Hybrid Hot Strip Rolling Force Prediction using a Bayesian Trained Artificial Neural Network and Analytical Models

Problem statement:The identification of faults in any analog circuit is highly required to ensure the reliability of the circuit. Early detection of faults in a circuit can greatly assist in maintenance of the system by avoiding possibly harmful damage borne out of the fault. Approach: A novel method for establishing a fault dictionary using Wavelet transform is presented. The Circuit Under Test (CUT) is three phase single level inverter. The transform coefficients for the fault free circuit as well as for the simulated faults of CUT are found. The Wavelet transform is applied to the output of CUT and Standard Deviation (SD) of the transform coefficients are extracted. Using the transform coefficients, fault dictionary has been formed. In order to identify the type of fault, a neural network classifier has been utilized. Results: The compatibility of wavelet analysis with the various classification techniques for fault diagnosis has been illustrated in this study. The results of the study demonstrate the suitability and viability of wavelet analysis in fault diagnosis of power electronic circuits. Conclusion: The proposed approach is found to be more reliable in accurate identification and isolation of faults using fault dictionary. Moreover, the neural classifier improves the efficiency of the system as neural networks do not require prior knowledge as they are capable of learning and evolving through a number of learning algorithms.

Section: Generalized Algorithmmentioning

confidence: 99%

Fault Detection and Classification in Power Electronic Circuits Using Wavelet Transform and Neural Network

Prasannamoorthy¹,

Devarajan²

2011

“…Some of the recent applications of Bayesian classifier are listed below. Moussaoui et al (2006) implemented Bayesian training method to construct a series of hybrid Artificial Neural Network (ANN) structures to model hot rolling force prediction from real input/output data and empirical expressions. Mazouzi and Batouche (2007) used Bayesian estimation, based on some prior assumptions on the regions for Range image segmentation.…”

Section: Unsupervisedmentioning

confidence: 99%

A New Modified Gaussian Mixture Model for Color-Texture Segmentation

Sujaritha¹,

Annadurai²

2011

Problem statement:This study presents a new, simple and efficient modified Gaussian mixture model based clustering algorithm for color-texture segmentation. The proposed mixture model introduces a new component density function which incorporates spatial information and the weighting factor for neighborhood effect is fully adaptive to the image content. Approach: It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge-blurring effect. An Expectation Maximization (EM) model fitting Maximum A Posteriori (MAP) algorithm segments the image by utilizing the pixel's color and texture features and the captured neighborhood relationships among them. Results: The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Conclusion:The experimental results with synthetic and natural images demonstrate that the proposed method is effective in improving the segmentation and it outperforms the Fusion of Clustering Results (FCR) algorithm, which is the recent popular segmentation approach.

“…G.729 (Chompun et al, 2000;2001a;2001b;Chompun, 2004;Aqel, 2006;Moussaoui et al, 2006). To verify this circumstance, this study will study and compare the performance of CS-ACELP coder according to ITU G.729 between Thai and English speeches.…”

Section: Introductionmentioning

confidence: 99%

Thai Speech Coding Based On Conjugate-Structure Algebraic Code Excited Linear Prediction Algorithm

Chomphan¹

2011

Problem statement:In mobile communication, speech coding aims at compressing the speech with lowest bitrate and highest quality for standard languages such as English, German and French. As for other languages with different uttering styles, the encoded speech quality is not guaranteed at the same bitrate. The appropriate evaluation should be performed to develop the speech quality by applying some suitable techniques. Approach: This study presents the comparison results of speech quality that is encoded and decoded by CS-ACELP coder according to ITU-G.729 standard. The purpose is to test the performance of CS-ACELP coder between Thai speech and English speech. Results: The study used 2 coding methods; (1) CS-ACELP coder without Voice Activity Detection and (2) CS-ACELP coder with Voice Activity Detection. The objective test was used to measure the speech quality for each case. The results show that both methods give Thai speech quality mostly below than English speech quality, as for methods comparison; both Thai and English, method (2) gives speech quality better than method (1). Eventually, we modified the coder by increasing the order of LP analysis to improve the Thai speech quality. Conclusion: From the finding, by no other modification, the quality of Thai coding is not equivalent to the English Language. After modifying the LP analysis by increasing the LP order from 10-12 or 14, the quality of Thai speech coding are truly improved. But the coding rate also increased for allocating the higher order information.