On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random in...On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.展开更多
在多重中断C程序中,中断嵌套可能会导致一些非期望的交叠执行,从而造成错误的程序执行结果。典型的问题是共享变量引起的数据竞争破坏了程序的原子性。针对此类问题,对多重中断C程序的运行时语义进行建模,根据共享变量的访问给出了一种...在多重中断C程序中,中断嵌套可能会导致一些非期望的交叠执行,从而造成错误的程序执行结果。典型的问题是共享变量引起的数据竞争破坏了程序的原子性。针对此类问题,对多重中断C程序的运行时语义进行建模,根据共享变量的访问给出了一种原子性的定义,提出了相应的数据竞争及原子性检测方法,并采用函数摘要技术缩减静态分析过程中所需遍历的程序状态。最后,设计并实现了一个数据竞争及原子性检测原型工具MIDAC(multiple interruption C program data race and atomicity checker),实验结果表明,该工具能够针对一定规模的实际程序得到很好的检测效果。展开更多
基金Project supported by the State Key Program of the National Natural Science of China (Grant No. 60835004)the Natural Science Foundation of Jiangsu Province of China (Grant No. BK2009727)+1 种基金the Natural Science Foundation of Higher Education Institutions of Jiangsu Province of China (Grant No. 10KJB510004)the National Natural Science Foundation of China (Grant No. 61075028)
文摘On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.
文摘在多重中断C程序中,中断嵌套可能会导致一些非期望的交叠执行,从而造成错误的程序执行结果。典型的问题是共享变量引起的数据竞争破坏了程序的原子性。针对此类问题,对多重中断C程序的运行时语义进行建模,根据共享变量的访问给出了一种原子性的定义,提出了相应的数据竞争及原子性检测方法,并采用函数摘要技术缩减静态分析过程中所需遍历的程序状态。最后,设计并实现了一个数据竞争及原子性检测原型工具MIDAC(multiple interruption C program data race and atomicity checker),实验结果表明,该工具能够针对一定规模的实际程序得到很好的检测效果。
