35,280 research outputs found
Using polyhedral models to automatically sketch idealized geometry for structural analysis
Simplification of polyhedral models, which may incorporate large numbers of faces and nodes, is often required to reduce their amount of data, to allow their efficient manipulation, and to speed up computation. Such a simplification process must be adapted to the use of the resulting polyhedral model. Several applications require simplified shapes which have the same topology as the original model (e.g. reverse engineering, medical applications, etc.). Nevertheless, in the fields of structural analysis and computer visualization, for example, several adaptations and idealizations of the initial geometry are often necessary. To this end, within this paper a new approach is proposed to simplify an initial manifold or non-manifold polyhedral model with respect to bounded errors specified by the user, or set up, for example, from a preliminary F.E. analysis. The topological changes which may occur during a simplification because of the bounded error (or tolerance) values specified are performed using specific curvature and topological criteria and operators. Moreover, topological changes, whether or not they kept the manifold of the object, are managed simultaneously with the geometric operations of the simplification process
The Design and Implementation of a Bayesian CAD Modeler for Robotic Applications
We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. To solve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity.
Using two examples, we show how to apply our approach by providing simulation results using our modeler
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
DGD Gallery: Storage, sharing, and publication of digital research data
We describe a project, called the "Discretization in Geometry and Dynamics
Gallery", or DGD Gallery for short, whose goal is to store geometric data and
to make it publicly available. The DGD Gallery offers an online web service for
the storage, sharing, and publication of digital research data.Comment: 19 pages, 8 figures, to appear in "Advances in Discrete Differential
Geometry", ed. A. I. Bobenko, Springer, 201
From Big Data to Big Displays: High-Performance Visualization at Blue Brain
Blue Brain has pushed high-performance visualization (HPV) to complement its
HPC strategy since its inception in 2007. In 2011, this strategy has been
accelerated to develop innovative visualization solutions through increased
funding and strategic partnerships with other research institutions.
We present the key elements of this HPV ecosystem, which integrates C++
visualization applications with novel collaborative display systems. We
motivate how our strategy of transforming visualization engines into services
enables a variety of use cases, not only for the integration with high-fidelity
displays, but also to build service oriented architectures, to link into web
applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho
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