In sports timing systems,P2P communication is used at low frequency bandwidths(under 135 KHz) between tags and readers in the RFID field.However,in such cases,collisions tend to occur when a reader deals with multiple...In sports timing systems,P2P communication is used at low frequency bandwidths(under 135 KHz) between tags and readers in the RFID field.However,in such cases,collisions tend to occur when a reader deals with multiple RFID tags simultaneously.To overcome this issue,a sports timing system including a Multi Reader Controller(MRC)loaded with an advanced multiple reader algorithm and application was created and applied at large-scale citizens' marathon events.In these cases,a large number of people pass over the installed urethane type's antenna mat continually during a short period of time.This study verified the superiority of the improved algorithm and application through the on-thespot application of the multi reader algorithm and application program,which allows us to smoothly measure runners' times through multiaccess reading for rapid collision avoidance.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
文摘In sports timing systems,P2P communication is used at low frequency bandwidths(under 135 KHz) between tags and readers in the RFID field.However,in such cases,collisions tend to occur when a reader deals with multiple RFID tags simultaneously.To overcome this issue,a sports timing system including a Multi Reader Controller(MRC)loaded with an advanced multiple reader algorithm and application was created and applied at large-scale citizens' marathon events.In these cases,a large number of people pass over the installed urethane type's antenna mat continually during a short period of time.This study verified the superiority of the improved algorithm and application through the on-thespot application of the multi reader algorithm and application program,which allows us to smoothly measure runners' times through multiaccess reading for rapid collision avoidance.
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%.
