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Record W2128176554 · doi:10.1109/haptic.2004.1287205

Haptic simulation of linear elastic media with fluid pockets

2004· article· en· W2128176554 on OpenAlexaff
Andrew H. Gosline, Septimiu E. Salcudean, J. Yan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSolverCompressibilityFluid–structure interactionFinite element methodMechanicsHaptic technologyComputer scienceIterative methodImmersed boundary methodLinear elasticityImaging phantomFluid simulationFluid dynamicsBoundary (topology)SimulationPhysicsMathematicsAlgorithmMathematical analysisOptics

Abstract

fetched live from OpenAlex

A fast technique for simulating fluid pockets enclosed in an elastic body has been developed using the finite element method. By treating fluid pressure as a force boundary condition, the relationship between the volume and pressure of a fluid cavity can be enforced with an iterative solver. This computational approach has been shown to agree with experimental data taken from a gelatin phantom that contains a small fluid pocket. Combining linear methods and condensation techniques with this iterative solver, fast simulation of elastic bodies that include fluid pockets can be achieved. For example, an extension of a two dimensional needle insertion simulation can be carried out at 512Hz for a 24 node incompressible fluid pocket.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.219
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2004
Admission routes1
Has abstractyes

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