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The approach adopted for steering is shown to be effective in directing the evolution of the fire parameters, thereby driving the fire predictions towards the measurements.
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The region of interest (covering the AoD of the different scatterers) is scanned by three beams with steering directions as is shown in Figure 5.
In particular, within the performance steering context it is shown how proposed performance metrics differ from using conventional performance metrics, i.e., Γq%, and how they address the inhomogeneity of suburban scenario, which is due to large ISD.
Finally, the automated steering control presented here is shown to be robust to the prescribed levels of uncertainty for highway maneuvers.
They are then demonstrated experimentally on a skid steering AGV and it is shown that the developed algorithm has the ability to parallel park AGVs in tight spaces under both vehicle localization errors and parking space detection errors.
The combination of the steering subsystem with the chassis is shown as Figure 6 d) and 6 e).
The effectiveness of the proposed control approach is shown through a steering vehicle control implementation.
It is shown that the local steering constraint performs best, at the cost of increasing the size of the problem.
The beam centroid coordination on the 2D plane of the OPD is estimated using Equation 7, and a portion of data is shown in Figure 14a: with beam steering off (blue), beam steering on (red), and optimal value (black).
As an example, the steering angle estimate for the top right trajectory in Figure 19 is shown in Figure 21.
While concrete illustrations of steering have been shown in several experiments, quantum steering has not been certified for higher dimensional systems.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com