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Record W2970168243 · doi:10.1109/whc.2019.8816076

Uncoupled Stability of a Haptic System with Position-Velocity Sampling

2019· article· en· W2970168243 on OpenAlexaff
Victor A. Luna Laija, Daniel Cleveland, Keyvan Hashtrudi-Zaad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsHaptic technologyControl theory (sociology)Sampling (signal processing)Position (finance)EncoderInertiaStability (learning theory)Computer scienceAnalogue filterFilter (signal processing)RippleLow-pass filterAnalog computerPotentiometerSimulationEngineeringPhysicsDigital filterArtificial intelligenceComputer visionClassical mechanics

Abstract

fetched live from OpenAlex

In typical haptic simulation systems, sampled position from encoders is utilized to compute the virtual environment force. For dynamic environments, velocity is numerically approximated using sampled position. In this paper, we analytically studied the uncoupled stability of a haptic simulation system when the discrete velocity needed to implement a damper-spring virtual environment came from sampling analog velocity. Since typical analog velocity sensors add inertia to the system or contain ripple, we implemented a high-pass filter to estimate the analog velocity from a potentiometer analog position output. We analytically and experimentally assessed the uncoupled stability for this system for various filter cut-off frequencies and sampling rates.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.199
Teacher spread0.186 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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