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Record W2740218399 · doi:10.1139/tcsme-2015-0017

EXPERIMENTAL VERIFICATION OF A PRACTICAL DIGITAL DRIVER WITH SWITCHED GAIN-TUNING FOR FIVE-PHASE STEPPING-MOTORS

2015· article· en· W2740218399 on OpenAlex
Keisuke Yagi, Noriyuki Hori, Meyer Nahon

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntegratorFeed forwardControl theory (sociology)Loop gainBlock (permutation group theory)RegulatorComputer scienceDiscretizationPhase (matter)Feedback loopLoop (graph theory)Path (computing)EngineeringControl engineeringVoltageMathematicsPhysicsElectrical engineeringBandwidth (computing)Telecommunications

Abstract

fetched live from OpenAlex

A digital driver that has a switched self-tuning gain in its current regulator is designed for five-phase stepping motors so that their performance could be improved and adjusted more easily, than with an analog driver. The regulator has a fixed gain block in its feedback loop and an adjustable gain in the feedforward path, replacing the integrator and the high gain that were required in previous designs to achieve good steady-state performance and fast response. Extensive experiments have been conducted under typical and extreme actuation conditions, and revealed that the proposed driver performs better than the analog drivers or their discretized equivalents, especially in eliminating undershoots, which were problematic with previous drivers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.640

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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