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The professional diversity allows us to challenge each other to design stronger, more refined solutions during the development process.
Uniformly and adaptively refined solutions using a Cartesian-mesh approach are obtained and compared to each other and to uniformly refined structured mesh results.
As designers, and animal research facility users and management develop more knowledge and appreciation of ergonomics, more refined solutions and suitable applications will be stimulated or will become apparent.
The proposed method, named IDLHCSA, combines the potential of the IDLHC in finding good matched models while preserving the diversity of solutions and the potential of the SA with local control in finding local (refined) solutions.
These methods adopt a combination of linear and non-linear solving strategies where a linear method (e.g., Direct Linear Transformation) is used to recover initial approximations for the parameters, followed by a non-linear algorithm (e.g., Levenberg-Marquardt) in search of refined solutions of the orientation and calibration.
Post-prediction results were nearly identical when obtained from rigid body solutions instead of refined solutions (results not shown).
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In this refined solution, the effect of the direct shear force is also considered.
The refined solution space and the generated information for system optimization are the basis for the multi-objective optimization.
This guess enables the algorithm to reach more refined solution iteratively by ensuring fast convergence.
It also offers, as discussed above in the context of cross-validation, a more refined solution to artifacts compared to more standard approaches (FBP, SIRT, etc).
In this paper, we focus on iterative tomographic reconstruction algorithms, where unknown coefficients in a 3D image are converged to a refined solution at each iteration.
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