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Record W1547520917 · doi:10.1109/whc.2015.7177727

Dynamics of coupled haptic systems

2015· article· en· W1547520917 on OpenAlexafffund
László L. Kovács, József Kövecses

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyComputer scienceFlexibility (engineering)Operator (biology)Control theory (sociology)Mechanical impedanceParametric statisticsElectrical impedanceSystem dynamicsSimulationEngineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The sampled-data nature and time delays induce complex dynamic behaviours in haptic systems which makes the stable, high performance force feedback challenging. Physiological factors, such as reflex delay, and the variable impedance of the human operator further complicate the modeling and analysis of these systems. Often the operator is neglected and only the dynamics of the uncoupled haptic device is investigated. When considered, the human model is typically represented by passive impedance elements attached to the device; most studies employ only simple mass-spring-damper representations. The dynamics of coupled systems with multiple degrees-of-freedom device- and human operator models have not been much investigated. In the present paper, we discuss reduced order, parametric dynamic representations for such complex models. We also consider the coupled system, and demonstrate the effect of the human operator on the combined dynamics. Structural flexibility, different grasping conditions, and active human stabilization with reflex delay are considered. The results are validated and illustrated experimentally by using a haptic device based on a five-bar mechanism.

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 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 score0.670
Threshold uncertainty score0.174

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)0.0000.000

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 head0.019
GPT teacher head0.212
Teacher spread0.193 · 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.

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

Citations6
Published2015
Admission routes2
Has abstractyes

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