73,182 research outputs found
Computer simulation of the microstructure and rheology of semi-solid alloys under shear
The rheological behavior of metallic alloys containing both solid and liquid
phases is investigated in the low solid fraction range (<50%). This behavior
depends on both the solid fraction and the shear rate. The concept of Effective
Volume Fraction (EVF) is used to decorrelate the influence of these two
parameters. At high shear rate the slurry behaves like a suspension of hard
spheres, whereas at lower shear rate, particles tend to aggregate in clusters,
entrapping liquid and thus, increasing the EVF and the viscosity. A lattice
model is introduced to simulate the aggregation / break-up processes within a
slurry under shear. When the steady state is reached, the entrapped liquid
fraction is calculated, leading to a viscosity estimation. Simulation results
for the viscosity and 3D cluster structure are in good agreement with
experimental results.Comment: 30 pages, 17 figures, to be published in Acta Mate
Resource dimensioning through buffer sampling
Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship among the traffic offered (in terms of the mean offered load , but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulas that estimate the required capacity as a function of the input traffic and the performance target. For the special case of Gaussian input traffic, these formulas reduce to , where directly relates to the performance requirement (as agreed upon in a service level agreement) and reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level, the Gaussianity assumption is justified.\ud
As estimating is relatively straightforward, the remaining open issue concerns the estimation of . We argue that particularly if corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate
Resource dimensioning through buffer sampling
Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship between the traffic offered (in terms of the mean offered load M, but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulae that estimate the required capacity C as a function of the input traffic and the performance target. For the special case of Gaussian input traffic these formulae reduce to C = M+V , where directly relates to the performance requirement (as agreed upon in a service level agreement) and V reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level the Gaussianity assumption is justified.\ud
As estimating M is relatively straightforward, the remaining open issue concerns the estimation of V . We argue that, particularly if V corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of V is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate
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On the simulation of infiltration- and saturation-excess runoff using radar-based rainfall estimates: Effects of algorithm uncertainty and pixel aggregation
The effects of uncertainty in radar-estimated precipitation input on simulated runoff generation from a medium-sized (100-km2) basin in northern Texas are investigated. The radar-estimated rainfall was derived from Next Generation Weather Radar (NEXRAD) Level II base reflectivity data and was supplemented by ground-based rain-gauge data. Two types of uncertainty in the precipitation estimates are considered: (1) those arising from the transformation of reflectivity to rainfall rate and (2) those due to the spatial and temporal representation of the 'true' rainfall field. The study explicitly differentiates between the response of simulated saturation-excess runoff and infiltration-excess runoff to these uncertainties. The results indicate that infiltration-excess runoff generation is much more sensitive than saturation-excess runoff generation to both types of precipitation uncertainty. Furthermore, significant reductions in infiltration-excess runoff volume occur when the temporal and spatial resolution of the precipitation input is decreased. A method is developed to relate this storm-dependent reduction in runoff volume to the spatial heterogeneity of the highest-intensity rainfall periods during a storm
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
Frame-level visual features are generally aggregated in time with the
techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust
video-level representation. We here introduce a learnable aggregation technique
whose primary objective is to retain short-time temporal structure between
frame-level features and their spatial interdependencies in the representation.
Also, it can be easily adapted to the cases where there have very scarce
training samples. We evaluate the method on a real-fake expression prediction
dataset to demonstrate its superiority. Our method obtains 65% score on the
test dataset in the official MAP evaluation and there is only one misclassified
decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) .
Lastly, we believe that this method can be extended to different problems such
as action/event recognition in future.Comment: Submitted to International Conference on Computer Vision Workshop
Cross-Scale Cost Aggregation for Stereo Matching
Human beings process stereoscopic correspondence across multiple scales.
However, this bio-inspiration is ignored by state-of-the-art cost aggregation
methods for dense stereo correspondence. In this paper, a generic cross-scale
cost aggregation framework is proposed to allow multi-scale interaction in cost
aggregation. We firstly reformulate cost aggregation from a unified
optimization perspective and show that different cost aggregation methods
essentially differ in the choices of similarity kernels. Then, an inter-scale
regularizer is introduced into optimization and solving this new optimization
problem leads to the proposed framework. Since the regularization term is
independent of the similarity kernel, various cost aggregation methods can be
integrated into the proposed general framework. We show that the cross-scale
framework is important as it effectively and efficiently expands
state-of-the-art cost aggregation methods and leads to significant
improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.Comment: To Appear in 2013 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). 2014 (poster, 29.88%
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