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An explicit finite element analysis (FEA) is used to derive higher-order response surfaces for these two objectives.
The evolutionary genetic algorithm is applied to find the optimum design based on each of the surrogates for these two objectives.
Among the non-dominated solutions for a given total area budget, the best solutions depend on the decision makers preference for these two objectives.
We present corresponding algorithms for these two objectives and simulation results, which show that the weighted frequency-domain least-squares algorithm and the two extensions we derive can achieve the shortening (channel or channel and echo) as well as noise suppression objectives effectively.
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The multi-objective genetic algorithm (MOGA) based on a rank-based fitness assign method is used for optimization of these two objectives.
While they're not always at loggerheads, documents obtained by Guardian Australia show that IHMS clearly sees the potential for conflict between these two objectives.
In this manuscript, we propose a Service-Oriented Architecture (SOA) for CDS that facilitates achievement of these six objectives.
Empirical evidence concerning social inequalities in health care is essential for finding the balance between these two objectives.
Have these two objectives been in conflict for you?
Have these two objectives come into conflict for you?
For examples of studies which combine these two objective measures of integration, see DeVoretz & Pivnenko, 2005; Helgertz, Bevelander, & Tegunimataka, 2014 and Pendakur & Bevelander, 2014.
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