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All of the testing frames are matched with the trained prototypes using the NN matching rule.
Similar to the comparison described in Section 4.1, the NN matching rule is adopted to identify each testing frames in the fine stage.
a The testing frames employed are extracted from datasets denoted as Curtain [30], Water Surface [30], Fountain [30], Fountain02 [38], Snow fall [38], and Skating [38] that present periodical or irregular background motion such as a curtain blown by the wind, flowing water, or falling snowflakes.
The testing frames are extracted from the Curtain [30], Water Surface [30], Fountain [30], Fountain02 [38], Snow fall [38], and Skating [38] datasets, which include different types of periodical or irregular background motion such as a curtain blown by the wind, flowing water, or falling snowflakes.
We generated a synthetic trajectory of 25 000 frames, during which the protein visited 76 of the 78 clusters present in the training data (cluster membership for 10 000 training and testing frames is shown in Fig. 4).
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A special pivoting testing frame is conceived to apply cyclic, uniformly distributed inertia-like forces.
Panels are mounted on a rigid testing frame and loaded with an uniformly distributed lateral pressure.
Predictions of constraint force compared favorably to experimental values when the compliance of the testing frame was modeled.
After humidity conditioning, one aggregate slice was attached to the base of a testing frame, and pull-off force was applied on the other aggregate slice.
Crystal plasticity modelling explicitly captures micro-pillar geometry, crystal orientation, as well as the stiffnesses of components of the experimental testing frame and sample mounting.
This task is currently performed by digitizing the component's surface after mounting it into its final assembly or on a special testing frame.
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