36,762 research outputs found
Robustness surfaces of complex networks
Despite the robustness of complex networks has been extensively studied in
the last decade, there still lacks a unifying framework able to embrace all the
proposed metrics. In the literature there are two open issues related to this
gap: (a) how to dimension several metrics to allow their summation and (b) how
to weight each of the metrics. In this work we propose a solution for the two
aforementioned problems by defining the -value and introducing the concept
of \emph{robustness surface} (). The rationale of our proposal is to
make use of Principal Component Analysis (PCA). We firstly adjust to 1 the
initial robustness of a network. Secondly, we find the most informative
robustness metric under a specific failure scenario. Then, we repeat the
process for several percentage of failures and different realizations of the
failure process. Lastly, we join these values to form the robustness surface,
which allows the visual assessment of network robustness variability. Results
show that a network presents different robustness surfaces (i.e., dissimilar
shapes) depending on the failure scenario and the set of metrics. In addition,
the robustness surface allows the robustness of different networks to be
compared.Comment: submitted to Scientific Report
Investigation of Air Transportation Technology at Princeton University, 1989-1990
The Air Transportation Technology Program at Princeton University proceeded along six avenues during the past year: microburst hazards to aircraft; machine-intelligent, fault tolerant flight control; computer aided heuristics for piloted flight; stochastic robustness for flight control systems; neural networks for flight control; and computer aided control system design. These topics are briefly discussed, and an annotated bibliography of publications that appeared between January 1989 and June 1990 is given
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
A framework for assessing robustness of water networks and computational evaluation of resilience.
Arid regions tend to take careful measures to ensure water supplies are secured to consumers, to help provide the basis for further development. Water distribution network is the most expensive part of the water supply infrastructure and it must maintain performance during unexpected incidents. Many aspects of performance have previously been discussed separately, including reliability, vulnerability, flexibility and resilience. This study aimed to develop a framework to bring together these aspects as found in the literature and industry practice, and bridge the gap between them.
Semi-structured interviews with water industry experts were used to examine the presence and understanding of robustness factors. Thematic analysis was applied to investigate these and inform a conceptual framework including the component and topological levels. Robustness was described by incorporating network reliability and resiliency. The research focused on resiliency as a network-level concept derived from flexibility and vulnerability.
To utilise this new framework, the study explored graph theory to formulate metrics for flexibility and vulnerability that combine network topology and hydraulics. The flexibility metric combines hydraulic edge betweenness centrality, representing hydraulic connectivity, and hydraulic edge load, measuring utilised capacity. Vulnerability captures the impact of failures on the ability of the network to supply consumers, and their sensitivity to disruptions, by utilising node characteristics, such as demand, population and alternative supplies. These measures together cover both edge (pipe) centric and node (demand) centric perspectives.
The resiliency assessment was applied to several literature benchmark networks prior to using a real case network. The results show the benefits of combining hydraulics with topology in robustness analysis. The assessment helps to identify components or sections of importance for future expansion plans or maintenance purposes. The study provides a novel viewpoint overarching the gap between literature and practice, incorporating different critical factors for robust performance
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