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The next section describes a methodology that aims at the incremental learning of environment properties based on moving agents' observations.
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It is based on moving particle semi-implicit (MPS) method.
Linear load trackers are based on moving averages.
Based on this, observations of moving agents that deviate from a random walk model can be used to incrementally explain effects of attractive areas in a particular location Z k from which the deviation was observed.
The novelties of this work are itemized as follows: (i) It is proposed a Bayesian approach for understanding the physical and non-physical surrounding areas of moving agents based on their nonlinear dynamics.
This can be easily solved by adding sensor nodes, based on the arrival rate of the moving agents.
A common trend in existent works is the characterization of moving agents' trajectories based on their interactions among them, without taking into account the environment and its effects in the state of agents.
Based on whether agents move toward the same target or toward separate targets, two kinds of potential functions are presented to carry out the flocking algorithm.
The proposed method was tested in two different scenarios: (i) synthetic data, in which an environment with different attractive areas is proposed and whose characteristics are inferred based on agents that move towards different goals; (ii) pedestrian dataset, where dynamics of people moving through an indoor place are analyzed and attractive points in the environment are identified.
In this paper we focus on approximate connectivity graphs for dynamic networks with moving agents.
The procedure is based on the moving rectangle window method.
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CEO of Professional Science Editing for Scientists @ prosciediting.com