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Significant reduction in computational effort has been achieved along with good level of accuracy by utilizing the proposed meta-model assisted sensitivity tools.
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A literature survey about existing knowledge models is done before presenting the proposed meta-model of knowledge.
Results show that the proposed meta-model can be used in the modeling of three different types of sensor web nodes with the extension mechanism.
Based on the proposed meta model, the multi-layer architecture of a LCA-oriented data service tool is then developed.
In order to reduce the computational cost it is proposed to compute the failure probability by means of the recently proposed meta-model-based importance sampling method.
Results, based on 10 climate divisions in Texas, show that the proposed meta-Gaussian model provides useful drought prediction information with performance depending on regions and seasons.
Having said this, some forms of meta-cognition could be modelled using the proposed meta-Bayesian framework.
The main advantage of the proposed meta-theory is that it makes it possible to construct contextualized explanatory models of different financial innovations without assuming any restrictive assumptions.
The proposed hybrid meta-model is developed in order to predict the occurrence of mode coupling instabilities submitted to random and interval parameter uncertainties.
The future work consists of utilizing the meta-model and profile in larger embedded system development projects.
Our proposed meta-analytic model can improve the comparison of the diagnostic accuracy of competing tests in a systematic review.
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