A framework of real time face tracking and recognition is presented, which integrates skin color based tracking and PCA/BPNN (principle component analysis/back propagation neural network) hybrid recognition techni...A framework of real time face tracking and recognition is presented, which integrates skin color based tracking and PCA/BPNN (principle component analysis/back propagation neural network) hybrid recognition techniques. The algorithm is able to track the human face against a complex background and also works well when temporary occlusion occurs. We also obtain a very high recognition rate by averaging a number of samples over a long image sequence. The proposed approach has been successfully tested by many experiments, and can operate at 20 frames/s on an 800 MHz PC.展开更多
Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathem...Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.展开更多
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
One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire min...One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.展开更多
Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used ...Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.展开更多
文摘A framework of real time face tracking and recognition is presented, which integrates skin color based tracking and PCA/BPNN (principle component analysis/back propagation neural network) hybrid recognition techniques. The algorithm is able to track the human face against a complex background and also works well when temporary occlusion occurs. We also obtain a very high recognition rate by averaging a number of samples over a long image sequence. The proposed approach has been successfully tested by many experiments, and can operate at 20 frames/s on an 800 MHz PC.
文摘Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.
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One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.
文摘Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.
