105,451 research outputs found
A Parallel High-Order Fictitious Domain Approach for Biomechanical Applications
The focus of this contribution is on the parallelization of the Finite Cell Method (FCM) applied for biomechanical simulations of human femur bones. The FCM is a high-order fictitious domain method that combines the simplicity of Cartesian grids with the beneficial properties of hierarchical approximation bases of higher order for an increased accuracy and reliablility of the simulation model. A pre-computation scheme for the numerically expensive parts of the finite cell model is presented that shifts a significant part of the analysis update to a setup phase of the simulation, thus increasing the update rate of linear analyses with time-varying geometry properties to a range that even allows user interactive simulations of high quality. Paralellization of both parts, the pre-computation of the model stiffness and the update phase of the simulation is simplified due to a simple and undeformed cell structure of the computation domain. A shared memory parallelized implementation of the method is presented and its performance is tested for a biomedical application of clinical relevance to demonstrate the applicability of the presented method
A review of data visualization: opportunities in manufacturing sequence management.
Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application
The Need to Support of Data Flow Graph Visualization of Forensic Lucid Programs, Forensic Evidence, and their Evaluation by GIPSY
Lucid programs are data-flow programs and can be visually represented as data
flow graphs (DFGs) and composed visually. Forensic Lucid, a Lucid dialect, is a
language to specify and reason about cyberforensic cases. It includes the
encoding of the evidence (representing the context of evaluation) and the crime
scene modeling in order to validate claims against the model and perform event
reconstruction, potentially within large swaths of digital evidence. To aid
investigators to model the scene and evaluate it, instead of typing a Forensic
Lucid program, we propose to expand the design and implementation of the Lucid
DFG programming onto Forensic Lucid case modeling and specification to enhance
the usability of the language and the system and its behavior. We briefly
discuss the related work on visual programming an DFG modeling in an attempt to
define and select one approach or a composition of approaches for Forensic
Lucid based on various criteria such as previous implementation, wide use,
formal backing in terms of semantics and translation. In the end, we solicit
the readers' constructive, opinions, feedback, comments, and recommendations
within the context of this short discussion.Comment: 11 pages, 7 figures, index; extended abstract presented at VizSec'10
at http://www.vizsec2010.org/posters ; short paper accepted at PST'1
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
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