Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization. Compared with other studies which focus on designing heuris...Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization. Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network. We distinguish the different components and embed VN requests onto them respectively. And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component. On the other hand, load balancing is also considered in this paper. It could avoid blocked or bottlenecked area of substrate network. Simulation experiments show that compared with other algorithms in large-scale network, acceptance ratio, average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced. It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.展开更多
Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intell...Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No.61471055
文摘Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization. Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network. We distinguish the different components and embed VN requests onto them respectively. And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component. On the other hand, load balancing is also considered in this paper. It could avoid blocked or bottlenecked area of substrate network. Simulation experiments show that compared with other algorithms in large-scale network, acceptance ratio, average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced. It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.
文摘Estimation of state-of-charge and state-of-health for batteries is one of the most important feature for modern battery management system(BMS).Robust or adaptive methods are the most investigated because a more intelligent BMS could lead to sensible cost reduction of the entire battery system.We propose a new robust method,called ERMES(extendible range multi-model estimator),for determining an estimated state-of-charge(SoC),an estimated state-of-health(SoH)and a prediction of uncertainty of the estimates(state-of-uncertainty—SoU),thanks to which it is possible to monitor the validity of the estimates and adjust it,extending the robustness against a wider range of uncertainty,if necessary.Specifically,a finite number of models in state-space form are considered starting from a modified Thevenin battery model.Each model is characterized by a hypothesis of SoH value.An iterated extended Kalman filter(EKF)is then applied to each model in parallel,estimating for each one the SoC state variable.Residual errors are then considered to fuse both the estimated SoC and SoH from the bank of EKF,yielding the overall SoC and SoH estimates,respectively.In addition,a figure of uncertainty of such estimates is also provided.
