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Rotating machines in particular induction electrical machines are important industry instruments. In manufacturing, electrical motors are exposed to many damages, and this causes stators and rotors not to work correctly. In this paper we addressed modal analysis and an intelligent method to detect motor load condition and also the stator faults such as turn-to-turn and coil-to-coil faults using motor vibration analysis. A three-phase induction motor with a special winding was used to create the faults artificially. The vibration signal of motor in different states such as working without fault, with various faults and with various loads was acquired. Some spectral analysis was done using the spectrum and the spectrograph of vibration signals and differences due to different states of motor were observed. Suitable features such as Linear Prediction Cepstral Coefficients and Fourier Transform Filter Bank Coefficients were extracted from vibration signals and were then applied to non-supervised (SOM) and supervised (LVQ) neural networks in order to classify motor faults and its load condition. Many experiments were conducted to evaluate the effect of neural network type, type and length of feature vector, length of training signal etc. In brief, using SOM and LVQ neural networks, 20 element Filter Bank feature vectors, and 600ms of the training data, performance of 93.6% and 94.2% were obtained for load and fault detection respectively.

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A parallel hybrid system of HMM and GMM modeling techniques was implemented and used in a telephony speaker verification and identification system. Spectral subtraction and Weighted Projection Measure were used to render this system more robust against additional noise. Cepstral Mean Subtraction method was also applied for the compensation of convolution noise due to transmission channel degradation and differences in the frequency response of telephone handsets. For a population of 100 speakers of FARSDIGITS1 database with a SNR of 8.8 dB, a speaker identification performance of 95.51% and a speaker verification error rate of 0.37% were obtained. Several score normalization methods in utterance and frame level and weighting of model scores were also implemented, and then compared and evaluated. It was shown that these methods improve discrimination between speakers and yield a reduction of speaker verification and identification error rates.

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In this paper, genetic programming is applied for quality improvement of noisy speech signal. Therefore, a system including both spectral subtraction and genetic programming is implemented for speech enhancement. In the proposed method, first noise is reduced by spectral subtraction. In the next step, genetic programming trees are trained for more enhancement of noisy signal by mapping the signal obtained by spectral subtraction to clean data. The proposed hybrid method improves signal to noise ratio about 2 to 6.5 dB. Comparison of genetic programming, multi-layer perceptron neural network, spectral subtraction, and the proposed hybrid method for speech enhancement indicates that the combination of spectral subtraction and genetic programming presents much better quality for enhanced signal compared to the other methods studied in this paper.

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Static balancing is one of the most valuable strategies in manufacturing and industrial designing. This paper deals with the static balancing of parallel mechanisms. Using counter-weights and springs, and their combination, are the most popular methods in this procedure. In this article, theories and formulas of static balancing, by considering the end-effector with constant-weight, using counter-weights and springs are addressed. As case studies, three 3-DOF planar parallel mechanisms, namely, 3-RRR, 3-PRR and 3-RPR with constant-weight are investigated. A static balanced 3-RRR is modeled and validated in Adams software and fabricated using a combination of spring and counter-weight. This mechanism is manufactured in Human and Robot Interaction laboratory (TaarLab). Moreover, a cable parallel 3-DOF mechanism using static balancing concept is designed for which variable weight is considered at the end-effector. The crane benefits from static balancing of variable weight that causes the power actuators just use in relocation the counter-weight in XY plane that is obviously less than the power needed to relocate the main load across the gravity direction. The advantages of these kinds of mechanisms consist in reducing manufacturing and operation price, increasing the safety and using less power in actuators.