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A higher value of expected probability can result in discovery of more optimal paths.
Based on this method, a packet-switch mechanism using flow label is presented, depending on which, packets can be transmitted fast according to label, and more optimal paths can be chosen to guarantee the QoS requirement.
However, we observe that BADGER with c = 4 samples many more optimal paths than with c = 1 in the case of the high-divergence data set, suggesting that parallel tempering is required to sample effectively and that BADGER with c = 1 may be mixing very slowly.
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In short, the bigger the radius is, the higher success rate VBF can achieve, the more energy VBF consumes, and the more optimal path VBF selects.
The major contribution of this article is the proposal of a new virtual force to guide mobile sensors onto a more optimal path in terms of coverage expansion with respect to GPS and obstacle in theory aspect.
In VBF, the bigger the pipe radius, the higher successful data delivery ratio VBF can achieve, and the more optimal the paths VBF can select.
We find that on high-divergence data sets, MC4Inversion finds more optimal sorting paths per second than BADGER and the IS technique and simultaneously avoids bias inherent in the IS technique.
The larger β, the more an optimal path-system is emphasized in the ensemble.
These results provide a basis for the design of absorber path configurations and will be relevant to future work in which optimal paths are considered for more general torques.
Obviously, when a network size increases, the optimal solution needs more iterations to discover the optimal path which costs more time (i.e. exponential time) to finish the job.
Finally, pedestrians also tread their own optimal paths through the snow.
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