Demand increase for reduction of fuel consumption and emissions of internal combustion engines has caused technology development in the automobile industry. A successful solution is downsizing the internal combustion engines and adding turbochargers to them. One of the consequences of adding turbocharger to the engine is slower transient response of the engine in comparison to the naturally aspirated engines. In this paper, the engine simulation is done in one-dimensional software GT-POWER and the torque transient response is being focused. For optimization, the coupling between two software of GT-POWER and MATLAB SIMULINK is used to find a rapid way for calculation of a proper strategy in order to utilize variable valve timing (VVT) technology during the transient. Variable valve timing in this study refers to opening and closing timing of inlet and exhaust valves. The optimization target is to maximize the torque integral during time interval of the transient. The transient in this paper is the one in which the engine speed is constant and the load increases rapidly and suddenly to a special value (step increase of load). Improved genetic algorithm is used for optimization. The studied engine is 1.65 liters EF7-TC which is a spark ignition engine equipped with turbocharger. تفاصيل المقالة

One of the methods in road detection is image processing, which is detected according to the contents of the image captured by the car camera. Much research has been done in this area by researchers, who have used a variety of methods to better process the road and detect the direction of vehicle movement. Using the photos taken by the camera installed on the car and their preprocessing, such as removing inefficient corners and turning it into a gray photo during various stages and applying a goblin filter on the photos and extracting the tallest diagram in order to detect the direction of the road. In this research, standard data processing such as line slope for neural network input and LVQ neural network are used to process road images and detect road direction based on three directions: straight, left and right. IAfter several experiments, it was found that the slope of the line in the full photo could not represent the route of the road, therefore, the photos were divided into two, three and four parts and another effective parameter in the system. the seven layers were used to start the neural network training and each time the number of layers increased and the processing results were compared. The best answers were obtained for the number of layers above 70, which is a small difference between the photos divided into three and Four were observed and with a difference of 2%, the four-part photos gave us the best answer. تفاصيل المقالة