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Exact(7)
PDFs are proposed to characterize interactions at far distances from attractive centers of force due to their properties for representing stochastic phenomena.
From Table 2, it can be seen that estimations of centers of force locations are not affected drastically at the presence of noise.
By taking the estimations of attractive centers of force generated by agents that behave according to a near range of interaction explained in Section 3.1.
As can been seen, similar centers of force can be clustered by using a classic k-means algorithm in order to identify attractive areas in the scene that combine information of similar attractors' estimations.
Particularly, qualitative results can be seen by comparing our approximation of attractive centers of force shown in Fig. 10 with the zones manually labeled by [39] and depicted in Fig. 8.
Since the control vector U k can be seen as the agent's velocity due to the interaction with external forces when at observing Z k, the orientation of this vector can be used as a measurement of alignment with attractive centers of force.
Similar(52)
Similar estimated centers of forces are grouped to characterize attractive areas.
Estimations of attractive zones' locations are depicted as red crosses on Fig. 9. Fig. 9 Clustering of attractive centers of forces.
"No matter how we beautify this term, it means one single center of power, one single center of force and one single master".
Nonetheless, it is feasible to determine a line on which the center of force belongs.
Estimating the center of force (mathcal {X}_{0}) in a far range of interaction is not possible since there is not conclusive information about velocity decrements.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com