The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs...The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs),Representative Elementary Watershed(REWs) and Hydrologic Response Units(HRUs).In this paper,a new discretization approach for landforms that have similar hydrologic properties is developed and discussed here for the Integrated Hydrologic Model(IHM),a combining simulation of surface and groundwater processes,accounting for the interaction between the systems.The approach used in the IHM is to disaggregate basin parameters into discrete landforms that have similar hydrologic properties.These landforms may be impervious areas,related areas,areas with high or low clay or organic fractions,areas with significantly different depths-to-water-table,and areas with different types of land cover or different land uses.Incorporating discrete landforms within basins allows significant distributed parameter analysis,but requires an efficient computational structure.The IHM integration represents a new approach interpreting fluxes across the model interface and storages near the interface for transfer to the appropriate model component,accounting for the disparate discretization while rigidly maintaining mass conservation.The discretization approaches employed in IHM will provide some ideas and insights which are helpful to those researchers who have been working on the integrated models for surface-groundwater interaction.展开更多
A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorit...A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorithm for discretizing continuous numeric-values is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with K2 Bayesian network （BN） structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. Then, BN inference can be carried out on the BN. Finally, the QoS metric is guaranteed to a specific value with certain probability by tuning its causal contexts to suitable values suggested by the BN inference. Our approach is validated to be sound and effective by simulations on a peer-to-peer （P2P） network.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.40901026)Beijing Municipal Science & Technology New Star Project Funds(No.2010B046)+1 种基金Beijing Municipal Natural Science Foundation(No.8123041)Southwest Florida Water Management District(SFWMD) Project
文摘The commonly used discretization approaches for distributed hydrological models can be broadly categorized into four types,based on the nature of the discrete components:Regular Mesh,Triangular Irregular Networks(TINs),Representative Elementary Watershed(REWs) and Hydrologic Response Units(HRUs).In this paper,a new discretization approach for landforms that have similar hydrologic properties is developed and discussed here for the Integrated Hydrologic Model(IHM),a combining simulation of surface and groundwater processes,accounting for the interaction between the systems.The approach used in the IHM is to disaggregate basin parameters into discrete landforms that have similar hydrologic properties.These landforms may be impervious areas,related areas,areas with high or low clay or organic fractions,areas with significantly different depths-to-water-table,and areas with different types of land cover or different land uses.Incorporating discrete landforms within basins allows significant distributed parameter analysis,but requires an efficient computational structure.The IHM integration represents a new approach interpreting fluxes across the model interface and storages near the interface for transfer to the appropriate model component,accounting for the disparate discretization while rigidly maintaining mass conservation.The discretization approaches employed in IHM will provide some ideas and insights which are helpful to those researchers who have been working on the integrated models for surface-groundwater interaction.
基金Supported by the National High Technology Research and Development Program of China （No. 2007AA010302, 2009AA012404） the National Basic Research Program of China （No. 2007CB307103）+1 种基金 the National Natural Science Foundation of China （No. 60432010, 60802034） the Specialized Research Fund for the Doctoral Program of Higher Education （No. 20070013026）.
文摘A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorithm for discretizing continuous numeric-values is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with K2 Bayesian network （BN） structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. Then, BN inference can be carried out on the BN. Finally, the QoS metric is guaranteed to a specific value with certain probability by tuning its causal contexts to suitable values suggested by the BN inference. Our approach is validated to be sound and effective by simulations on a peer-to-peer （P2P） network.
