Qiongli Wu

An efficient computational method for global sensitivity analysis and its application to tree growth modelling

Global sensitivity analysis has a key role to play in the design and parameterization of functional-structural plant growth models which combine the description of plant structural development(organogenesis and geometry) and functional growth(biomass accumulation and allocation). We are particularly interested in this study in Sobols method which decomposes the variance of the output of interest into terms due to individual parameters but also to interactions between parameters. Such information is crucial for systems with potentially high levels of non-linearity and interactions between processes, like plant growth. However, the computation of Sobols indices relies on Monte Carlo sampling and re-sampling, whose costs can be very high, especially when model evaluation is also expensive, as for tree models. In this paper, we thus propose a new method to compute Sobols indices inspired by Homma-Saltelli, which improves slightly their use of model evaluations, and then derive for this generic type of computational methods an estimator of the error estimation of sensitivity indices with respect to the sampling size. It allows the detailed control of the balance between accuracy and computing time. Numerical tests on a simple non-linear model are convincing and the method is finally applied to a functional-structural model of tree growth, GreenLab, whose particularity is the strong level of interaction between plant functioning and organogenesis.

Game and balance multicast architecture algorithms for sensor grid.

We propose a scheme to reach shorter multicast delay, better energy utilizing efficiency and higher efficiency of data transferring for Sensor Grid. Our scheme calculates the space, energy and data weight vectors in one cluster. Then it searches a new vector composed by the linear combination of the three individual ones. We build game balance equation, use the equal correlation coefficient between the new and old vectors to find the point of game balance, seek linear parameters, and generate a least weight path tree. Extended simulation results indicate that our scheme attains less average multicast delay, the number of links used and better system robusticity.

3 Vectors Game and Balance Multicast architecture Algorithms for Sensor Grid

We propose a scheme to reach shorter multicast delay, better energy utilizing efficiency and higher efficiency of data transferring for Sensor Grid. Our scheme calculates the space, energy and data weight vectors in one cluster. Then it searches a new vector composed by the linear combination of the three individual ones. We build game balance equation, use the equal correlation coefficient between the new and old vectors to find the point of game balance, seek linear parameters, and generate a least weight path tree. Extended simulation results indicate that our scheme attains less average multicast delay, the number of links used and better system robusticity.