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Record W2765521725 · doi:10.1049/iet-epa.2017.0276

Combined commutation optimisation strategy for brushless DC motors with misaligned hall sensors

2017· article· en· W2765521725 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

VenueIET Electric Power Applications · 2017
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsDC motorCommutationHall effect sensorBrushed DC electric motorElectrical engineeringControl theory (sociology)EngineeringComputer scienceAC motorElectric motorMagnetVoltageControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Brushless direct current (BLDC) motors with Hall sensors are widely used in various applications. Installation errors for Hall sensors may lead to inaccuracy regarding the commutation position, which can lower the motor efficiency. To improve the performance of the BLDC motors, this study presents a new combined commutation optimisation strategy for obtaining the ideal commutation position. The new strategy consists of two procedures: averaging the misaligned Hall signals and compensating for the averaged Hall signals. A mathematical relationship between the DC‐link current and overall deviation error was established, and a proportional‐integral controller was built to compensate the commutation position. Several experiments were conducted to verify the effectiveness of the new combined commutation optimisation strategy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.611
Threshold uncertainty score0.814

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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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