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The methodology of calibration is to minimize the discrepancy between the experimental hysteresis loops and the corresponding numerical results using Particle Swarm Optimization (PSO) algorithm.
Pursing of simple and direct synthesis route is vital and desirable to minimize the discrepancy between electrodes, and is essential for implementing of reliable detection.
Latent Heat Thermal Storage (LHTS) systems can be applied to minimize the discrepancy between energy supply and demand in solar applications.
The subsystems minimize the discrepancy between their own local variables and the corresponding allocated values, and then return the optimization results to main optimization.
The objective is to minimize the discrepancy between these fluxes and the given high-order target mass fluxes, subject to constraints that enforce physically motivated bounds on the associated primitive variable (density).
The underlying paradigm is, roughly speaking, to minimize the discrepancy between the reconstruction and measured data while, at the same time, taking into account various structural a-priori information via suitable penalty terms.
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Constitutive parameters have been identified by minimizing the discrepancy between model and experimental data.
The imitation process is modeled as a hierarchical optimization system, which minimizes the discrepancy between two multi-dimensional datasets.
The dielectric profile of the system is then determined by minimizing the discrepancy between these two calculations simultaneously for multiple positions of the test charge.
Pile design efficiency was improved by minimizing the discrepancy between design and field pile resistances through a proposed probability-based construction control method.
Then, a heuristic algorithm is designed for the TDM mode wireless multi-hop networks by minimizing the discrepancy between the expected network cost and the optimal one in each timeslot.
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