3 resultados para Adaptive object model
em Nottingham eTheses
Resumo:
Portable Document Format (PDF) is a page-oriented, graphically rich format based on PostScript semantics and it is also the format interpreted by the Adobe Acrobat viewers. Although each of the pages in a PDF document is an independent graphic object this property does not necessarily extend to the components (headings, diagrams, paragraphs etc.) within a page. This, in turn, makes the manipulation and extraction of graphic objects on a PDF page into a very difficult and uncertain process. The work described here investigates the advantages of a model wherein PDF pages are created from assemblies of COGs (Component Object Graphics) each with a clearly defined graphic state. The relative positioning of COGs on a PDF page is determined by appropriate "spacer" objects and a traversal of the tree of COGs and spacers determines the rendering order. The enhanced revisability of PDF documents within the COG model is discussed, together with the application of the model in those contexts which require easy revisability coupled with the ability to maintain and amend PDF document structure.
Resumo:
Scalable Vector Graphics (SVG) has an imaging model similar to that of PostScript and PDF but the XML basis of SVG allows it to participate fully, via namespaces, in generalised XML documents.There is increasing interest in using SVG as a Page Description Language and we examine ways in which SVG document components can be encapsulated in contexts where SVG will be used as a rendering technology for conventional page printing.Our aim is to encapsulate portions of SVG content (SVG COGs) so that the COGs are mutually independent and can be moved around a page, while maintaining invariant graphic properties and with guaranteed freedom from side effects and mutual interference. Parellels are drawn between COG implementation within SVG's tree-based inheritance mechanisms and an earlier COG implementation using PDF.
Resumo:
The major function of this model is to access the UCI Wisconsin Breast Cancer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classification can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artificial Immune Systems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to problem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifically for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based modelling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.