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Optical design and calculation were employed to optimize the structure using a optimization program.
Multi-response optimization technique was also employed to optimize the control factors.
The particle swarm optimization (PSO) algorithm is employed to optimize parameters of the adaptive gain nonlinear observer (AGNO).
Particle swarm optimization and non-linear programming techniques were employed to optimize the parameters of cycles.
Various strategies could be employed to optimize the PI range.
A full factorial design was employed to optimize the crosslinking.
The SURE is employed to optimize the objective function.
Experimental design was employed to optimize the method.
Genetic algorithm is employed to optimize the system performance.
The multivariate analysis of variance (MANOVA) was employed to optimize the data matrix.
For a resistive-state-switched circuit, Clark's control logic is employed to optimize the shunt resistance.
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