642 research outputs found
A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud
Energy efficiency has become an important measurement of scheduling algorithm
for private cloud. The challenge is trade-off between minimizing of energy
consumption and satisfying Quality of Service (QoS) (e.g. performance or
resource availability on time for reservation request). We consider resource
needs in context of a private cloud system to provide resources for
applications in teaching and researching. In which users request computing
resources for laboratory classes at start times and non-interrupted duration in
some hours in prior. Many previous works are based on migrating techniques to
move online virtual machines (VMs) from low utilization hosts and turn these
hosts off to reduce energy consumption. However, the techniques for migration
of VMs could not use in our case. In this paper, a genetic algorithm for
power-aware in scheduling of resource allocation (GAPA) has been proposed to
solve the static virtual machine allocation problem (SVMAP). Due to limited
resources (i.e. memory) for executing simulation, we created a workload that
contains a sample of one-day timetable of lab hours in our university. We
evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list
of virtual machines in start time (i.e. earliest start time first) and using
best-fit decreasing (i.e. least increased power consumption) algorithm, for
solving the same SVMAP. As a result, the GAPA algorithm obtains total energy
consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page
Towards improving positioning accuracy of conducting polymer actuators
Recently, there have been significant developments in conducting polymers, particularly in their synthesis and use as electromechanical actuators. This is mainly due to their many promising features including biocompatibility, high force to weight ratio, suitability to open loop control. On the other hand, they suffer from nonlinear problems such as hysteresis and creep. With this in mind, it is the aim of this study to evaluate the existence level of these nonlinearities and their mathematical modeling in order to improve the positioning accuracy of conducting polymer actuators. The polymer actuator considered in this study which has a symmetrical structure can operate in both liquid and non-liquid media as opposed to its predecessor. The actuator drives a rigid link, like positioning a payload. The experimental results demonstrate that while the hysteresis is negligibly small, the level of the creep is significant enough to model it and subsequently employ the model to improve steady-state positioning of the actuator. Based on experimental results, a viscoelastic model is employed to describe the creep behaviour. The outcomes of this study will pave the way towards understanding of the limitations as well as potential usefulness of conducting polymer actuators in many cutting edge applications ranging from biomedical to micro/nano manipulation systems
A heuristic combinatorial optimisation approach to synthesising a population for agent-based modelling purposes
This paper presents an algorithm that follows the sample-free approach to synthesise a population for agent based modelling purposes. This algorithm is among the very few in the literature that do not rely on a sample survey data to construct a synthetic population, and thus enjoy a potentially wider applications where such survey data is not available or inaccessible. Different to existing sample-free algorithms, the population synthesis presented in this paper applies the heuristics to part of the allocation of synthetic individuals into synthetic households. As a result the iterative process allocating individuals into households, which normally is the most computationally demanding and time consuming process, is required only for a subset of synthetic individuals. The population synthesiser in this work is therefore computational efficient enough for practical application to build a large synthetic population (many millions) for many thousands target areas at the smallest possible geographical level. This capability ensures that the geographical heterogeneity of the resulting synthetic population is best preserved. The paper also presents the application of the new method to synthesise the population for New South Wales in Australia in 2006. The resulting total synthetic population has approximately 6 million people living in over 2.3 million households residing in private dwellings across over 11000 Census Collection Districts. Analyses show evidence that the synthetic population matches very well with the census data across seven demographics attributes that characterise the population at both household level and individual level
Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
A crucial limitation of current high-resolution 3D photoacoustic tomography
(PAT) devices that employ sequential scanning is their long acquisition time.
In previous work, we demonstrated how to use compressed sensing techniques to
improve upon this: images with good spatial resolution and contrast can be
obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning
systems if sparsity-constrained image reconstruction techniques such as total
variation regularization are used. Now, we show how a further increase of image
quality can be achieved for imaging dynamic processes in living tissue (4D
PAT). The key idea is to exploit the additional temporal redundancy of the data
by coupling the previously used spatial image reconstruction models with
sparsity-constrained motion estimation models. While simulated data from a
two-dimensional numerical phantom will be used to illustrate the main
properties of this recently developed
joint-image-reconstruction-and-motion-estimation framework, measured data from
a dynamic experimental phantom will also be used to demonstrate their potential
for challenging, large-scale, real-world, three-dimensional scenarios. The
latter only becomes feasible if a carefully designed combination of tailored
optimization schemes is employed, which we describe and examine in more detail
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
We present a framework for accelerated iterative reconstructions using a fast
and approximate forward model that is based on k-space methods for
photoacoustic tomography. The approximate model introduces aliasing artefacts
in the gradient information for the iterative reconstruction, but these
artefacts are highly structured and we can train a CNN that can use the
approximate information to perform an iterative reconstruction. We show
feasibility of the method for human in-vivo measurements in a limited-view
geometry. The proposed method is able to produce superior results to total
variation reconstructions with a speed-up of 32 times
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