168,292 research outputs found
Similarity measures for mid-surface quality evaluation
Mid-surface models are widely used in engineering analysis to simplify the analysis of thin-walled parts, but it can be difficult to ensure that the mid-surface model is representative of the solid part from which it was generated. This paper proposes two similarity measures that can be used to evaluate the quality of a mid-surface model by comparing it to a solid model of the same part. Two similarity measures are proposed; firstly a geometric similarity evaluation technique based on the Hausdorff distance and secondly a topological similarity evaluation method which uses geometry graph attributes as the basis for comparison. Both measures are able to provide local and global similarity evaluation for the models. The proposed methods have been implemented in a software demonstrator and tested on a selection of representative models. They have been found to be effective for identifying geometric and topological errors in mid-surface models and are applicable to a wide range of practical thin-walled designs
What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics
This paper is about enabling robots to improve their perceptual performance
through repeated use in their operating environment, creating local expert
detectors fitted to the places through which a robot moves. We leverage the
concept of 'experiences' in visual perception for robotics, accounting for bias
in the data a robot sees by fitting object detector models to a particular
place. The key question we seek to answer in this paper is simply: how do we
define a place? We build bespoke pedestrian detector models for autonomous
driving, highlighting the necessary trade off between generalisation and model
capacity as we vary the extent of the place we fit to. We demonstrate a
sizeable performance gain over a current state-of-the-art detector when using
computationally lightweight bespoke place-fitted detector models.Comment: IROS 201
Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)
Results of ecological models differ, to some extent, more from measured data than from empirical knowledge. Existing techniques for validation based on quantitative assessments sometimes cause an underestimation of the performance of models due to time shifts, accelerations and delays or systematic differences between measurement and simulation. However, for the application of such models it is often more important to reproduce essential patterns instead of seemingly exact numerical values. This paper presents techniques to identify patterns and numerical methods to measure the consistency of patterns between observations and model results. An orthogonal set of deviance measures for absolute, relative and ordinal scale was compiled to provide informations about the type of difference. Furthermore, two different approaches accounting for time shifts were presented. The first one transforms the time to take time delays and speed differences into account. The second one describes known qualitative criteria dividing time series into interval units in accordance to their main features. The methods differ in their basic concepts and in the form of the resulting criteria. Both approaches and the deviance measures discussed are implemented in an R package. All methods are demonstrated by means of water quality measurements and simulation data. The proposed quality criteria allow to recognize systematic differences and time shifts between time series and to conclude about the quantitative and qualitative similarity of patterns.
Protein Evolution within a Structural Space
Understanding of the evolutionary origins of protein structures represents a
key component of the understanding of molecular evolution as a whole. Here we
seek to elucidate how the features of an underlying protein structural "space"
might impact protein structural evolution. We approach this question using
lattice polymers as a completely characterized model of this space. We develop
a measure of structural comparison of lattice structures that is analgous to
the one used to understand structural similarities between real proteins. We
use this measure of structural relatedness to create a graph of lattice
structures and compare this graph (in which nodes are lattice structures and
edges are defined using structural similarity) to the graph obtained for real
protein structures. We find that the graph obtained from all compact lattice
structures exhibits a distribution of structural neighbors per node consistent
with a random graph. We also find that subgraphs of 3500 nodes chosen either at
random or according to physical constraints also represent random graphs. We
develop a divergent evolution model based on the lattice space which produces
graphs that, within certain parameter regimes, recapitulate the scale-free
behavior observed in similar graphs of real protein structures.Comment: 27 pages, 7 figure
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