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Record W4293094856 · doi:10.1109/aim52237.2022.9863311

Uncoupled Stability of Kinesthetic Haptic Systems Simulating Mass-Damper-Spring Environments with Complementary Filter

2022· article· en· W4293094856 on OpenAlexafffund
Leonam Pecly, Keyvan Hashtrudi-Zaad

Bibliographic record

Venue2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) · 2022
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyKinesthetic learningAccelerationControl theory (sociology)DamperVirtual workStiffnessSpring (device)Position (finance)SimulationComputer scienceWork (physics)Stability (learning theory)PhysicsEngineeringStructural engineeringMechanical engineeringArtificial intelligenceClassical mechanicsMathematicsFinite element method

Abstract

fetched live from OpenAlex

Uncoupled stability, the condition by which the user is not in contact with the haptic device, is arguably a stringent stability condition for haptic simulation systems. Uncoupled stability of haptic systems simulating linear mass-spring or viscoelastic virtual environments have been analyzed. In this paper, we analytically and experimentally evaluate uncoupled stability for simulating mass-damper-spring virtual environments when only position or when position and velocity are available. In addition, the effect of using a linear combination of position and velocity in deriving acceleration estimate is also studied. Experimental results in a one degree-of-freedom device showed that the highest stiffness values are obtained when the acceleration is equally derived from position and velocity. This work will shed light on the interaction of the three dynamic components for virtual environment rendering.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
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 science0.0010.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.254
Teacher spread0.217 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

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