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Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions.
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We also assume that the application can be modeled by a finite state machine.
Many real-world network applications can be modeled as knapsack problems and solved by approximate algorithms.
The elasticity of these applications can be modeled by concave utility functions [3].
A number of video service applications can be modeled with a generalized version of Figure 15.
Such applications can be modeled by sigmoidal-like [3] utility functions, for which a part of the utility curve is convex, representing the fact that, once the average data rate is below a certain threshold rate R ̄ t h, satisfaction of a real-time user drops dramatically.
But not all vessel images in real applications can be modeled by the above Gaussian mixture model regarding the intensity distribution.
While application profiles can be modeled at different levels of granularity, a reasonable starting point is creating application profiles on a basis of symbols, which correspond to functions or objects.
While developed here for dilute gas-particle flows, quadrature-based moment methods can, in principle, be applied to any application that can be modeled by a kinetic equation (e.g., thermal and non-isothermal flows currently treated using lattice Boltzmann methods), and examples are given from the literature.
For many applications, nonlinear systems can be modeled in additive white Gaussian noise environments with the state and observation equations as follows: x n = f n ( x n − 1 ) + B n w n, (96).
Since a fixed reference environment is unrealistic for most aerospace applications, the reference environment can be modeled as the ambient operating environment by allowing it to vary as the operating environment changes.
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