In order to improve the rutting resistance of asphalt mixtures in the middle course to reduce the rutting of asphalt pavement, the influence of different types of gradation with their own optimal asphaltaggregate rati...In order to improve the rutting resistance of asphalt mixtures in the middle course to reduce the rutting of asphalt pavement, the influence of different types of gradation with their own optimal asphaltaggregate ratios is analyzed. Some investigations are made out on the mixture in the middle course through the modified wheel tracking test in air bath and the Hamburg wheel tracking test （HWTT） in water bath, and the results of which are compared with the corresponding research in Germany. Results show that the Sup20 and the modified AC-20I have better performance than that of AC-20I under the same test conditions. In addition, the high-quality bitumen and hard aggregate can improve the rutting performance of the mixture in water-submerged conditions. The selection of modified asphalt, hard aggregate and a reasonable gradation are essential to the improvement of the rutting resistance of the mixtures used in the middle course.展开更多
Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping...Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.展开更多
The theory and method of extenics were applied to establish classical field matterelements and segment field matter elements for coal and gas outburst.A matter-element model for prediction was established based on fiv...The theory and method of extenics were applied to establish classical field matterelements and segment field matter elements for coal and gas outburst.A matter-element model for prediction was established based on five matter-elements,which includedgas pressure,types of coal damage,coal rigidity,initial speed of methane diffusionand in-situ stress.Each index weight was given fairly and quickly through the improvedanalytic hierarchy process,which need not carry on consistency checks,so accuracy ofassessment can be improved.展开更多
文摘In order to improve the rutting resistance of asphalt mixtures in the middle course to reduce the rutting of asphalt pavement, the influence of different types of gradation with their own optimal asphaltaggregate ratios is analyzed. Some investigations are made out on the mixture in the middle course through the modified wheel tracking test in air bath and the Hamburg wheel tracking test （HWTT） in water bath, and the results of which are compared with the corresponding research in Germany. Results show that the Sup20 and the modified AC-20I have better performance than that of AC-20I under the same test conditions. In addition, the high-quality bitumen and hard aggregate can improve the rutting performance of the mixture in water-submerged conditions. The selection of modified asphalt, hard aggregate and a reasonable gradation are essential to the improvement of the rutting resistance of the mixtures used in the middle course.
基金Supported by the National Natural Science Foundation of China(61074153)
文摘Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problem of reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the ill-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey–Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data.The final prediction correct rate reaches 81%.
基金Supported by the National Natural Science Foundation of China(50534080)the Science and Technology Research Project of Chongqing(CSCT,2006AA7002)
文摘The theory and method of extenics were applied to establish classical field matterelements and segment field matter elements for coal and gas outburst.A matter-element model for prediction was established based on five matter-elements,which includedgas pressure,types of coal damage,coal rigidity,initial speed of methane diffusionand in-situ stress.Each index weight was given fairly and quickly through the improvedanalytic hierarchy process,which need not carry on consistency checks,so accuracy ofassessment can be improved.
