7,064 research outputs found
Battery Charge Applications Based on Wide Output Voltage Range
In this study, high efficiency design procedure of a phase shifted full bridge (PSFB) converter is presented for on-board electrical vehicle (EV) battery charger. Presented design methodology used lithium-ion battery cells because of their high voltage and current rates compared to a lead-acid battery cells. In this case, PSFB converter can be regulated wide range output voltage with while its soft switching operation is maintained. The basic operation principles of PSFB converter is defined and its soft switching operation requirements are given. To evaluate the performance of the converter over wide output voltage range, zero voltage switching (ZVS) operation of converter is discussed based on dead time requirement. To improve efficiency, the snubber inductance effects on soft switching over wide output voltage range are evaluated. Finally, operation of the PSFB converter is validated experimentally with a prototype which has 42-54 V/15 A output range at 200 kHz switching frequency
Projections for Neutral Di-Boson and Di-Higgs Interactions at FCC-he Collider
As a high energy e-p collider, FCC-he, has been recently proposed with
sufficient energy options to investigate Higgs couplings. To analyse the
sensitivity on the Higgs boson couplings, we focus spesifically on the CP-even
and CP-odd Wilson coefficients with and four-point
interactions of Higgs boson with Effective Lagrangian Model through the process
. We simulate the related processes in FCC-he, with 60 GeV
and 120 GeV beams and 50 TeV proton beam collisions. We present the
exclusion limits on these couplings both for 68% and 95% C.L. in terms of
integrated luminosities.Comment: 18 pages, 20 figures, 3 table
Distributed data association for multi-target tracking in sensor networks
Associating sensor measurements with target tracks is a fundamental and challenging problem in multi-target tracking. The problem is even more
challenging in the context of sensor networks, since association is coupled
across the network, yet centralized data processing is in general
infeasible due to power and bandwidth limitations. Hence efficient, distributed solutions are needed. We propose techniques based on graphical models to efficiently solve such data association problems in sensor networks. Our approach scales well with the number of sensor nodes in the network, and it is well--suited for distributed implementation. Distributed inference is realized by a message--passing algorithm which requires iterative, parallel exchange of information among neighboring nodes on the graph. So as to address trade--offs between inference performance and communication costs, we also propose a communication--sensitive form of message--passing that is capable of achieving near--optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data
With four Standard Model families, the LHC could discover the Higgs boson with a few fb^-1
The existence of a 4th SM family would produce a large enhancement of the
gluon fusion channel of Higgs boson production at hadron colliders. In this
case, the SM Higgs boson could be seen at the CERN Large Hadron Collider (LHC)
via the golden mode (H->4l) with an integral luminosity of only a few fb^-1.Comment: 7 pages, 2 figures, 2 tables, references updated in v
Segmentation of the evolving left ventricle by learning the dynamics
We propose a method for recursive segmentation of the left ventricle
(LV) across a temporal sequence of magnetic resonance (MR) images.
The approach involves a technique for learning the LV boundary
dynamics together with a particle-based inference algorithm on
a loopy graphical model capturing the temporal periodicity of the
heart. The dynamic system state is a low-dimensional representation
of the boundary, and boundary estimation involves incorporating
curve evolution into state estimation. By formulating the problem
as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also
on predictions based on past and future boundary estimates. We assess
and demonstrate the effectiveness of the proposed framework
on a large data set of breath-hold cardiac MR image sequences
Learning the dynamics and time-recursive boundary detection of deformable objects
We propose a principled framework for recursively segmenting deformable objects across a sequence
of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac
cycle. The approach involves a technique for learning the system dynamics together with methods of
particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing
the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation
of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state
estimation. By formulating the problem as one of state estimation, the segmentation at each particular
time is based not only on the data observed at that instant, but also on predictions based on past and future
boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes
to temporally segmenting any deformable object
Analysis of heat transfer and entropy generation for a low-Peclet-number microtube flow using a second-order slip model: an extended-Graetz problem
Cataloged from PDF version of article.The classical Graetz problem, which is the problem of the hydrodynamically developed, thermally
developing laminar flow of an incompressible fluid inside a tube neglecting axial conduction and viscous dissipation,
is one of the fundamental problems of internal-flow studies. This study is an extension of the Graetz problem to
include the rarefaction effect, viscous dissipation term and axial conduction with a constant wall temperature thermal
boundary condition. The energy equation is solved to determine the temperature field analytically using general
eigenfunction expansion with a fully developed velocity profile. To analyze the low-Peclet-number nature of the
flow, the flow domain is extended from −∞ to +∞. To model the rarefaction effect, a second-order slip model
is implemented. The temperature distribution, local Nusselt number, and local entropy generation are determined
in terms of confluent hypergeometric functions. This kind of theoretical study is important for a fundamental
understanding of the convective heat transfer characteristics of flows at the microscale and for the optimum design
of thermal systems, which includes convective heat transfer at the microscale, especially operating at low Reynolds
number
Design and low-power implementation of an adaptive image rejection receiver
This paper deals with and details the design and implementation of a low-power; hardware-efficient adaptive self-calibrating image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Hybrid strength-reduced and re-scheduled data-flow, low-power implementation of the adaptive self-calibration algorithm is developed and its efficiency is demonstrated through simulation case studies. A behavioral and structural model is developed in Matlab as well as a low-level architectural design in VHDL providing valuable test benches for the performance measures undertaken on the detailed algorithms and structures
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