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Record W2538379805 · doi:10.1109/pesc.1990.131259

Audible noise reduction for medium power inverters

2002· article· en· W2538379805 on OpenAlexaff
M. Boost, P.D. Ziogas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsConcordia University
Fundersnot available
KeywordsPulse-width modulationGalvanic isolationInverterHarmonicNoise (video)Electronic engineeringComputer sciencePower (physics)Modulation indexModulation (music)ConvertersVoltageElectrical engineeringEngineeringPhysicsAcousticsTransformer

Abstract

fetched live from OpenAlex

An audible noise reduction for medium power inverters which involves the application of a specialized inverter PWM (pulse width modulated) technique combined with a preregulation stage offering an improved inverter output voltage spectrum is proposed. Consequently, inverter switching losses are kept low, while the dominant harmonic generated should not contribute to audible noise measured. Thus, inverter stages previously limited by switching losses can process larger power levels. The carrier-based PWM technique presented here has its first harmonic family at 2.5 times the switching frequency. The technique allows base frequency changes identical to that of sine PWM, yet offers a 15% higher voltage gain. Since the technique is most effective at full modulation index, medium power DC-DC converters capable of inverter input regulation are discussed. Selected experimental results are presented to validate the PWM technique, and a high-frequency preregulation stage is proposed for applications requiring galvanic isolation between source and load.>

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.198
Teacher spread0.185 · 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 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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