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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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
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 head0.013
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 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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