16,041 research outputs found
Off-line Flux Injection Test Probe for Screening Defective Rotors in Squirrel Cage Induction Machines
Object Contour and Edge Detection with RefineContourNet
A ResNet-based multi-path refinement CNN is used for object contour
detection. For this task, we prioritise the effective utilization of the
high-level abstraction capability of a ResNet, which leads to state-of-the-art
results for edge detection. Keeping our focus in mind, we fuse the high, mid
and low-level features in that specific order, which differs from many other
approaches. It uses the tensor with the highest-levelled features as the
starting point to combine it layer-by-layer with features of a lower
abstraction level until it reaches the lowest level. We train this network on a
modified PASCAL VOC 2012 dataset for object contour detection and evaluate on a
refined PASCAL-val dataset reaching an excellent performance and an Optimal
Dataset Scale (ODS) of 0.752. Furthermore, by fine-training on the BSDS500
dataset we reach state-of-the-art results for edge-detection with an ODS of
0.824.Comment: Keywords: Object Contour Detection, Edge Detection, Multi-Path
Refinement CN
Planck priors for dark energy surveys
Although cosmic microwave background (CMB) anisotropy data alone cannot
constrain simultaneously the spatial curvature and the equation of state of
dark energy, CMB data provide a valuable addition to other experimental
results. However computing a full CMB power spectrum with a Boltzmann code is
quite slow; for instance if we want to work with many dark energy and/or
modified gravity models, or would like to optimize experiments where many
different configurations need to be tested, it is possible to adopt a quicker
and more efficient approach.
In this paper we consider the compression of the projected Planck CMB data
into four parameters, R (scaled distance to last scattering surface), l_a
(angular scale of sound horizon at last scattering), Omega_b h^2 (baryon
density fraction) and n_s (powerlaw index of primordial matter power spectrum),
all of which can be computed quickly. We show that, although this compression
loses information compared to the full likelihood, such information loss
becomes negligible when more data is added. We also demonstrate that the method
can be used for scalar field dark energy independently of the parametrisation
of the equation of state, and discuss how this method should be used for other
kinds of dark energy models.Comment: 8 pages, 3 figures, 4 table
P04.63. The consciousness of medical doctors about collaborative practice of Western medicine and traditional Korean medicine
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