The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) m...The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.展开更多
The remain passenger problem at subway station platform was defined initially,and the period variation of remain passenger queues at platform was investigated through arriving and boarding analyses.Taking remain passe...The remain passenger problem at subway station platform was defined initially,and the period variation of remain passenger queues at platform was investigated through arriving and boarding analyses.Taking remain passenger queues at platform as dynamic stochastic process,a new probabilistic queuing method was developed based on probabilistic theory and discrete time Markov chain theory.This model can calculate remain passenger queues while considering different directions.Considering the stable or variable train arriving period and different platform crossing types,a series of model deformation research was carried out.The probabilistic approach allows to capture the cyclic behavior of queues,measures the uncertainty of a queue state prediction by computing the evolution of its probability in time,and gives any temporal distribution of the arrivals.Compared with the actual data,the deviation of experimental results is less than 20%,which shows the efficiency of probabilistic approach clearly.展开更多
As known to all that Henon chaotic system is not appropriate for generating the key-streams because it has non-uniformly distributed output signal, a new key-stream generation scheme based on Henon chaotic system is p...As known to all that Henon chaotic system is not appropriate for generating the key-streams because it has non-uniformly distributed output signal, a new key-stream generation scheme based on Henon chaotic system is presented. In order to get the key-streams with good statistics and long enough cycle length, a specific method for dividing the enon attractor into numerous non-overlapping sub-regions, and a new one-to-one mapping strategy between the divided sub-regions and elements of dynamically generated matrix consisting of O＇s and l＇s are proposed. Experimental results demonstrate that the generated key-streams are with long enough cycle length and very sensitive to the initial values and secret keys. For example, key-streams with the cycle length of 10^32 can easily be obtained. Moreover, even if the fluctuation to the initial values or secret keys is as small as 10^- 14 uncorrelated key-streams will be generated. Experimental results also demonstrate that the generated key-streams have good randomness and they can pass all the standard criteria specified in FIPS PUB 140^-2 with no less than 98%.展开更多
文摘The grey forecasting model has been successfully applied to many fields. However, the precision of GM(1,1) model is not high. In order to remove the seasonal fluctuations in monitoring series before building GM(1,1) model, the forecasting series of GM(1,1) was built, and an inverse process was used to resume the seasonal fluctuations. Two deseasonalization methods were presented , i.e., seasonal index-based deseasonalization and standard normal distribution-based deseasonalization. They were combined with the GM(1,1) model to form hybrid grey models. A simple but practical method to further improve the forecasting results was also suggested. For comparison, a conventional periodic function model was investigated. The concept and algorithms were tested with four years monthly monitoring data. The results show that on the whole the seasonal index-GM(1,1) model outperform the conventional periodic function model and the conventional periodic function model outperform the SND-GM(1,1) model. The mean Absolute error and mean square error of seasonal index-GM(1,1) are 30.69% and 54.53% smaller than that of conventional periodic function model, respectively. The high accuracy, straightforward and easy implementation natures of the proposed hybrid seasonal index-grey model make it a powerful analysis technique for seasonal monitoring series.
基金Project(2011BAG01B01) supported by the Major State Basic Research and Development Program of ChinaProject(RCS2012ZZ002) supported by the State Key Lab of Rail Traffic Control and Safety,China
文摘The remain passenger problem at subway station platform was defined initially,and the period variation of remain passenger queues at platform was investigated through arriving and boarding analyses.Taking remain passenger queues at platform as dynamic stochastic process,a new probabilistic queuing method was developed based on probabilistic theory and discrete time Markov chain theory.This model can calculate remain passenger queues while considering different directions.Considering the stable or variable train arriving period and different platform crossing types,a series of model deformation research was carried out.The probabilistic approach allows to capture the cyclic behavior of queues,measures the uncertainty of a queue state prediction by computing the evolution of its probability in time,and gives any temporal distribution of the arrivals.Compared with the actual data,the deviation of experimental results is less than 20%,which shows the efficiency of probabilistic approach clearly.
基金Foundation item： Proj ects（61172184, 61173147） supported by the National Natural Science Foundation of China Project（12JJ6062） supported by Natural Science Foundation of Hunan Province, China+1 种基金 Project（121gpy31） supported by the Fundamental Research Funds for the Central Universities of China Project supported by the State Key Laboratory of Information Security （Institute of Software, Chinese Academy of Sciences）, China
文摘As known to all that Henon chaotic system is not appropriate for generating the key-streams because it has non-uniformly distributed output signal, a new key-stream generation scheme based on Henon chaotic system is presented. In order to get the key-streams with good statistics and long enough cycle length, a specific method for dividing the enon attractor into numerous non-overlapping sub-regions, and a new one-to-one mapping strategy between the divided sub-regions and elements of dynamically generated matrix consisting of O＇s and l＇s are proposed. Experimental results demonstrate that the generated key-streams are with long enough cycle length and very sensitive to the initial values and secret keys. For example, key-streams with the cycle length of 10^32 can easily be obtained. Moreover, even if the fluctuation to the initial values or secret keys is as small as 10^- 14 uncorrelated key-streams will be generated. Experimental results also demonstrate that the generated key-streams have good randomness and they can pass all the standard criteria specified in FIPS PUB 140^-2 with no less than 98%.
