The coal-gas existing condition was ameliorated in the coal seams prone to coal-gas outburst adopting the mining method of protective strata.The gas volume and the gas pressure were reduced synchronously in the protec...The coal-gas existing condition was ameliorated in the coal seams prone to coal-gas outburst adopting the mining method of protective strata.The gas volume and the gas pressure were reduced synchronously in the protected coal seam,and the coal seam of high permeability prone to the coal-gas outburst was changed into that of low perme- ability with no proneness to the coal-gas outburst.The D_(15)coal seam was treated as the protective strata,and the D_(16-17)coal seam was treated as the protected strata in the Fifth coal mine in the Pingdingshan Coal Mining Group.The distance between the two coal seams was 5 m averagely,clarified into the extreme short-range protective strata.The numerical analysis was based on the theory of the porous media flow with the finite ele- ment method.The gas flow process and the change mechanism of the coal-gas pressure were analyzed in the process of mining the protective strata.展开更多
Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ...Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.展开更多
A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking in...A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.展开更多
基金the Grants of National Scientific Funds of Control Mechanism of Geologic Hazards Induced by Coal-gas(50534070)
文摘The coal-gas existing condition was ameliorated in the coal seams prone to coal-gas outburst adopting the mining method of protective strata.The gas volume and the gas pressure were reduced synchronously in the protected coal seam,and the coal seam of high permeability prone to the coal-gas outburst was changed into that of low perme- ability with no proneness to the coal-gas outburst.The D_(15)coal seam was treated as the protective strata,and the D_(16-17)coal seam was treated as the protected strata in the Fifth coal mine in the Pingdingshan Coal Mining Group.The distance between the two coal seams was 5 m averagely,clarified into the extreme short-range protective strata.The numerical analysis was based on the theory of the porous media flow with the finite ele- ment method.The gas flow process and the change mechanism of the coal-gas pressure were analyzed in the process of mining the protective strata.
基金supported by the Science and Technology Plan Projects of Sichuan Province of China under Grant No.2008GZ0003the Key Technologies R & D Program of Sichuan Province of China under Grant No.2008SZ0100
文摘Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
文摘A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.
