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Record W2045304915 · doi:10.1109/ichqp.2014.6842814

Modeling and prediction of conducted EMI noise in a 2-stage interleaved boost DC/DC converter

2014· article· en· W2045304915 on OpenAlexaff
Amir Tahavorgar, John E. Quaicoe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsElectromagnetic interferenceEMITopology (electrical circuits)Noise (video)Electronic engineeringConducted electromagnetic interferenceCommon-mode signalComputer scienceElectrical engineeringEngineeringDigital signal processing

Abstract

fetched live from OpenAlex

Conducted electromagnetic interference (EMI) noise for a 2-stage interleaved DC/DC boost converter is investigated in this paper. Both differential mode (DM) noise and common mode (CM) noise are studied, taking into account all parasitic components. Using frequency domain approach, a noise prediction model for the interleaved topology is developed. The results from the model are compared with those of the non-interleaved topology to characterize the conducted EMI noise caused by doubling the number of high frequency power switches and switching frequency reduction in the interleaved topology. It is shown that the magnitude of the DM and CM noise in the interleaved topology is less than in the non-interleaved topology, especially at high frequencies in the spectrum of the conducted noise.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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
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

Citations13
Published2014
Admission routes1
Has abstractyes

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