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Record W2098249351 · doi:10.1109/acc.2002.1025263

Current harmonic detection for induction motor speed sensorless control using fast orthogonal search

2002· article· en· W2098249351 on OpenAlexaff
K. Nutt, Mohammed Tarbouchi, D. McGaughey, A.Y. Chikhani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsWaveformHarmonic analysisHarmonicComputer scienceInduction motorDigital signal processingEstimatorCurrent (fluid)Resolution (logic)Electronic engineeringFast Fourier transformFourier transformDiscrete Fourier transform (general)AlgorithmControl theory (sociology)Fourier analysisControl (management)VoltageMathematicsComputer hardwareEngineeringArtificial intelligenceShort-time Fourier transformElectrical engineeringAcousticsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper describes the design of an improved method of detection of harmonic current components in AC machines. A fully digital implementation of the speed estimator requires the use of DSP routines to perform a spectral analysis of the sampled current waveform and extract harmonic information. Conventional methods using Fast Fourier Transforms (FFTs) are limited by the frequency resolution and the minimum number of samples required to obtain this resolution. The Fast Orthogonal Search (FOS) alleviates these restrictions, which makes it suitable for use in this type of application.

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 categoriesInsufficient payload (model declined to judge)
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 score0.466
Threshold uncertainty score0.999

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)0.0020.000

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 head0.087
GPT teacher head0.289
Teacher spread0.203 · 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.

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

Citations4
Published2002
Admission routes1
Has abstractyes

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