This paper introduces some new and novel concepts to classify and evaluate freeform surfaces, including a structured neighbourhood signature fitting method for smooth freeform surfaces and a tessellation technique to identify and characterise micro-structured surfaces.
In the following sections, we present such a simulation-based attempt at developing concepts to classify transient, non-autonomous behaviours.
Our approach is supported by a cooperation between Formal Concept Analysis, to classify, infer and prune discovered concepts, and Semantic Query Expansion, to leverage the hierarchical structure of ontologies.
It is possible that memory errors are an inevitable by-product of our adaptive memories and that semantic false memories are specifically connected to our ability to learn rules and concepts and to classify objects by category memberships.
It is therefore possible that memory errors are an inevitable fluke of a powerful, adaptive cognitive phenomenon, in the case of semantic false memories our ability to learn rules and concepts, and to classify novel objects by category memberships (Carey, 2011; Chittka & Jensen, 2011).
Based on the results of the analysis of a well validated CFD CSD model system and large-scale physical model test data, a new concept, named load eccentricity concept, is proposed to classify the response of the foundation in four load eccentricity regimes.
The annotators were given the concept strings and asked to classify each concept into one or more of the broad class es), which they considered appropriate.
Specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned.
The shakedown concept was used to classify the deformation behavior of the materials.
In the first part of research the shakedown concept was used to classify the mechanical response of the granular mixtures.
