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In this study, we simulated multiple flow regime metrics simultaneously by coupling a distributed hydrological model with an equally weighted multi-objective optimization algorithm.
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The plate edge is joined to the shell by coupling rotational springs distributed around the edge.
This is achieved by solving the CCI problem by coupling the MAC protocol with distributed beamforming.
The control architecture is formalized by a system of coupled dynamic neural fields representing a distributed network of local but connected neural populations.
In this paper, we introduce a graph structure for a distributed sampling and reconstruction system by coupling agents in a spatially distributed network with innovative positions of signals.
The effects of parameterized cross sections are evaluated in this study by developing a modeling framework that externally couples a spatially distributed hydrologic model, HL-RDHM, with a one-dimensional unsteady hydraulic model, HEC-RAS.
The problem of obtaining bounds on the response of a distributed system which is governed by a class of coupled partial differential equations is considered.
Service-oriented applications are dynamically built by assembling existing, loosely coupled, distributed, and heterogeneous services.
Typically, a distributed network having 100 nodes needs a couple of joules of energy for scheduling [26, 27].
We propose a distributed model predictive control approach for linear time-invariant systems coupled via dynamics.
The above local architecture of the model is expanded to a distributed one, first through the addition of a second population, coupled to the first by recurrent long-range projections (Figure 1B).
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