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A time-domain approach is presented to calculate electromagnetic fields inside a large Electromagnetic Pulse (EMP) simulator. This type of EMP simulator is used for studying the effect of electromagnetic pulses on electrical apparatus in various structures such as vehicles, a reoplanes, etc. The simulator consists of three planar transmission lines. To solve the problem, we first model the metallic structure of the simulator as a grid of conducting wires. The numerical solution of the governing electric field integral equation is then obtained using the method of moments in time domain. To demonstrate the accuracy of the model, we consider a typical EMP simulator. The comparison of our results with those obtained experimentally in the literature validates the model introduced in this paper.

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In this study a feed forward back propagation artificial neural network (ANN) model was established to predict Vickers microhardness in aluminum-alumina nanocomposites which have been synthesized by mechanical alloying and hot pressing. Volume percent of reinforcement, size of nanoparticles, force in microhardness test; and mechanical alloying parameters, such as time, ball to powder ratio (BPR) and speed of ball mill were used as the inputs and Vickers microhardness as the output of the model. Effective parameters in training such as learning rate, hidden layers and number of neurons, were determined by trail and error due to amount and percentage of errors. Regression analysis in train, validation and test stages; and mean squared error were used to verify the performance of neural network. Average error of predicted results was 2.67% or 2.25 Vickers. Also mean squared error for validation data was 7.76. As can be expected, ANN methods reduce the expenses of experimental investigations, by predicting the optimum parameters.

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This paper focused on the vehicle path planning in the highways and complex urban environments. At first, obstacles and road lines have been detected by sensors of the intelligent vehicle, thereupon the vehicle will be find the safe areas using the time distance method developed in this paper. Then, an appropriate path close to the intelligent decisions about human being would be chosen through the developed algorithm. There is the possibility of collision to surrounding vehicles in the areas where changing the lane is needed. Therefore, to prevent collision, a five orders polynomial curve is offered for each lane change maneuver. The reached maneuver is optimized based on the vehicle dynamic and allowed lateral acceleration. Finally, a suitable path to pass quite safely and without any collision through the obstacles is suggested. At the end, two main and different simulation scenarios included the lack of collision is verified by MATLAB software and the obtained path is controlled by the sliding mode controller. These simulations indicated effectiveness of this method. The lateral acceleration is obtained in allowed range for comfort of occupants in these scenarios.