In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases More
In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases of writing variations alteration like scaled or rotated handwritings. Also using the log-polar transform, the sub-word image sampling will be performed so that most of acquired samples will be centered in a certain area. The proposed method uses the discrete Hidden Markov’s Model (HMM) as a classifier. Furthermore a net of dictionaries were employed to increase the reliability and precision of the system output. Finally, the Iran-Shahr database is utilized to evaluate the system performance. Comparing the results of the proposed method and other previous methods, proves that a less sensitivity has been achieved by the proposed method about handwriting variations.
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One of the most common electrical faults in Permanent Magnet Synchronous Motor (PMSM) is inter-turn fault in stator winding. At the incipient steps it seems not dangerous and so light, but spreading this fault can leads to irreparable Consequences. In this paper, the in More
One of the most common electrical faults in Permanent Magnet Synchronous Motor (PMSM) is inter-turn fault in stator winding. At the incipient steps it seems not dangerous and so light, but spreading this fault can leads to irreparable Consequences. In this paper, the intelligent system is presented to protect PMSMs from this kind fault. At the first, intelligent protection system determine the condition of the motor (which can be: Normal, Phase-phase short circuit, Open circuit and Inter-turn fault conditions). If the system determines the faults then send an alarm to operator and also if the fault is inter-turn, it can determine the damaged phase. Obtaining results show that Probabilistic Neural Network can be the most reliable and robust protection system for PMSMs against internal faults, especially inter-turn faults.
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In this paper an automated system based on feature extraction of new techniques is presented to detect the gender from the scanned images (off-line) handwriting samples. In order to show the difference between examples of handwriting, in the first step Radon transform i More
In this paper an automated system based on feature extraction of new techniques is presented to detect the gender from the scanned images (off-line) handwriting samples. In order to show the difference between examples of handwriting, in the first step Radon transform is taken from the handwritten image, and then each handwriting sample features are extracted using symbolic dynamic filtering. Training and classification of extracted features from the samples are carried out by the multi-layer perceptron neural network. At the end, to determine the effectiveness of the proposed method, experiments are carried out on the Multi Script Handwritten Database (MSHD). In addition, two new challenges of text and script-independent gender detection are explored. Experiences show that the proposed method improves the detection rate compared to the previous works such as fractals, chain codes and textures. The best detection rate is able to achieve accuracy of 84.9% in experiences.
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