MétaCan
Menu
Back to cohort
Record W2547505576 · doi:10.1109/iecon.2006.348031

A new resonant gate drive circuit utilizing leakage inductance of transformer

2006· article· en· W2547505576 on OpenAlexaff
Kai Xu, Yan‐Fei Liu, P.C. Sen

Bibliographic record

VenueProceedings of the Annual Conference of the IEEE Industrial Electronics Society · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsLeakage inductanceDuty cycleInductanceRLC circuitWaveformElectronic circuitGate driverElectrical engineeringTransformerElectronic engineeringMOSFETConvertersResonant inverterEngineeringVoltageCapacitorTransistorInverter

Abstract

fetched live from OpenAlex

This paper presents a new resonant gate drive circuit that utilizes transformer leakage inductance as resonant component. The new gate driver circuit can drive a pair of MOSFETs, which can be used in both with same reference and different reference circuits such as MOSFETs in buck, bridge converters. Pulse controlled resonance is used to reduce the conduction loss of resonant circuit. Also it provides fast transition, thus the limitation on the duty cycle is minimized. In the paper, the theoretical analysis and simulation waveforms are provided. A prototype board is built and experimental results are also given

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 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: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.211
Teacher spread0.186 · 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.

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

Citations9
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of the IEEE Industrial Electronics SocietySame topicAdvanced DC-DC ConvertersFrench-language works237,207