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This solves the load balancing problem by keeping all simulation engines always busy and reduces to the minimum the transient storage of incomplete results, thus reducing the shared memory traffic.
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In this way, the balancing of tasks in the MB level is achieved dynamically without going any thread to idle state, which solves the load balance problem.
In this paper, we propose and evaluate an analytical framework to formulate and solve the load management problem in this context.
In this paper, a dynamic thread scheduling strategy is proposed to solve the load imbalancing problem.
The proposed technique in this research involves an iterative algorithm based on trajectory sensitivity analysis to solve the load shedding problem.
Different programs use a variety of different mathematical methods to solve the load flow equations associated with the network.
Then, the bearing restoring forces are then obtained through iteratively solving the load distribution equations at every time step.
In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits.
A weighted coefficient method was proposed by Huang et al. [73], to solve the load distribution of a redundantly actuated walking machine, in which the values of the weighted coefficients can be given arbitrarily according to different optimization goals.
It is used to model the corrugated laminate, solve the load cases, and obtain the substitute stiffness matrix entries as a postprocessing task.
Pre-loaded parallelograms and loading springs are utilized to solve the loading problem in practical applications, and the redundant degree of freedom (DOF) is restricted to a certain extent.
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