MétaCan
Menu
Back to cohort
Record W2105040275 · doi:10.1109/tpel.2003.820549

A Zero Voltage Transition Boost Converter Employing a Soft Switching Auxiliary Circuit With Reduced Conduction Losses

2004· article· en· W2105040275 on OpenAlexaff
Nikhil Jain, Praveen Jain, G. Joós

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMcGill UniversityQueen's University
Fundersnot available
KeywordsBoost converterCapacitorTopology (electrical circuits)Ćuk converterPower factorInductorElectronic engineeringBuck–boost converterVoltageElectronic circuitPower (physics)Network topologyEngineeringComputer scienceElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a zero-voltage-transition (ZVT) boost converter using a soft switching auxiliary circuit for power factor correction (PFC) applications. The improvement over existing topologies lies in the positioning of the auxiliary circuit capacitors and the subsequent reduction in the resonant current and therefore the conduction losses as compared to other similar topologies. The proposed converter operates in two modes - Mode 1 and Mode 2. It is shown in the paper that the converter should be designed using the constraints obtained in Mode 1 to achieve low-loss switching. The converter is analyzed and characteristic curves presented which are then used in a detailed design example. Experimental results from a 250 W, 127 V input laboratory prototype switching at 100 kHz verify the design process and highlight the advantages of the proposed topology. The proposed converter is suitable for single-phase, two stage power factor correction circuits with universal input voltage range and power levels up to 3 kW.

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 score0.005

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 communication0.0010.000
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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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

Citations94
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207