Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator

Shewchuk, Jonathan Richard

doi:10.1007/bfb0014497

Cited by 1,667 publications

(1,285 citation statements)

References 13 publications

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“…We therefore use an a posteriori error estimate, which is used to determine whether refinement of the mesh is required. Starting with an initially rather coarse quality mesh (Shewchuk 1996), we refine according to this estimator and adapt the mesh to give an accurate solution.…”

Section: Methodsmentioning

confidence: 99%

Adaptive mesh refinement techniques for electrical impedance tomography

et al. 2001

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Adaptive mesh refinement techniques can be applied to increase the efficiency of electrical impedance tomography reconstruction algorithms by reducing computational and storage cost as well as providing problem-dependent solution structures. A self-adaptive refinement algorithm based on an a posteriori error estimate has been developed and its results are shown in comparison with uniform mesh refinement for a simple head model.

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Section: Methodsmentioning

confidence: 99%

Adaptive mesh refinement techniques for electrical impedance tomography

et al. 2001

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“…For this example, the workspace is partitioned in 132 discrete regions by Triangle [27] and the size of the intervals, i.e., ∆τ, is 0.5. This discretisation produces a product BMDP P with 10516 states.…”

Section: Examplementioning

confidence: 99%

Sampling-based stochastic optimal control with metric interval temporal logic specifications

Montana

Dodd

2016

2016 IEEE Conference on Control Applications (CCA)

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© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Sampling-based Stochastic Optimal Control with Metric Interval Temporal Logic SpecificationsFelipe J. Montana, Jun Liu and Tony J. DoddAbstract-This paper describes a method to find optimal policies for stochastic dynamic systems that maximise the probability of satisfying real-time properties. The method consists of two phases. In the first phase, a coarse abstraction of the original system is created. In each region of the abstraction, a sampling-based algorithm is utilised to compute local policies that allow the system to move between regions. Then, in the second phase, the selection of a policy in each region is obtained by solving a reachability problem on the Cartesian product between the abstraction and a timed automaton representing a real-time specification given as a metric interval temporal logic formula. In contrast to current methods that require a fine abstraction, the proposed method achieves computational tractability by modelling the coarse abstraction of the system as a bounded-parameter Markov decision process (BMDP). Moreover, once the BMDP is created, this can be reused for new specifications assuming the same stochastic system and workspace. The method is demonstrated with an autonomous driving example.

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“…A new mesh taking into account these areas is achieved by the means of constrained triangulation. The latter is performed by the means of the Triangle software (Shewchuk, 1996(Shewchuk, , 2001. This new mesh is the geometrical support of the elements.…”

Section: Poglio T Mathieu-marni S Ranchin T Savaria E Wald Lmentioning

confidence: 99%

OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution

Poglio

Mathieu-Marni²,

Ranchin

et al. 2006

Remote Sensing of Environment

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. OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment, Elsevier, 2006, 104, pp.238-246. <10.1016/j.rse.2006 a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution. Remote Sensing of Environment, 104, 238-246, 2006Environment, 104, 238-246, , doi:10.1016Environment, 104, 238-246, /j.rse.2006 AbstractThis paper describes an infrared image simulator for remote sensing applications, called OSIrIS Examples are given that illustrate specific aspects of infrared images.

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Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator

Cited by 1,667 publications

References 13 publications

Adaptive mesh refinement techniques for electrical impedance tomography

Adaptive mesh refinement techniques for electrical impedance tomography

Sampling-based stochastic optimal control with metric interval temporal logic specifications

OSIrIS: a physically based simulation tool to improve training in thermal infrared remote sensing over urban areas at high spatial resolution

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