To address the issue of resource scarcity in wireless communication, a novel dynamic call admission control scheme for wireless mobile network was proposed. The scheme established a reward computing model of call admi...To address the issue of resource scarcity in wireless communication, a novel dynamic call admission control scheme for wireless mobile network was proposed. The scheme established a reward computing model of call admission of wireless cell based on Markov decision process, dynamically optimized call admission process according to the principle of maximizing the average system rewards. Extensive simulations were conducted to examine the performance of the model by comparing with other policies in terms of new call blocking probability, handoff call dropping probability and resource utilization rate. Experimental results show that the proposed scheme can achieve better adaptability to changes in traffic conditions than existing protocols. Under high call traffic load, handoff call dropping probability and new call blocking probability can be reduced by about 8%, and resource utilization rate can be improved by 2%-6%. The proposed scheme can achieve high source utilization rate of about 85%.展开更多
In this paper, we use the car-following model with the anticipation effect of the potential lane-changing probability （Acta Mech. Sin. 24 （2008） 399） to investigate the effects of the potential lane-changing proba...In this paper, we use the car-following model with the anticipation effect of the potential lane-changing probability （Acta Mech. Sin. 24 （2008） 399） to investigate the effects of the potential lane-changing probability on uniform flow. The analytical and numerical results show that the potential lane-changing probability can enhance the speed and flow of uniform flow and that their increments are related to the density.展开更多
Probabilistic models are commonly used in computational medicine for diagnostics. Smoking cessation is an important issue of modern medicine. According to statistics about third part of male in global population are s...Probabilistic models are commonly used in computational medicine for diagnostics. Smoking cessation is an important issue of modern medicine. According to statistics about third part of male in global population are smokers. It is important to develop new approaches for smoking cessation treatment including methods of early diagnosis and development of individual treatment programs for each patient according to his or her physical peculiarities. One of the promising methods is computerized approach for tobacco treatment including electronic survey and computer data analysis. In this work we propose a probabilistic model based on Markov chain for estimation of patient behavior in the process on medical survey. This analysis can help to find out patient＇s individual characteristics and develop effective personal treatment program. Based on probabilistic model software was developed with aim to enhance diagnosis and developing individual smoking cessation treatment programs for each patient.展开更多
For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP) based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the marg...For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP) based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the marginal probability density function(PDF) of the epistemic variable and the conditional PDF of the aleatory one at the fixed epistemic variable,the epistemic and aleatory uncertainties are propagated to the response of the structure firstly in the presented method.And the computational model for calculating the importance measures of the epistemic variables is established.For solving the computational model,the high efficient SDP method is applied to estimating the first order high dimensional model representation(HDMR) to obtain the importance measures.Compared with the direct Monte Carlo method,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared with the existing approximation method,because it is suitable not only for the linear response functions,but also for nonlinear response functions.Several examples are used to demonstrate the advantages of the presented method.展开更多
基金Project(60873082) supported by the National Natural Science Foundation of ChinaProject(09C794) supported by the Natural Science Foundation of Education Department of Hunan Province, China+1 种基金Project (S2008FJ3078) supported by the Science and Technology Program Foundation of Hunan Province, ChinaProject(07JJ6109) supported by the Natural Science Foundation of Hunan Province, China
文摘To address the issue of resource scarcity in wireless communication, a novel dynamic call admission control scheme for wireless mobile network was proposed. The scheme established a reward computing model of call admission of wireless cell based on Markov decision process, dynamically optimized call admission process according to the principle of maximizing the average system rewards. Extensive simulations were conducted to examine the performance of the model by comparing with other policies in terms of new call blocking probability, handoff call dropping probability and resource utilization rate. Experimental results show that the proposed scheme can achieve better adaptability to changes in traffic conditions than existing protocols. Under high call traffic load, handoff call dropping probability and new call blocking probability can be reduced by about 8%, and resource utilization rate can be improved by 2%-6%. The proposed scheme can achieve high source utilization rate of about 85%.
基金Supported by the Program for New Century Excellent Talents in University under Grant No.NCET-08-0038the National Natural Science Foundation of China under Grant Nos.70701002,70971007,and 70521001 the National Basic Research Program of China under Grant No.2006CB705503
文摘In this paper, we use the car-following model with the anticipation effect of the potential lane-changing probability （Acta Mech. Sin. 24 （2008） 399） to investigate the effects of the potential lane-changing probability on uniform flow. The analytical and numerical results show that the potential lane-changing probability can enhance the speed and flow of uniform flow and that their increments are related to the density.
文摘Probabilistic models are commonly used in computational medicine for diagnostics. Smoking cessation is an important issue of modern medicine. According to statistics about third part of male in global population are smokers. It is important to develop new approaches for smoking cessation treatment including methods of early diagnosis and development of individual treatment programs for each patient according to his or her physical peculiarities. One of the promising methods is computerized approach for tobacco treatment including electronic survey and computer data analysis. In this work we propose a probabilistic model based on Markov chain for estimation of patient behavior in the process on medical survey. This analysis can help to find out patient＇s individual characteristics and develop effective personal treatment program. Based on probabilistic model software was developed with aim to enhance diagnosis and developing individual smoking cessation treatment programs for each patient.
基金supported by the National Natural Science Foundation of China (Grant No. 51175425)the Aviation Science Foundation (Grant No.2011ZA53015)the Doctorate Foundation of Northwestern Polytechnical University (Grant No. CX201205)
文摘For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP) based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the marginal probability density function(PDF) of the epistemic variable and the conditional PDF of the aleatory one at the fixed epistemic variable,the epistemic and aleatory uncertainties are propagated to the response of the structure firstly in the presented method.And the computational model for calculating the importance measures of the epistemic variables is established.For solving the computational model,the high efficient SDP method is applied to estimating the first order high dimensional model representation(HDMR) to obtain the importance measures.Compared with the direct Monte Carlo method,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared with the existing approximation method,because it is suitable not only for the linear response functions,but also for nonlinear response functions.Several examples are used to demonstrate the advantages of the presented method.
