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Record W578153373

Dynamic Compensation of the Effect of Gravity on a Force Illusion Device

2013· article· en· W578153373 on OpenAlexfundno aff
Jacobus W. M. Wever

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2013
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIllusionMechanism (biology)Moment (physics)Compensation (psychology)SimulationOptical illusionComputer scienceControl theory (sociology)Gravitational forceGravity forceReactionComputer visionArtificial intelligencePhysicsMechanicsClassical mechanicsCognitive psychologyControl (management)PsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

A force illusion device is a portable mechanism that generates the illusion of an external force applied on the device. It has potential applicability as a navigation aid for blind or visually impaired people. Force illusion devices that use a cyclic translating mass to generate the illusion have one drawback. Their users will feel a moment caused by gravity acting on this moving mass. It is desired to compensate for this moment since it is likely to disturb the force illusion. This paper proposes an additional mechanism capable of partial moment compensation. A hypocycloid straight-line mechanism is selected from a variety of candidate mechanisms. A prototype of both the force illusion device and additional compensation mechanism is built to enable experimental validation. The dynamic measurements show that the mechanism behaves as is expected from the calculations. The hypocycloid mechanism adds little complexity with only 4 additional axis, yet reduces the gravitational moment with 80%.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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 designBench or experimental
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

Citations0
Published2013
Admission routes1
Has abstractyes

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