Two of the most important tasks in coal mines are to improve efficiency and to increase production besides keeping safety constantly in mind.In order to obtain these goals,mine mechanization is required.Mine mechaniza...Two of the most important tasks in coal mines are to improve efficiency and to increase production besides keeping safety constantly in mind.In order to obtain these goals,mine mechanization is required.Mine mechanization needs high levels of investment and should therefore be studied carefully before final decisions about mechanization are made.When analysizing the potential for mechanization the following,rather imprecise,factors should be considered:seam inclination and thickness,geological disturbances,seam floor conditions,roof conditions,water at the working face and the extension of seams.In our study we have used fuzzy logic,membership functions and created fuzzy rule-based methods and to considered the ultimate objective:mechanization of mining.As a case study,the mechanization of the Takht coal seams in Iran was investigated.The results show a high potential for mechanization in most of the Takht coal seams.展开更多
In order to improve the efficiency of learning the triangular membership functions( TMFs) for mining fuzzy association rule( FAR) in dynamic database,a single-pass fuzzy c means( SPFCM)algorithm is combined with the r...In order to improve the efficiency of learning the triangular membership functions( TMFs) for mining fuzzy association rule( FAR) in dynamic database,a single-pass fuzzy c means( SPFCM)algorithm is combined with the real-coded CHC genetic model to incrementally learn the TMFs. The cluster centers resulting from SPFCM are regarded as the midpoint of TMFs. The population of CHC is generated randomly according to the cluster center and constraint conditions among TMFs. Then a new population for incremental learning is composed of the excellent chromosomes stored in the first genetic process and the chromosomes generated based on the cluster center adjusted by SPFCM. The experiments on real datasets show that the number of generations converging to the solution of the proposed approach is less than that of the existing batch learning approach. The quality of TMFs generated by the approach is comparable to that of the batch learning approach. Compared with the existing incremental learning strategy,the proposed approach is superior in terms of the quality of TMFs and time cost.展开更多
文摘Two of the most important tasks in coal mines are to improve efficiency and to increase production besides keeping safety constantly in mind.In order to obtain these goals,mine mechanization is required.Mine mechanization needs high levels of investment and should therefore be studied carefully before final decisions about mechanization are made.When analysizing the potential for mechanization the following,rather imprecise,factors should be considered:seam inclination and thickness,geological disturbances,seam floor conditions,roof conditions,water at the working face and the extension of seams.In our study we have used fuzzy logic,membership functions and created fuzzy rule-based methods and to considered the ultimate objective:mechanization of mining.As a case study,the mechanization of the Takht coal seams in Iran was investigated.The results show a high potential for mechanization in most of the Takht coal seams.
基金Supported by the National Natural Science Foundation of China(No.61301245,U1533104)
文摘In order to improve the efficiency of learning the triangular membership functions( TMFs) for mining fuzzy association rule( FAR) in dynamic database,a single-pass fuzzy c means( SPFCM)algorithm is combined with the real-coded CHC genetic model to incrementally learn the TMFs. The cluster centers resulting from SPFCM are regarded as the midpoint of TMFs. The population of CHC is generated randomly according to the cluster center and constraint conditions among TMFs. Then a new population for incremental learning is composed of the excellent chromosomes stored in the first genetic process and the chromosomes generated based on the cluster center adjusted by SPFCM. The experiments on real datasets show that the number of generations converging to the solution of the proposed approach is less than that of the existing batch learning approach. The quality of TMFs generated by the approach is comparable to that of the batch learning approach. Compared with the existing incremental learning strategy,the proposed approach is superior in terms of the quality of TMFs and time cost.
