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Go to Editorial ManagerThis paper presents a new design to implement DFT/IDFT using the two components of a sequence, which are even and odd component sequences to solve the complexity of complex multiplications and reduce the number of multipliers. The proposed two implementations reduce the number of real multipliers needed to compute the DFT. The first proposed design gives good results for $N 512$ as compared to conventional FFT algorithm, while the second scenario gives good results for $N 1024$ as compared to conventional FFT algorithm. The proposed design is performed directly from real and imaginary part equations of the DFT sequence $X[k]$ without additional processing.
The paper deals with neural networks identification of ultimate moment capacity of steel-concrete composite beams on base of experimental results. Basic information on artificial neural networks and its parameters suitable for analysis of experimental results are given. Two types of neural network algorithms are used. Results of identification are reported. The results show that artificial neural networks are highly suitable for assessing the ultimate moment capacity of composite section. The proposed neural network was also used to explore the effect of the various parameters on the behaviour of composite beams.
Artificial Neural Networks (ANN) have been applied to structural engineering in recent years. Most of the researches are based on backpropagation neural networks due to its well-studied theory. A backpropagation neural network has been used to predict the ultimate torsional strength of reinforced concrete rectangular beams. The effects of the parameters, such as the number of nodes in the input, output and hidden layers and the pre-process of the training patterns, on the behaviour of the neural network have been investigated. The algorithm called 'resilient propagation algorithm' has been used to the performance of the neural network. After training, the generalization of the neural network was tested by the patterns not included in the training patterns. Once the neural network has been trained, the ultimate torsional strength of reinforced concrete is obtained very easily and efficiently. Based on the ANN results, a parametric analysis was carried out to study the influence of parameters affecting the ultimate torsional strength of reinforced concrete beams and these results are compared with the equations of ACI-code.
In this paper induction motor and its direct torque control are simulated and a speed estimator scheme based on wavenet (WN) theory has been developed and compared with the actual speed. The wavenet speed estimator inputs are a single line current and the state of the torque comparator output which are trained to follow the relationship between the motor current and the rotor speed. To ensure the validity of this scheme, the estimated speed is compared with a speed estimated from a conventional model reference adaptive system (MRAS). The operation of direct torque control (DTC) drive with the actual speed and the estimated wavenet speed as a feedback signal are simulated and compared. The results show that the wavenet method is effective for rotor speed estimation.