Maintaining temporal consistency of real-time data is important for cyber-physical systems.Most of the previous studies focus on uniprocessor systems.In this paper,the problem of temporal consistency maintenance on mu...Maintaining temporal consistency of real-time data is important for cyber-physical systems.Most of the previous studies focus on uniprocessor systems.In this paper,the problem of temporal consistency maintenance on multiprocessor platforms with instance skipping was formulated based on the(m,k)-constrained model.A partitioned scheduling method SC-AD was proposed to solve the problem.SC-AD uses a derived sufficient schedulability condition to calculate the initial value of m for each sensor transaction.It then partitions the transactions among the processors in a balanced way.To further reduce the average relative invalid time of real-time data,SC-AD judiciously increases the values of m for transactions assigned to each processor.Experiment results show that SC-AD outperforms the baseline methods in terms of the average relative invalid time and the average valid ratio under different system workloads.展开更多
In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Par...In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.展开更多
基金Project(2020JJ4032)supported by the Hunan Provincial Natural Science Foundation of China。
文摘Maintaining temporal consistency of real-time data is important for cyber-physical systems.Most of the previous studies focus on uniprocessor systems.In this paper,the problem of temporal consistency maintenance on multiprocessor platforms with instance skipping was formulated based on the(m,k)-constrained model.A partitioned scheduling method SC-AD was proposed to solve the problem.SC-AD uses a derived sufficient schedulability condition to calculate the initial value of m for each sensor transaction.It then partitions the transactions among the processors in a balanced way.To further reduce the average relative invalid time of real-time data,SC-AD judiciously increases the values of m for transactions assigned to each processor.Experiment results show that SC-AD outperforms the baseline methods in terms of the average relative invalid time and the average valid ratio under different system workloads.
基金Project(51178466) supported by the National Natural Science Foundation of ChinaProject(200545) supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China+1 种基金Project(2011JQ006) supported by the Fundamental Research Funds of the Central Universities of ChinaProject(2008BAJ12B03) supported by the National Key Program of Scientific and Technical Supporting Programs of China
文摘In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.
