211,661 research outputs found
Sound propagation in choked ducts
The linearized equations describing the propagation of sound in variable area ducts containing flow are shown to be singular when the duct mean flow is sonic. The singularity is removed when previously ignored nonlinear terms are retained. The results of a numerical study, for the case of plane waves propagating in a one-dimensional converging-diverging duct, show that the sound field is adequately described by the linearized equations only when the axial mean flow Mach number at the duct throat M sub th 0.6. For M sub th 0.6, the numerical results showed that acoustic energy flux was not conserved. An attempt was made to extend the study to include the nonlinear behavior of the sound field. Meaningful results were not obtained due, primarily, to numerical difficulties
On-Site Wireless Power Generation
Conventional wireless power transfer systems consist of a microwave power
generator and a microwave power receiver separated by some distance. To realize
efficient power transfer, the system is typically brought to resonance, and the
coupled-antenna mode is optimized to reduce radiation into the surrounding
space. In this scheme, any modification of the receiver position or of its
electromagnetic properties results in the necessity of dynamically tuning the
whole system to restore the resonant matching condition. It implies poor
robustness to the receiver location and load impedance, as well as additional
energy consumption in the control network. In this study, we introduce a new
paradigm for wireless power delivery based on which the whole system, including
transmitter and receiver and the space in between, forms a unified microwave
power generator. In our proposed scenario the load itself becomes part of the
generator. Microwave oscillations are created directly at the receiver
location, eliminating the need for dynamical tuning of the system within the
range of the self-oscillation regime. The proposed concept has relevant
connections with the recent interest in parity-time symmetric systems, in which
balanced loss and gain distributions enable unusual electromagnetic responses.Comment: 10 pages, 13 figure
The classification of traveling wave solutions and superposition of multi-solutions to Camassa-Holm equation with dispersion
Under the traveling wave transformation, Camassa-Holm equation with
dispersion is reduced to an integrable ODE whose general solution can be
obtained using the trick of one-parameter group. Furthermore combining complete
discrimination system for polynomial, the classifications of all single
traveling wave solutions to the Camassa-Holm equation with dispersion is
obtained. In particular, an affine subspace structure in the set of the
solutions of the reduced ODE is obtained. More general, an implicit linear
structure in Camassa-Holm equation with dispersion is found. According to the
linear structure, we obtain the superposition of multi-solutions to
Camassa-Holm equation with dispersion
Scalar-Kinetic Branes
This work tries to find out thick brane solutions in braneworld scenarios
described by a real scalar field in the presence of a scalar-kinetic term
with a single extra dimension, where
stands for the standard kinetic term and
. We mainly consider bent branes, namely de Sitter and Anti-de
Sitter four-dimensional slices. The solutions of a flat brane are obtained when
taking the four-dimensional cosmological constant .
When the parameter , these solutions turn to those of the standard
scenario. The localization and spectrum of graviton on these branes are also
analyzed.Comment: 10 pages, no figures, accepted by EP
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust
localization of object parts to extract localized feature representations
suitable for discrimination. However, part localization is a challenging task
due to the large variation of appearance and pose. In this paper, we show how
pre-trained convolutional neural networks can be used for robust and efficient
object part discovery and localization without the necessity to actually train
the network on the current dataset. Our approach called "part detector
discovery" (PDD) is based on analyzing the gradient maps of the network outputs
and finding activation centers spatially related to annotated semantic parts or
bounding boxes.
This allows us not just to obtain excellent performance on the CUB200-2011
dataset, but in contrast to previous approaches also to perform detection and
bird classification jointly without requiring a given bounding box annotation
during testing and ground-truth parts during training. The code is available at
http://www.inf-cv.uni-jena.de/part_discovery and
https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision
(ACCV) 201
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