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  • Quantitative analysis of electron-beam-induced-current profiles of grain boundariesin multicrystalline solar cells
    (1998) Corkish, Richard; Sproul, Alistair; Puzzer, Tom; Altermatt, Peter; Heiser, Gernot; Luke, Keung
    Conference Paper
  • Electron-Beam-Induced Current Studies of Grain Boundaries in Solar Cells
    (1997) Corkish, Richard; Puzzer, Tom; Sproul, Alistair; Luke, Keung; Heiser, Gernot
    Conference Paper
  • Grain boundary modelling and characterization of thin-film silicon solar cell
    (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.
  • From Multiple Classification RDR to Configuration RDR
    (1998) Ramadan, Ziad; Byrnes-Preston, Philip; Le-Gia, Thong; Chellen, Vija; Compton, Paul; Mulholland, Mary; Hibbert, D. Brynn; Haddad, Paul; Kang, Byeong
    Conference Paper
    Ripple Down Rules (RDR) is a knowledge acquisition method for knowledge based systems (KBS) which facilitates incremental acquisition of knowledge and ensures that the previous performance of the KBS is not degraded by the incremental addition of the new knowledge. This approach is now well established for single classification tasks and more recently has been extended to multiple classification tasks. This paper describes the further extension of the approach to configuration tasks. The test domain for this study is the configuration of ion chromatography methods in analytical chemistry.
  • Evaluation of Multiple Classification Ripple Down Rules
    (1997) Ramadan, Ziad; Preston, Philip; Thong, Le-Gia; Chellen, Vija; Compton, Paul; Mulholland, Mary; Hibbert, D. Brynn; Kang, Byeong
    Conference Paper
  • Towards an Expert System in Ion Exclusion Chromatography using Multiple classification Ripple Down Rules (MCRDR) IICS'97
    (1997) Ramadan, Ziad; Mulholland, Mary; Hibbert, D. Brynn; Preston, Philip; Compton, Paul; Haddad, Paul
    Conference Paper
  • A trade-off between domain knowledge and problem-solving method power
    (1998) Compton, Paul; Ramadan, Ziad; Preston, P; Le-Gia, Thong; Chellen, Vija; Mullholland, M; Hibbert, D. Brynn; Haddad, Paul; Kang, B
    Conference Paper
    The major focus of recent knowledge acquisition research has been on problem-solving methods (PSM). This paper present results where a PSM developed for classification has been extended to handle a configuration or parametric design task, designing ion chromatography methods in analytical chemistry. Surprisingly good results have been obtained seemingly because any knowledge that has been added to the knowledge base, has been added precisely to overcome any limitations of the PSM. These results suggest a trade-off between domain knowledge and the power of the PSM and that greater use of domain knowledge would facilitate re-use by allowing PSMs to be used for a broader range of tasks.
  • Classifying dry sclerophyll forest from augmented satellite data : Comparing neural network, decision tree and maximum likelihood
    (1995) Milne, Linda; Gedeon, Tom; Skidmore, Andrew
    Conference Paper
    Detailed maps derived from geographical data are becoming increasingly desirable for use in forest management. Many types of data are available for use in generating maps, for example, soil and vegetation maps. We look at a method for giving high level classifications that can be used as additional data for the generation of more detailed maps, and compare the results with other currently used techniques. We use multiple techniques to increase the reliability and accuracy of predictions. We describe a simple method of adjusting the balance of false positive and false negative classifications that are produced by the neural network. This allows better integration with non-neural network techniques.