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Record W2048003098 · doi:10.1109/tia.2013.2257973

Intelligent Speed Control of Interior Permanent Magnet Motor Drives Using a Single Untrained Artificial Neuron

2013· article· en· W2048003098 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2013
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsArtificial neural networkArtificial neuronControl engineeringComputer scienceController (irrigation)Set (abstract data type)EngineeringControl theory (sociology)Artificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

This paper presents an intelligent speed controller for the interior permanent magnet synchronous motor based on a single artificial neuron. Traditional artificial neural network-based motor controllers require extensive offline training, which is both time consuming and requires extensive knowledge of motor behavior for the specific drive system. In addition, drive behavior is unpredictable when parameters outside the training set are encountered. The proposed drive system overcomes these limitations by requiring no offline training, is robust under varying operating parameters, and is easily adaptable to various drive systems. Drive efficacy is verified in simulation as well as experimentally.

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 categoriesMeta-epidemiology (narrow)
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 score0.564
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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