PhD theses from the Department of Computer Science, AU http://hdl.handle.net/2160/395 2013-05-26T03:56:22Z 2013-05-26T03:56:22Z Pugh, Stephen http://hdl.handle.net/2160/5722 2011-02-08T17:23:40Z 2010-04-01T00:00:00Z

Autonomous Science For Future Planetary Exploration Operations Pugh, Stephen A major mission driver for space exploration is to maximise science data return whilst minimising ground-based human intervention and hence associated operations costs. Future robotic exploration such as the ESA ExoMars mission (launch 2018), and the eventual Mars Sample Return (MSR) mission will require rovers to travel further and faster than has been achieved to date. In order to make this possible it is essential that currently earth bound decisions be transferred to the exploration platform wherever possible. In line with this, this Thesis presents a new solution which requires a combined on-Earth and on-board rover approach. The on-board element utilises autonomy and basic image processing techniques to image a predefined number of potential targets. The Earth-based element uses a more complex knowledge based system approach which has been primed by a human Planetary Geology Expert. This Earth based approach, which is used to process the autonomously captured images, is presented as a precursor to a future onboard solution. Both solution elements represent significant advances in the current state of the art. This Thesis provides details of the design, implementation and experimentation undertaken to validate the performance of both the on-board and on-Earth solution elements.

2010-04-01T00:00:00Z Mac Parthaláin, Neil http://hdl.handle.net/2160/3053 2009-09-24T01:00:23Z 2009-09-23T00:00:00Z

Rough Set Extensions for Feature Selection Mac Parthaláin, Neil Rough set theory (RST) was proposed as a mathematical tool to deal with the analysis of imprecise, uncertain or incomplete information or knowledge. It is of fundamental importance to artificial intelligence particularly in the areas of knowledge discovery, machine learning, decision support systems, and inductive reasoning. At the heart of RST is the idea of only employing the information contained within the data, thus unlike many other methods, probability distribution information or assignments are not required. RST relies on the concept of indiscernibility to group equivalent elements and generate knowledge granules. These granules are then used to build a structure to approximate a given concept. This framework has unsurprisingly proven successful for the application to the task of feature selection. Feature selection (FS) is a term given to the problem of selecting input attributes which are most predictive of a given outcome. Unlike other dimensionality reduction methods, feature selection algorithms preserve the original semantics of the features following reduction. This has been applied to tasks which involve datasets that contain huge numbers of features (in the order of tens of thousands), which would be impossible to process otherwise. Recent examples of such problems include text processing and web content classification. FS techniques have also been applied to small and medium-sized datasets in order to discover the most information-rich features. The application of rough sets for FS has resulted in many efficient algorithms. However, due to the granularity of the approximations generated by the rough set approach there is often a resulting level of uncertainty. This uncertainty in information is usually ignored for FS (by nature of the very fact that it is `uncertain'). In this thesis, a number of methods are proposed which attempt to use the uncertain information to improve the performance of rough sets and extensions thereof for the task of FS. These approaches are applied to two application domain problems where the reduction of features is of high importance; mammographic image analysis and complex systems monitoring. The utility of the approaches is demonstrated and compared empirically with several other dimensionality reduction techniques. In several experimental evaluation sections, the approaches are shown to equal or improve classification accuracy when compared to results obtained from unreduced data. Based on the new fuzzy-rough approaches, further developments and techniques are also presented in this thesis. The first of these is the application of a nearest neighbour classifier for the classification of real-valued data. This technique is evaluated within the mammographic imaging application. Also, a novel unsupervised feature selection approach is proposed which reduces features by eliminating those which are considered redundant. Both the fuzzy-rough classifier mentioned above, and UFRFS are employed and evaluated for the complex systems monitoring application.

2009-09-23T00:00:00Z Riley, Michael http://hdl.handle.net/2160/1941 2011-02-28T17:10:37Z 2009-03-01T00:00:00Z

Significant Pattern Discovery in Gene Location and Phylogeny Riley, Michael This thesis documents the investigation into the acquisition of knowledge from biological data using computational methods for the discovery of significantly frequent patterns in gene location and phylogeny. Beginning with an initial statistical analysis of distribution of gene locations in the flowering plant Arabidopsis thaliana, we discover unexplained elements of order. The second area of this research looks into frequent patterns in the single dimensional linear structure of the physical locations of genes on the genome of Saccharomyces cerevisiae. This is an area of epigenetics which has, hitherto, attracted little attention. The frequent patterns are patterns of structure represented in Datalog, suitable for analyses using the logic programming methodology Prolog. This is used to find patterns in gene location with respect to various gene attributes such as molecular function and the distance between genes. Here we find significant frequent patterns in neighbouring pairs of genes. We also discover very significant patterns in the molecular function of genes separated by distances of between 5,000 and 20,000 base pairs. However, in complete contrast to the latter result, we find that the distribution of genes of molecular function within a local region of ±20, 000 base pairs is locationally independent. In the second part of this research we look for significantly frequent patterns of phylogenetic subtrees in a broad database of phylogenetic trees. Here we investigate the use of two types of frequent phylogenetic structures. Firstly, phylogenetic pairs are used to determine relationships between organisms. Secondly, phylogenetic triple structures are used to represent subtrees. Frequent subtree mining is then used to establish phylogenetic relationships with a high confidence between a small set of organisms. This exercise was invaluable to enable these procedures to be extended in future to encompass much larger sets of organisms. This research has revealed effective methods for the analysis of, and has discovered patterns of order in the locations of genes within genomes. Research into phylogenetic tree generation based on protein structure has discovered the requirements for an effective method to extract elements of phylogenetic information from a phylogenetic database and reconstruct a single consensus tree from that information. In this way it should be possible to produce a species tree of life with high degree of confidence and resolution.

2009-03-01T00:00:00Z Bell, Jonathan http://hdl.handle.net/2160/177 2006-08-23T08:09:28Z 2006-01-01T00:00:00Z

Interpretation of simulation for model-based design analysis of engineered systems Bell, Jonathan This thesis attempts to answer the question "Can we devise a language for interpretation of behavioural simulation of engineered systems (of arbitrary complexity) in terms of the systems’ purpose?" It does so by presenting a language that represents a device’s function as achieving some purpose if the device is in a state that is intended to trigger the function and the function’s expected effect is present. While most work in the qualitative and model-based reasoning community has been concerned with simulation, this language is presented as a basis for interpret- ing the results of the simulation of a system, enabling these results to be expressed in terms of the system’s purpose. This, in turn, enables the automatic production of draft design analysis reports using model based analysis of the subject system. The increasing behavioural complexity of modern systems (resulting from the increasing use of microprocessors and software) has led to a need to interpret the results of simulation in cases beyond the capabilities of earlier functional mod- elling languages. The present work is concerned with such cases and presents a functional modelling language that enables these complex systems to be analysed. Specifically, the language presented herein allows functional description and interpretation of the following. • Cases where it is desired to distinguish between partial and complete failure of a function. • Systems whose functionality depends on achieving a sequence of intermittent effects. • Cases where a function being achieved in an untimely manner (typically late) needs to be distinguished from a function failing completely. • Systems with functions (such as warning functions) that depend upon the state of some other system function. This offers significant increases both in the range of systems and of design analysis tasks for which the language can be used, compared to earlier work.

2006-01-01T00:00:00Z