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Record W1989303686 · doi:10.1115/detc2013-13411

Virtual Contact Dynamics Behaviour in Haptic Interfacing

2013· article· en· W1989303686 on OpenAlexaff
Sara Shayan Amin, László L. Kovács, József Kövecses

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsInterfacingHaptic technologyComputer scienceContact forceParametric statisticsSimulationComputer hardware

Abstract

fetched live from OpenAlex

The key element in haptic interfacing is the virtual contact representation that connects the virtual environment to the human via the haptic device. The properties of virtual contact can determine the quality of the haptic interfacing. In this work, we particularly investigate how the mechanical and structural properties can influence the virtual contact behaviour. To this end, representative models are incorporated, where the effective parameters are directly connected to physical and structural properties of the system. Virtual contact behaviour is then investigated via parametric stability analysis, and corresponding spectral radius of the transition matrix is selected as a performance indicator. It is shown that structural flexibility plays an important role in the quality of the haptic interfacing. As an example, rendering virtual walls with different impedances are considered and for each case the optimum effective parameters of the model is obtained for fidelity improvement.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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 teacher head, not a consensus.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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