The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability ...The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.展开更多
Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play...Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.展开更多
The morphological phylogeny of the water snake subfamily Homalopsinae, containing 10 genera, of which seven are monotypic, was not reported up until now. Here fourteen morphological characters were selected for the cl...The morphological phylogeny of the water snake subfamily Homalopsinae, containing 10 genera, of which seven are monotypic, was not reported up until now. Here fourteen morphological characters were selected for the cladistic analysis. Using software Hennig 86, two phylogenetic trees were inferred and the results showed that the subfamily Homalopsinae was divided into two groups. Compared with the molecular phylonenetic tree of Voris et al （2002）, the genera Gerarda and Fordonia are sister groups in both studies; both studies also yielded the same monophyletic lineage, which contained three genera （Cerberus ＋ Erpeton ＋ Homalopsis ）. However, the position of the genus Cantoria is distinctly different with the study of Voris et a1（2002）.展开更多
In the present paper,the cladistics method is used to analyze the phylogenetic relationship of 16 genera of Tinginae (Hemiptera:Heteroptera:Tingidae) from northern China. 33 characters with 88 states were chosen based...In the present paper,the cladistics method is used to analyze the phylogenetic relationship of 16 genera of Tinginae (Hemiptera:Heteroptera:Tingidae) from northern China. 33 characters with 88 states were chosen based on the morphological comparison. The character matrix was formed through ingroup and outgroup analysis and computed using the computer programs MacClad (version 3 0 1), AutoDecay (version 3 0), PAUP (version 3 1 1) and Hennig 86 (version 1 5),and the same most parsimonious tree and Nelson consensus cladogram were obtained from both computation to explain the phylogenetic relationship among the genera (L=118,CI=0 454,RI=0 529). We categorized these genera involved in the analysis phylogenetically into 6 groups according to the results. They are Agramma group,Leptoypha group,Dictyla group,Catoplatus group,Physatocheila group and Derephysia group. The phylogenetic relationship among the involved genera is presented as (((((Derephysia group=( Lasiacantha ,( Acalypta ,( Galeatus ,( Dictyonota ,( Derephysia,Sphaerista ))))),Physatocheila group =(( Elasmotropis,Tingis ),(( Oncochila,Cochlochila ), Physatocheila ))),Catoplatus group= Catoplatus ),Dictyla group=( Monosteira,Dictyla )),Leptoypha group= Leptoypha ),Agramma group= Agramma ). These groups will not be regarded as higher classific taxa until more genera and data are available and further analysis are carried out.展开更多
The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vec...The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vector machine(SVM)and generalized regression neural network(GRNN)were used to find the relationship among rock cuttability,uniaxial confining stress applied to rock,uniaxial compressive strength(UCS)and tensile strength of rock material.It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability,which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material.Based on prediction models for revealing the optimal stress condition and determining the cutting parameters,the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.展开更多
Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault sampl...Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.展开更多
Based on nonlinear failure criterion,a three-dimensional failure mechanism of the possible collapse of deep tunnel is presented with limit analysis theory.Support pressure is taken into consideration in the virtual wo...Based on nonlinear failure criterion,a three-dimensional failure mechanism of the possible collapse of deep tunnel is presented with limit analysis theory.Support pressure is taken into consideration in the virtual work equation performed under the upper bound theorem.It is necessary to point out that the properties of surrounding rock mass plays a vital role in the shape of collapsing rock mass.The first order reliability method and Monte Carlo simulation method are then employed to analyze the stability of presented mechanism.Different rock parameters are considered random variables to value the corresponding reliability index with an increasing applied support pressure.The reliability indexes calculated by two methods are in good agreement.Sensitivity analysis was performed and the influence of coefficient variation of rock parameters was discussed.It is shown that the tensile strength plays a much more important role in reliability index than dimensionless parameter,and that small changes occurring in the coefficient of variation would make great influence of reliability index.Thus,significant attention should be paid to the properties of surrounding rock mass and the applied support pressure to maintain the stability of tunnel can be determined for a given reliability index.展开更多
Monitoring indoor harmful gas can obtain the infrared spectra of mixed harmful gases.Since the absorption bands of mixed gases overlap and their qualitative and quantitative analyses are not easy,feature extraction me...Monitoring indoor harmful gas can obtain the infrared spectra of mixed harmful gases.Since the absorption bands of mixed gases overlap and their qualitative and quantitative analyses are not easy,feature extraction method based on joint approximative diagonalization of eigenmatrix（JADE）is proposed.By fully mining the hidden information of original data and analyzing higher-order statistics information of the data,each substance spectrum in the mixed gas can be accurately distinguished.In addition,a multi-dimensional data quantitative analysis model of the extracted independent source is established by using support vector machine（SVM）based on regular theory.The experimental results show that the correlation coefficients of the components of mixed gases is above 0.999 1by quantitative analysis,which verifies the accuracy of this feature extraction method.展开更多
On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process m...On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.展开更多
Design of rib support systems in U.S. coal mines is based primarily on local practices and experience. A better understanding of current rib support practices in U.S. coal mines is crucial for developing a sound engin...Design of rib support systems in U.S. coal mines is based primarily on local practices and experience. A better understanding of current rib support practices in U.S. coal mines is crucial for developing a sound engineering rib support design tool. The objective of this paper is to analyze the current practices of rib control in U.S. coal mines. Twenty underground coal mines were studied representing various coal basins,coal seams,geology,loading conditions,and rib control strategies. The key findings are:(1) any rib design guideline or tool should take into account external rib support as well as internal bolting;(2) rib bolts on their own cannot contain rib spall,especially in soft ribs subjected to significant load—external rib control devices such as mesh are required in such cases to contain rib sloughing;(3) the majority of the studied mines follow the overburden depth and entry height thresholds recommended by the Program Information Bulletin 11-29 issued by the Mine Safety and Health Administration;(4) potential rib instability occurred when certain geological features prevailed—these include draw slate and/or bone coal near the rib/roof line,claystone partings,and soft coal bench overlain by rock strata;(5) 47% of the studied rib spall was classified as blocky—this could indicate a high potential of rib hazards; and(6) rib injury rates of the studied mines for the last three years emphasize the need for more rib control management for mines operating at overburden depths between 152.4 m and 304.8 m.展开更多
基金Project (50934006) supported by the National Natural Science Foundation of ChinaProject (2010CB732004) supported by the National Basic Research Program of ChinaProject (CX2011B119) supported by the Graduated Students’ Research and Innovation Fund Project of Hunan Province of China
文摘The purpose of this study is to apply some statistical and soft computing methods such as Fisher discriminant analysis （FDA） and support vector machines （SVMs） methodology to the determination of pillar stability for underground mines selected from various coal and stone mines by using some index and mechanical properties, including the width, the height, the ratio of the pillar width to its height, the uniaxial compressive strength of the rock and pillar stress. The study includes four main stages： sampling, testing, modeling and assessment of the model performances. During the modeling stage, two pillar stability prediction models were investigated with FDA and SVMs methodology based on the statistical learning theory. After using 40 sets of measured data in various mines in the world for training and testing, the model was applied to other 6 data for validating the trained proposed models. The prediction results of SVMs were compared with those of FDA as well as the measured field values. The general performance of models developed in this study is close; however, the SVMs exhibit the best performance considering the performance index with the correct classification rate Prs by re-substitution method and Pcv by cross validation method. The results show that the SVMs approach has the potential to be a reliable and practical tool for determination of pillar stability for underground mines.
基金sponsored by the National Science and Technology Major Project(No.2011ZX05023-005-006)
文摘Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.
文摘The morphological phylogeny of the water snake subfamily Homalopsinae, containing 10 genera, of which seven are monotypic, was not reported up until now. Here fourteen morphological characters were selected for the cladistic analysis. Using software Hennig 86, two phylogenetic trees were inferred and the results showed that the subfamily Homalopsinae was divided into two groups. Compared with the molecular phylonenetic tree of Voris et al （2002）, the genera Gerarda and Fordonia are sister groups in both studies; both studies also yielded the same monophyletic lineage, which contained three genera （Cerberus ＋ Erpeton ＋ Homalopsis ）. However, the position of the genus Cantoria is distinctly different with the study of Voris et a1（2002）.
文摘In the present paper,the cladistics method is used to analyze the phylogenetic relationship of 16 genera of Tinginae (Hemiptera:Heteroptera:Tingidae) from northern China. 33 characters with 88 states were chosen based on the morphological comparison. The character matrix was formed through ingroup and outgroup analysis and computed using the computer programs MacClad (version 3 0 1), AutoDecay (version 3 0), PAUP (version 3 1 1) and Hennig 86 (version 1 5),and the same most parsimonious tree and Nelson consensus cladogram were obtained from both computation to explain the phylogenetic relationship among the genera (L=118,CI=0 454,RI=0 529). We categorized these genera involved in the analysis phylogenetically into 6 groups according to the results. They are Agramma group,Leptoypha group,Dictyla group,Catoplatus group,Physatocheila group and Derephysia group. The phylogenetic relationship among the involved genera is presented as (((((Derephysia group=( Lasiacantha ,( Acalypta ,( Galeatus ,( Dictyonota ,( Derephysia,Sphaerista ))))),Physatocheila group =(( Elasmotropis,Tingis ),(( Oncochila,Cochlochila ), Physatocheila ))),Catoplatus group= Catoplatus ),Dictyla group=( Monosteira,Dictyla )),Leptoypha group= Leptoypha ),Agramma group= Agramma ). These groups will not be regarded as higher classific taxa until more genera and data are available and further analysis are carried out.
基金financial supports from the National Natural Science Foundation of China(Nos.51904333,51774326)。
文摘The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength.Based on the test results,the regression analyses,support vector machine(SVM)and generalized regression neural network(GRNN)were used to find the relationship among rock cuttability,uniaxial confining stress applied to rock,uniaxial compressive strength(UCS)and tensile strength of rock material.It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability,which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material.Based on prediction models for revealing the optimal stress condition and determining the cutting parameters,the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.
文摘Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.
基金Project (2013CB036004) supported by National Basic Research Program of China
文摘Based on nonlinear failure criterion,a three-dimensional failure mechanism of the possible collapse of deep tunnel is presented with limit analysis theory.Support pressure is taken into consideration in the virtual work equation performed under the upper bound theorem.It is necessary to point out that the properties of surrounding rock mass plays a vital role in the shape of collapsing rock mass.The first order reliability method and Monte Carlo simulation method are then employed to analyze the stability of presented mechanism.Different rock parameters are considered random variables to value the corresponding reliability index with an increasing applied support pressure.The reliability indexes calculated by two methods are in good agreement.Sensitivity analysis was performed and the influence of coefficient variation of rock parameters was discussed.It is shown that the tensile strength plays a much more important role in reliability index than dimensionless parameter,and that small changes occurring in the coefficient of variation would make great influence of reliability index.Thus,significant attention should be paid to the properties of surrounding rock mass and the applied support pressure to maintain the stability of tunnel can be determined for a given reliability index.
基金National Natural Science Foundation of China(No.61127015)
文摘Monitoring indoor harmful gas can obtain the infrared spectra of mixed harmful gases.Since the absorption bands of mixed gases overlap and their qualitative and quantitative analyses are not easy,feature extraction method based on joint approximative diagonalization of eigenmatrix（JADE）is proposed.By fully mining the hidden information of original data and analyzing higher-order statistics information of the data,each substance spectrum in the mixed gas can be accurately distinguished.In addition,a multi-dimensional data quantitative analysis model of the extracted independent source is established by using support vector machine（SVM）based on regular theory.The experimental results show that the correlation coefficients of the components of mixed gases is above 0.999 1by quantitative analysis,which verifies the accuracy of this feature extraction method.
文摘On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study. Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.
文摘Design of rib support systems in U.S. coal mines is based primarily on local practices and experience. A better understanding of current rib support practices in U.S. coal mines is crucial for developing a sound engineering rib support design tool. The objective of this paper is to analyze the current practices of rib control in U.S. coal mines. Twenty underground coal mines were studied representing various coal basins,coal seams,geology,loading conditions,and rib control strategies. The key findings are:(1) any rib design guideline or tool should take into account external rib support as well as internal bolting;(2) rib bolts on their own cannot contain rib spall,especially in soft ribs subjected to significant load—external rib control devices such as mesh are required in such cases to contain rib sloughing;(3) the majority of the studied mines follow the overburden depth and entry height thresholds recommended by the Program Information Bulletin 11-29 issued by the Mine Safety and Health Administration;(4) potential rib instability occurred when certain geological features prevailed—these include draw slate and/or bone coal near the rib/roof line,claystone partings,and soft coal bench overlain by rock strata;(5) 47% of the studied rib spall was classified as blocky—this could indicate a high potential of rib hazards; and(6) rib injury rates of the studied mines for the last three years emphasize the need for more rib control management for mines operating at overburden depths between 152.4 m and 304.8 m.
