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Record W2767453544 · doi:10.1109/toh.2017.2768526

Analog Haptic Control: Advantages and Challenges

2017· article· en· W2767453544 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.610

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 head0.033
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