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Record W2767453544 · doi:10.1109/toh.2017.2768526

Analog Haptic Control: Advantages and Challenges

2017· article· en· W2767453544 on OpenAlexfundno aff
Ryan Schindeler, Keyvan Hashtrudi-Zaad

Bibliographic record

VenueIEEE Transactions on Haptics · 2017
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyStability (learning theory)Computer scienceRange (aeronautics)Controller (irrigation)Frequency domainVirtual realityDomain (mathematical analysis)DecompositionAnalog computerControl theory (sociology)Control engineeringSampling (signal processing)Virtual machineSimulationEngineeringControl (management)Artificial intelligenceComputer visionMathematics

Abstract

fetched live from OpenAlex

Haptic simulation systems, which typically implement virtual environments in the discrete-time domain, present an inherent trade-off between stability, sampling frequency, and the range of implementable environment dynamics. Previous research has demonstrated the potential of analog feedback for expanding the range of environment dynamics that result in a stable haptic interaction. In this paper, the effect of various system parameters on the environment dynamic range is analytically and experimentally investigated in the sense of uncoupled stability. In addition, Multilayer Decomposition, which enables a simple analog PD controller to implement nonhomogeneous or multilayer virtual environment dynamics, will be presented and evaluated.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.033
GPT teacher head0.241
Teacher spread0.208 · 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 designTheoretical or conceptual
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

Citations8
Published2017
Admission routes1
Has abstractyes

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