319 research outputs found
Self-organising maps : statistical analysis, treatment and applications.
This thesis presents some substantial theoretical analyses and optimal treatments
of Kohonen's self-organising map (SOM) algorithm, and explores the practical
application potential of the algorithm for vector quantisation, pattern classification,
and image processing. It consists of two major parts. In the first part, the SOM
algorithm is investigated and analysed from a statistical viewpoint. The proof of its
universal convergence for any dimensionality is obtained using a novel and
extended form of the Central Limit Theorem. Its feature space is shown to be an
approximate multivariate Gaussian process, which will eventually converge and
form a mapping, which minimises the mean-square distortion between the feature
and input spaces. The diminishing effect of the initial states and implicit effects of
the learning rate and neighbourhood function on its convergence and ordering are
analysed and discussed. Distinct and meaningful definitions, and associated
measures, of its ordering are presented in relation to map's fault-tolerance. The
SOM algorithm is further enhanced by incorporating a proposed constraint, or
Bayesian modification, in order to achieve optimal vector quantisation or pattern
classification. The second part of this thesis addresses the task of unsupervised
texture-image segmentation by means of SOM networks and model-based
descriptions. A brief review of texture analysis in terms of definitions, perceptions,
and approaches is given. Markov random field model-based approaches are
discussed in detail. Arising from this a hierarchical self-organised segmentation
structure, which consists of a local MRF parameter estimator, a SOM network, and
a simple voting layer, is proposed and is shown, by theoretical analysis and
practical experiment, to achieve a maximum likelihood or maximum a posteriori
segmentation. A fast, simple, but efficient boundary relaxation algorithm is
proposed as a post-processor to further refine the resulting segmentation. The class
number validation problem in a fully unsupervised segmentation is approached by
a classical, simple, and on-line minimum mean-square-error method. Experimental
results indicate that this method is very efficient for texture segmentation
problems. The thesis concludes with some suggestions for further work on SOM
neural networks
Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots
As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ânuclear cavesâ. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm
Learning cognitive maps: Finding useful structure in an uncertain world
In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg
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ReSCon '12, Research Student Conference: Book of Abstracts
The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
ANALYSIS OF THE VOLTAGE STABILITY PROBLEM IN ELECTRIC POWER SYSTEMS USING ARTIFICIAL NEURAL NETWORKS
PhDThe voltage stability problem in electric power systems is concerned with the analysis of
events and mechanisms that can lead a system into inadmissible operating conditions from the
voltage viewpoint. In the worst case, total collapse of the system may result, with disastrous consequences
for both electricity utilities and customers. The analysis of this problem has become an
important area of research over the past decade due to some instances of voltage collapse that have
occurred in electric systems throughout the world.
This work addresses the voltage stability problem within the framework of artificial neural
networks. Although the field of neural networks was established during the late 1940s, only in the
past few years has it experienced rapid development. The neural network approach offers some
potential advantages to the solution of problems for which an analytical solution is difficult. Also,
efficient and accurate computation may be achieved through neural networks.
The first contribution of this work refers to the development of an artificial neural network
capable of computing a static voltage stability index, which provides information on the stability
of a given operating state in the power system. This analytical tool was implemented as a self-contained
computational system which exhibited good accuracy and extremely low processing times
when applied to some study cases.
Dynamic characteristics of the electrical system in the voltage stability problem are very
important. Therefore, in a second stage of the present work, the scope of the research was extended
so as to take into account these new aspects. Another neural network-based computational system
was developed and implemented with the purpose of providing some information on the behaviour
of the electrical system in the immediate future.
Examples and case studies are presented throughout the thesis in order to illustrate the most
relevant aspects of both artificial neural networks and the computational models developed. A general
discussion summarises the main contributions of the present work and topics for further
research are outlined.CNPq -Conselho Nacional de Desenvolvimento Cientffico e Tecnoldgico
EPUSP -Escola Politecnica da Universidade de Sao Paul
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Complexity-reduced hardware-based track-trigger for CMS upgrade
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC)
is designed to study the results of proton-proton collisions. The Tracker
sub-detector is designed to detect and reconstruct the trajectories of charged
particles produced by the collisions. During the lifetime of the CMS detector,
there have been several upgrades aimed at increasing the chance of discovering
new physics through increased luminosity levels and instrumentation of
advanced technology. The High-Luminosity upgrade optimises the LHC to
accelerate high-energy particles with an average of 200 proton-proton
interactions per bunch crossing. The Level-1 Trigger system promptly analyses
and filters collisions using hardware to reduce the data volume in real-time. For
the upgrade, the trigger mechanism will use a particle trajectory estimator that
discriminates between particles based on their transverse momentum (pT ).
Particles with pT â„ 2 GeV/c will be transmitted to the Level-1 Track-Trigger
system for trajectory reconstruction within a fixed 3 ÎŒs latency. This thesis
presents a novel Hardware-based Multivariate Linear Fitter (MVLF) system
focusing on robustness in tracking efficiency and reduction in logic resource
usage within the specified latency. The system components are implemented in
Field Programmable Gate Arrays (FPGA), targeting 16 nm FinFET UltraScale+
silicon technology. The development was performed using the High-Level
Synthesis (HLS) automation tools and the Hardware acceleration platform for
Application-Specific Integrated Circuits (ASIC). A firmware demonstrator has
been assembled to verify the feasibility and compatibility of the scaled system
with the CMS Level-1 Track-Trigger infrastructure. The systemâs performance is
compared to past and current system developments, and the results are
presented accordingly
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