Science

Publication Search Results

Now showing 1 - 10 of 227
  • (1998) Corkish, Richard; Sproul, Alistair; Puzzer, Tom; Altermatt, Peter; Heiser, Gernot; Luke, Keung
    Conference Paper

  • (1997) Corkish, Richard; Puzzer, Tom; Sproul, Alistair; Luke, Keung; Heiser, Gernot
    Conference Paper

  • (1996) Sproul, Alistair; Edminston, Sean; Puzzer, Tom; Heiser, Gernot; Wenham, Stuart; Green, Martin; Young, Timothy
    Conference Paper
    An analytical model is developed to decribe recombination currents arising from recombination at grain boundaries (GBs) in the depletion region of a p-n junction solar cell. Grain boundaries are modelled as having a single energy evel in the energy gap, and partial occupancy of these stats gives raise to a chage on the GB. The analytical model is compared to a complete numerical simulation package (DESSIS) and found to be in excellent agreement. Additionally,. cross sectional EBIC images of a multilayer device containing vertical GBs are presented. The experimental data is comared qualitatively with results derived from numerical modelling.

  • (2007) Laffan, Shawn; Lubarsky, Eugene; Ward, M; Highfield, L
    Conference Paper
    Management response to contagious disease outbreaks requires rapid application of appropriate control measures (for example, quarantine and vaccination), but with little current or empirical data about how the disease may spread. This means that modelling is one of the few available options for making important decisions, and geographical modelling is a key approach. While there are many models that deal with disease spread within livestock populations, there are few that deal explicitly with widely dispersed animal populations such as unfenced livestock and wild and feral animals. It is for this purpose that we have developed a geographic automata model, Sirca, to simulate possible outbreaks of disease in wild and unfenced animal populations.

  • (2006) Lees, Brian; Van Niel, Kimberley; Laffan, Shawn
    Conference Paper

  • (2005) Laffan, Shawn; Bickford, S
    Conference Paper
    Geographically Weighted Regression (GWR) and its variants are analysis methods that can cope with the multi-scale, spatially non-stationary relationships common to spatial data. They achieve this by using geographical sub-samples of the data for which one expects the complexity of any relationships to be simpler than over the whole study area.

  • (2003) Hegland, Markus; Laffan, Shawn
    Conference Paper

  • (2003) Laffan, Shawn; Silcock, Howard; Nielsen, Ole; Hegland, Markus
    Conference Paper
    We describe in this paper a new data mining approach for the analysis of spatial data for environmental modelling. The sparse grids analysis system models the functional relationship between a set of predictor variables and a response variable by using a combination of easily computable functions defined on grids of varying mesh sizes in attribute space. The approach circumvents the so-called “curse of dimensionality” by using, instead of a costly high-dimensional grid a with a fine mesh size in every dimension, a collection of grids that are coarse along some dimensions but fine along others. Adaptive sparse grid regression and classification methods select combinations of grids that suit a particular data set. One advantage of the sparse grids approach from an environmental analysis perspective is that it uses machine learning approaches, and so can deal with correlated data, as are common in environmental problems. One advantage of the sparse grids approach from an environmental analysis perspective is that it uses machine learning approaches, and so can deal with correlated data, as is commonly the case with geographic data. They also require fewer degrees of freedom than do full grid models, allowing them to be applied to more datasets. The parameters defining the adaptive sparse grids can be used to interpret relationships in terms of scale and resolution. For example, the distribution of mesh points used in the set of lattices describes the complexity of the relationships present. It can be used to understand if the system is responding to fine scale variations (many mesh points used) or to gross patterns (few mesh points used). This is valuable information for environmental modelling.

  • (1997) Gong, S; Bandyopadhyay, Srikanta
    Conference Paper

  • (1996) Leung, S; Stevens, Gaye; Bandyopadhyay, Srikanta; Sorrell, Charles
    Conference Paper