文章提出基于MSEM(Manager,Security and Entity Mode)的工业网络安全防护模型,它在传统纵深防御理论的基础上,将工业网络划分为实体对象、安全对象和管理对象,并增加了对象间的协同防御机制;同时依托该模型,实现基于协同防御架构的工...文章提出基于MSEM(Manager,Security and Entity Mode)的工业网络安全防护模型,它在传统纵深防御理论的基础上,将工业网络划分为实体对象、安全对象和管理对象,并增加了对象间的协同防御机制;同时依托该模型,实现基于协同防御架构的工业网络安全防护系统,提升了工业网络安全防护能力。展开更多
The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under m...The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.展开更多
This paper considers the empirical Bayes （EB） estimation problem for the parameter β of the linear regression model y = Xβ＋ ε with ε- N（0, σ^2I） given β. Based on Pitman closeness （PC） criterion and mean ...This paper considers the empirical Bayes （EB） estimation problem for the parameter β of the linear regression model y = Xβ＋ ε with ε- N（0, σ^2I） given β. Based on Pitman closeness （PC） criterion and mean square error matrix （MSEM） criterion, we prove the superiority of the EB estimator over the ordinary least square estimator （OLSE）.展开更多
文摘文章提出基于MSEM(Manager,Security and Entity Mode)的工业网络安全防护模型,它在传统纵深防御理论的基础上,将工业网络划分为实体对象、安全对象和管理对象,并增加了对象间的协同防御机制;同时依托该模型,实现基于协同防御架构的工业网络安全防护系统,提升了工业网络安全防护能力。
基金This work was supported by the Doctoral Program Foundation of the Institute of High Educationthe Special Foundation of Chinese Academy of Sciences.
文摘The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.
文摘This paper considers the empirical Bayes （EB） estimation problem for the parameter β of the linear regression model y = Xβ＋ ε with ε- N（0, σ^2I） given β. Based on Pitman closeness （PC） criterion and mean square error matrix （MSEM） criterion, we prove the superiority of the EB estimator over the ordinary least square estimator （OLSE）.
