MétaCan
Menu
Back to cohort
Record W2134363280 · doi:10.1109/tmag.2006.872422

Simulation of a miniature DC motor: Investigating the use of haptics and sound in detecting ripple torque

2006· article· en· W2134363280 on OpenAlexaff
Y. Ahmad, Ayman Afanah, David A. Lowther

Bibliographic record

VenueIEEE Transactions on Magnetics · 2006
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceHaptic technologyTorqueTorque rippleRippleDC motorSound (geography)SimulationAcousticsHuman–computer interactionElectrical engineeringInduction motorDirect torque controlPhysics

Abstract

fetched live from OpenAlex

The numerical analysis of electromagnetic devices can generate large volumes of data describing the performance of the device. To date, this information has been provided to the user either as sets of numeric data or in graphical form. However, modern personal computers are actually capable of producing other forms of output. Among these are force feedback and sound systems. These are common in computer gaming but have not been explored in any depth in scientific result evaluation. This paper investigates the possible benefits in result understanding which may accrue from using these systems.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.467
Threshold uncertainty score0.273

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 head0.034
GPT teacher head0.241
Teacher spread0.207 · 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
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
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on MagneticsSame topicMusic Technology and Sound StudiesFrench-language works237,207