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Record W1480444069 · doi:10.1109/ias.1990.152262

Selecting stepped reference waveforms for PWM inverter drives to minimize the current distortion

2002· article· en· W1480444069 on OpenAlexafffund
John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsDistortion (music)WaveformPulse-width modulationCurrent (fluid)Modulation (music)Sine waveEmphasis (telecommunications)Computer scienceFundamental frequencyPhase distortionSampling (signal processing)InverterElectronic engineeringControl theory (sociology)MathematicsAlgorithmPhysicsEngineeringElectrical engineeringTelecommunicationsAcousticsBandwidth (computing)VoltageRadarArtificial intelligence

Abstract

fetched live from OpenAlex

Four modulation techniques that use three steps per 90 degrees of the fundamental cycle are analyzed. The functional relationships between each of the steps, determined by a current distortion minimization procedure, are shown to change with the carrier frequency and the magnitude of the fundamental. The lowest current distortion characteristics are obtained by selecting modulating techniques to cover specified ranges of the fundamental magnitude. Stepped waveforms are shown to be competitive with sinewave regular sampling techniques, and near-optimal performance can be obtained at low carrier frequencies. Operation at very high fundamental magnitudes is possible with minimal current distortion and well-defined switching periods. Theoretical developments are discussed which form the basis for specifying three-phase stepped PWM techniques that exhibit low current distortion levels.>

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.046
GPT teacher head0.238
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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 routes2
Has abstractyes

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