Backfill hydraulic support is the key equipment in achieving coal mining and solid backfilling simultaneously in solid backfill mining technology.Based on the summary and analysis of main types,basic structural proper...Backfill hydraulic support is the key equipment in achieving coal mining and solid backfilling simultaneously in solid backfill mining technology.Based on the summary and analysis of main types,basic structural properties and filed application of backfill hydraulic support,this work has firstly proposed the basic principle of backfill hydraulic support optimization design and provided the method of optimal design of key structural components,like four-bar linkage,rear canopy and tamping structure;the method is further elaborated as changing hinging position of upper bar to optimize four-bar linkage,by lengthening or shortening the rear canopy to optimize length ratio of canopy;and by changing length and hinging position of tamping structure as well as suspension height of backfill scrape conveyor to realize optimization of tamping structure.On this basis,the process of optimal design of backfill hydraulic support is built.The optimal design case of ZC5200/14.5/30 six columns-four bar linkage used in 7203 W workface of Zhaizhen Coal Mine shows that the backfill properties like horizontal roof gap,vertical horizontal gap,tamping angle and tamping head gap are improved obviously through optimizing four-bar linkage,canopy length and tamping structure according to the optimal design method proposed in this work.展开更多
This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using...This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.展开更多
基金Project(2017QNA21)supported by the Fundamental Research Funds for the Central Universities of ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China
文摘Backfill hydraulic support is the key equipment in achieving coal mining and solid backfilling simultaneously in solid backfill mining technology.Based on the summary and analysis of main types,basic structural properties and filed application of backfill hydraulic support,this work has firstly proposed the basic principle of backfill hydraulic support optimization design and provided the method of optimal design of key structural components,like four-bar linkage,rear canopy and tamping structure;the method is further elaborated as changing hinging position of upper bar to optimize four-bar linkage,by lengthening or shortening the rear canopy to optimize length ratio of canopy;and by changing length and hinging position of tamping structure as well as suspension height of backfill scrape conveyor to realize optimization of tamping structure.On this basis,the process of optimal design of backfill hydraulic support is built.The optimal design case of ZC5200/14.5/30 six columns-four bar linkage used in 7203 W workface of Zhaizhen Coal Mine shows that the backfill properties like horizontal roof gap,vertical horizontal gap,tamping angle and tamping head gap are improved obviously through optimizing four-bar linkage,canopy length and tamping structure according to the optimal design method proposed in this work.
基金Project(BK20160685)supported by the Science Foundation of Jiangsu Province,ChinaProject(61620106002)supported by the National Natural Science Foundation of China
文摘This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.
