A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic mod...A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.展开更多
As the basal group of Polypodiales, the specific taxonomy of Dicksoniaceae is still being debated. As aquantitative analysis method, numerical taxonomy has been applied to the taxonomic study of many plant families an...As the basal group of Polypodiales, the specific taxonomy of Dicksoniaceae is still being debated. As aquantitative analysis method, numerical taxonomy has been applied to the taxonomic study of many plant families andgenera in recent years due to its simplicity and high accuracy. However, the numerical analysis of the Dicksoniaceae fossilshas not been reported at present. In the present study, the pinnule morphological data of 42 Mesozoic fossil species of theDicksoniaceae were analyzed using cluster analysis, principal component analysis and correlation analysis. The resultsrevealed that 42 taxonomic units could be divided into six representative groups, which are consistent with the traditionaltaxonomy. After screening, an identification key on 28 fossil species of four genera with a definite taxonomic position wasestablished. According to the quantitative analysis, a Bayes discriminant model was established for the selected species.Lastly, the model was tested using the morphological data of the fossil pinnules in Dicksoniaceae from the YaojieFormation, suggesting that the discriminant model is accurate to a certain extent. As a result, the numerical taxonomy canbe applied to the classification of the Dicksoniaceae fossils.展开更多
A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,t...A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.展开更多
基金Project (2010CB732004) supported by the National Basic Research Program of China
文摘A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.
基金support from the National Natural Science Foundation of China(Grant No41262001)the Science and Technology Support Fund of Gansu Province(Grant No.1104FKCA116)
文摘As the basal group of Polypodiales, the specific taxonomy of Dicksoniaceae is still being debated. As aquantitative analysis method, numerical taxonomy has been applied to the taxonomic study of many plant families andgenera in recent years due to its simplicity and high accuracy. However, the numerical analysis of the Dicksoniaceae fossilshas not been reported at present. In the present study, the pinnule morphological data of 42 Mesozoic fossil species of theDicksoniaceae were analyzed using cluster analysis, principal component analysis and correlation analysis. The resultsrevealed that 42 taxonomic units could be divided into six representative groups, which are consistent with the traditionaltaxonomy. After screening, an identification key on 28 fossil species of four genera with a definite taxonomic position wasestablished. According to the quantitative analysis, a Bayes discriminant model was established for the selected species.Lastly, the model was tested using the morphological data of the fossil pinnules in Dicksoniaceae from the YaojieFormation, suggesting that the discriminant model is accurate to a certain extent. As a result, the numerical taxonomy canbe applied to the classification of the Dicksoniaceae fossils.
基金Project(50490274)supported by the National Natural Science Foundation of China
文摘A method to forecast the over-excavation of underground opening by using the Bayes discriminant analysis(BDA)theory was presented.The Bayes discriminant analysis theory was introduced.Based on an engineering example,the factors influencing the over-excavation of underground opening were taken into account to build a forecast BDA model,and the prior information about over-excavation of underground opening was also taken into consideration.Five parameters influencing the over-excavation of opening,including 2 groups of joints,1 group of layer surface,extension and space between structure faces were selected as geometric parameters.Engineering data in an underground opening were used as the training samples.The cross-validation method was introduced to verify the stability of BDA model and the ratio of mistake-discrimination was equal to zero after the BDA model was trained.Data in an underground engineering were used to test the discriminant ability of BDA model.The results show that five forecast results are identical with the actual situation and BDA can be used in practical engineering.
