Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing...Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.展开更多
This research proposes and implements an Arabic Sub-Words Recognition System （ASWR）. The system focuses on employing a combination of statistical and structural features to provide complete pattern＇s description an...This research proposes and implements an Arabic Sub-Words Recognition System （ASWR）. The system focuses on employing a combination of statistical and structural features to provide complete pattern＇s description and enhances the recognition rate. Support Vector Machines （SVMs） is utilized as a promising pattern recognition tool. In addition to that, the problems of dots and holes are solved in a completely different way from the ones previously employed. The proposed system proceeds in several phases as follows： （1） image acquisition, （2） binarisation, （3） morphological processing, （4） feature extraction, which includes statistical features, i.e., moment invariants, and structural features, i.e., dot number, dot position, and number of holes, features, and （5） classification, using multi-class SVMs and applying a one-against-all technique. The proposed system has been tested using different sets of words and subwords and has achieved a nearly 98.90% recogiaition rate. Comparative results with NNs are also presented.展开更多
文摘Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.
文摘This research proposes and implements an Arabic Sub-Words Recognition System （ASWR）. The system focuses on employing a combination of statistical and structural features to provide complete pattern＇s description and enhances the recognition rate. Support Vector Machines （SVMs） is utilized as a promising pattern recognition tool. In addition to that, the problems of dots and holes are solved in a completely different way from the ones previously employed. The proposed system proceeds in several phases as follows： （1） image acquisition, （2） binarisation, （3） morphological processing, （4） feature extraction, which includes statistical features, i.e., moment invariants, and structural features, i.e., dot number, dot position, and number of holes, features, and （5） classification, using multi-class SVMs and applying a one-against-all technique. The proposed system has been tested using different sets of words and subwords and has achieved a nearly 98.90% recogiaition rate. Comparative results with NNs are also presented.
