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Record W3048363112 · doi:10.1109/tie.2020.3013538

Asymmetric ZVS Buck Converters With High-Step-Down Conversion Ratio

2020· article· en· W3048363112 on OpenAlexaff
Marziyeh Hajiheidari, Hosein Farzanehfard, Morteza Esteki

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConvertersInductorCapacitorBuck converterVoltageElectronic engineeringDiodeTopology (electrical circuits)Materials scienceComputer scienceControl theory (sociology)Electrical engineeringEngineering

Abstract

fetched live from OpenAlex

This article proposes a family of asymmetric high-step-down converters in which zero voltage switching (ZVS) is achieved and common ground between the input and the output is retained. The proposed converters are derived from the multiphase coupled-buck converter and both coupled inductors and series capacitors are employed in order to achieve a high-step-down conversion ratio. Therefore, the voltage stress across the freewheeling diodes and the current stress of the main switches are reduced. Furthermore, switching losses including turn-on, turn-off, capacitive turn-on, and reverse recovery losses are considerably reduced due to the soft switching of semiconductor devices. As a result, the converter can efficiently operate at higher switching frequencies. Also, in comparison to the conventional interleaved buck converter (IBC) the cost, volume, weight, and complexity of the proposed topologies have not increased significantly as the proposed converters use the minimum number of auxiliary components and, similar to the conventional IBC, share the common ground. The validity of this research is confirmed by the experimental results of a 150 V to 12 V/15 A prototype.

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 score0.007

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 communication0.0010.001
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 head0.015
GPT teacher head0.195
Teacher spread0.179 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

Explore more

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