning, one of the popular artificial intelligence techniques, is a method used to train autonomous systems in many different fields. In this simulation study, the adaptive cruise control (ACC) of a ring bus serving in the campus area is controlled with Deep Deterministic Policy Gradient, which is one of the reinforcement learning methods. This simulation study is carried out considering the speed limit in the campus area and the acceleration values required for a comfortable journey of the passengers. Acceleration, velocity and distance values are given with graphs. Consequently; the proposal neural predictor has superior performance to adapt and predict the distance, velocity and acceleration of ego vehicle (bus). © 2023, Cefin Publishing House. All rights reserved.
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    NEURAL PREDICTOR DESIGN FOR COVID-19 CASES IN DIFFERENT REGIONS
    (Cefin Publishing House, 2023) Yildirim, Sahin; Durmusoglu, Aslı; Sevim, Caglar; Bingol, Mehmet Safa; Kalkat, Menderes
    COVID-19, which emerged in the past years, has affected human life in many different ways. The COVID-19 virus has spread very quickly around the world and has become a pandemic. In many applications, artificial neural networks are used to estimate system parameters in real-time or simulation-based methods. In this study, the daily and total number of cases in Turkey, Italy and India are predicted. Three alternative areas, with or without following rules, are chosen for the COVID-19 cases. For this prediction process, 3 different neural network methods are used: Nonlinear autoregressive neural network (NAR-NN), Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Autoregressive integrated moving average (ARIMA). The results obtained for 3 different neural networks are given with graphs and tables. The conclusion of this study may be used to improve the precaution for the pandemic. © 2023, Cefin Publishing House. All rights reserved.
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    Tuning PID controller parameters of the DC motor with PSO algorithm
    (Akademiai Kiado ZRt., 2024) Ylldlrlm, Sahin; Bingol, Mehmet Safa; Savas, Sertac
    Direct current (DC) motors have superior features such as operating at different speeds, being affordable and easily controllable. Therefore, DC motors have many uses, such as machine tools and robotic systems in many factories up to the textile industry. The PID controller is one of the most common methods used to control DC motors. PID is a feedback controller with the terms Proportional, Integral, and Derivative. The proper selection of P, I, and D parameters is critical for achieving the desired control in the PID controller. In this study, the transfer function of a DC motor is first obtained, and the speed of the DC motor is controlled by the PID controller using this transfer function. Then, Particle Swarm Optimization (PSO), an optimization method based on swarm intelligence, is used to adjust the P, I, and D parameters. By using the obtained P, I, and D coefficients, the speed of the DC motor is tried to be controlled, and the effect of the filter coefficient on the system output is examined. The performance of the proposed PSO-PID controller with successful results is given in tables and graphics. Control and optimization studies are carried out with MATLAB Simulink. © 2023 The Author(s).