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Causality plays an important role in qualitative reasoning about physical systems. In this paper we show that the bond-graph method can be f ru i t fu l ly applied to represent and generate causal order on a formal basis. Both physical and computational aspects of bond-graph causality are discussed. In particular we show that it provides a (inner physical(More)
Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is(More)
One of the major challenges in computer vision is to create automated systems that perform tasks with at least the same competences as human experts. In particular for automated inspection of natural objects this is not easy to achieve. The task is hampered by large inclass variations and complex 3D-morphology of the objects and subtle argumentations of(More)
Collaboration in science requires a shared model of underlying workflows and concepts. In addition to leveraging information exchange between scientists, the shared model should enable automated invocation of computational (numerical) methods from a conceptual level. In this way, the model fills the gap between humans interpreting textual information and(More)
The ever increasing amount of data gathered by more growers in more years offers possibilities to add value. Therefore—for interested parties and stakeholders—a common and controlled vocabulary of the potato domain that describes concepts, attributes, and the relations between them in a formal way using a standardised knowledge representation language is(More)
  • Jan-Erik Strr, Jan Top, Ulf Ss Oderman
  • 1993
Current approaches to the problem of switching between modes in continuous dynamic system models tend to confound modelling and computation. Here we introduce an alternative approach ensuring a clear distinction between the physical and computational levels. Our method is centered around the idea that the variability of causal directions should be accepted(More)