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Record W2162338748 · doi:10.1109/haptics.2014.6775517

Uncoupled stability analysis of haptic simulation systems for various kinematic sampled data and discretization methods

2014· article· en· W2162338748 on OpenAlexaff
Siyuan Yin, Ajay Koti, Amir Haddadi, Keyvan Hashtrudi-Zaad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsDiscretizationHaptic technologyKinematicsControl theory (sociology)Stability (learning theory)Computer scienceGRASPInvariant (physics)SIGNAL (programming language)SimulationMathematicsArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

Previous research has shown that the stability of haptic simulation systems is largely affected by the type of signals sampled and the discretization method used for implementing the virtual environment. In this paper, we analytically derive and experimentally evaluate the uncoupled stability of haptic simulation systems, that is when these systems are not being held by any operator, for various conditions. These stability conditions are expected to be the most stringent ones, as operators' grasp tend to stabilize the coupled system. Our evaluation includes cases in which position, velocity or both signals are sampled, the backward difference or Tustin methods are used to implement a linear-time-invariant damper-spring environment. Our results show that sampling the velocity signal will significantly increase the range of environment dynamics that can be stably implemented, particularly when the backward difference method is applied as the discretization method.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.060
GPT teacher head0.338
Teacher spread0.278 · 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
GenreMethods

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

Citations13
Published2014
Admission routes1
Has abstractyes

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