Sentence examples for the dynamic length from inspiring English sources
Where DL denotes the dynamic length of an inner loop iteration.
As a future work, we will study a further model for throughput estimation taking into account the dynamic length of which was assumed as a fixed length in this paper.
The dynamic length will be determined by finding the number of destinations that were visited by an individual on the majority of days that are similar to the day of his/her current activity.
Indeed, if the dynamic length (DL) denotes the number of cycles that are needed by an inner loop iteration for its processing, then the whole FFT will at least require B N MII/U m + (DL-II)(N/48 − 1/3) cycles (assuming N a power-of-4).
To investigate the effect of a growing domain, we include the dynamic length of the domain in the model Lmax t) computed by (6) L max (t ) = ∫ a b x s 2 (s, t ) + y s 2 (s, t ) d s, in which the crypt(x s, t), y s, t)) is governed by Eq. (4) and a, b are the two fixed end points of the domain, taken as (−0.1, 0) and (0.1, 0) in our simulations.
This means that for pedestrians there are two effects which imply that the effective dynamic length is increased: one as a consequence of reaction time and one from the way pedestrian locomotion is done.
The analytical and simulation study have been carried out to explore the impact of dynamic length for authentication vector on the signalling traffic in mobile network.
Let E the elite solution of a cluster ({rm {CL}}_{i},) (E' in N(E)) is a neighbor of the elite solution E, ({rm{GL}}_{i}) is the global list of each ({rm{CA}}_{i}) to receive new found elite solutions by the remaining CAs, each ({rm {CL}}_{i}) plays the role of the tabu list with a dynamic length, and (C_{rm {max}}) is the makespan of the obtained solution.
From the fetched trajectories, the dynamic prediction length (i.e. length of the D-trajectory to be predicted) will be determined by finding the number of destinations that were visited by individual(x) on the majority of the selected historical days.
Compared to the use of a predefined length, using a dynamic length to predict the number of destinations that could be visited by a targeted individual allowed the P-DPA algorithm to achieve good prediction accuracy with most subjects and virtual clients, as shown in Figs. 9 and 12. Figure 17 shows the average F-measure with all virtual clients in V-Mall dataset.
As especially observed during the 2004 Alaska fire season, huge wildfires drastically alter land cover leading to a change in the dynamic (roughness length), radiative (albedo, emissivity), vegetative (vegetation type and fraction, stomatal resistance), thermal (soil heating, thawing of permafrost), and hydrological (water loss due to the fire) surface characteristics.
