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Record W2942302475 · doi:10.1049/joe.2018.8073

Three‐phase fixed‐frequency interleaved (LC)(L)‐type series‐resonant converter with a capacitive output filter

2019· article· en· W2942302475 on OpenAlexafffund
M. Almardy, Ashoka K. S. Bhat

Bibliographic record

VenueThe Journal of Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWaveformVoltageControl theory (sociology)LC circuitCapacitive sensingFilter (signal processing)Series (stratigraphy)Boost converterBuck–boost converterĆuk converterForward converterComputer scienceElectronic engineeringCapacitorEngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

A three‐phase interleaved (LC)(L)‐type dc–dc series‐resonant converter with a capacitive output filter using fixed frequency control is proposed. The converter operations for different modes during different intervals have been presented using the operating waveforms. The converter is analysed using the approximate analysis approach, and the design procedure is illustrated with a design example. PSIM simulation results for the designed converter are given for load and input voltage variations. It is shown that the converter operates in zero‐voltage switching (ZVS) at the minimum input voltage and for different load conditions. On the other hand, at the maximum input voltage from full load to light load, leading switches operate with ZVS, whereas lagging switches operate with zero‐current switching (ZCS).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.195
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 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

Citations5
Published2019
Admission routes2
Has abstractyes

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