3,251 research outputs found
Some Pattern Recognition Challenges in Data-Intensive Astronomy
We review some of the recent developments and challenges posed by the data
analysis in modern digital sky surveys, which are representative of the
information-rich astronomy in the context of Virtual Observatory. Illustrative
examples include the problems of an automated star-galaxy classification in
complex and heterogeneous panoramic imaging data sets, and an automated,
iterative, dynamical classification of transient events detected in synoptic
sky surveys. These problems offer good opportunities for productive
collaborations between astronomers and applied computer scientists and
statisticians, and are representative of the kind of challenges now present in
all data-intensive fields. We discuss briefly some emergent types of scalable
scientific data analysis systems with a broad applicability.Comment: 8 pages, compressed pdf file, figures downgraded in quality in order
to match the arXiv size limi
Supporting 'design for reuse' with modular design
Engineering design reuse refers to the utilization of any knowledge gained from the design activity to support future design. As such, engineering design reuse approaches are concerned with the support, exploration, and enhancement of design knowledge prior, during, and after a design activity. Modular design is a product structuring principle whereby products are developed with distinct modules for rapid product development, efficient upgrades, and possible reuse (of the physical modules). The benefits of modular design center on a greater capacity for structuring component parts to better manage the relation between market requirements and the designed product. This study explores the capabilities of modular design principles to provide improved support for the engineering design reuse concept. The correlations between modular design and 'reuse' are highlighted, with the aim of identifying its potential to aid the little-supported process of design for reuse. In fulfilment of this objective the authors not only identify the requirements of design for reuse, but also propose how modular design principles can be extended to support design for reuse
Understanding Computer Role-Playing Games: A Genre Analysis Based on Gameplay Features in Combat Systems
A game genre as diverse as that of computer role-playing games is difficult to overview. This poses challenges or both developers and researchers to position their work clearly within the genre. We present an overview of the genre based on clustering games with similar gameplay features. This allows a tracing of relations between subgenres through their gameplay, and connecting this to concrete game examples. The analysis was done through using gameplay design patterns to identify gameplay features and focused upon the combat systems in the games. The resulting cluster structure makes use of 321 patterns to create 37 different subgenre classifications based solely on gameplay features. In addition to the clusters, we identify four categories of patterns that help designers and researchers understand the combat systems in computer role-playing games
The development of artificial neural networks for the analysis of market research and electronic nose data
This thesis details research carried out into the application of unsupervised neural
network and statistical clustering techniques to market research interview survey
analysis. The objective of the research was to develop mathematical mechanisms to
locate and quantify internal clusters within the data sets with definite commonality.
As the data sets being used were binary, this commonality was expressed in terms of
identical question answers. Unsupervised neural network paradigms are investigated,
along with statistical clustering techniques. The theory of clustering in a binary space
is also looked at.
Attempts to improve the clarity of output of Self-Organising Maps (SOM) consisted
of several stages of investigation culminating in the conception of the Interrogative
Memory Structure (lMS). IMS proved easy to use, fast in operation and consistently
produced results with the highest degree of commonality when tested against SOM,
Adaptive Resonance Theory (ART!) and FASTCLUS. ARTl performed well when
clusters were measured using general metrics. During the course of the research a
supervised technique, the Vector Memory Array (VMA), was developed. VMA was
tested against Back Propagation (BP) (using data sets provided by the Warwick
electronic nose project) and consistently produced higher classification accuracies.
The main advantage of VMA is its speed of operation - in testing it produced results
in minutes compared to hours for the BP method, giving speed increases in the
region of 100: 1
Visualizing and Interacting with Concept Hierarchies
Concept Hierarchies and Formal Concept Analysis are theoretically well
grounded and largely experimented methods. They rely on line diagrams called
Galois lattices for visualizing and analysing object-attribute sets. Galois
lattices are visually seducing and conceptually rich for experts. However they
present important drawbacks due to their concept oriented overall structure:
analysing what they show is difficult for non experts, navigation is
cumbersome, interaction is poor, and scalability is a deep bottleneck for
visual interpretation even for experts. In this paper we introduce semantic
probes as a means to overcome many of these problems and extend usability and
application possibilities of traditional FCA visualization methods. Semantic
probes are visual user centred objects which extract and organize reduced
Galois sub-hierarchies. They are simpler, clearer, and they provide a better
navigation support through a rich set of interaction possibilities. Since probe
driven sub-hierarchies are limited to users focus, scalability is under control
and interpretation is facilitated. After some successful experiments, several
applications are being developed with the remaining problem of finding a
compromise between simplicity and conceptual expressivity
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