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Record W2947467708 · doi:10.1109/apec.2019.8722291

A new non-multi-level structured, H-bridgeless DC/DC bidirectional converter with low voltage stress and complete soft-switching operation

2019· article· en· W2947467708 on OpenAlexaff
Reza Emamalipour, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
Fundersnot available
KeywordsVoltageDiodeBuck converterElectronic engineeringElectrical engineeringComputer scienceBoost converterMaterials scienceEngineering

Abstract

fetched live from OpenAlex

A new soft-switched, 5-switch string-structured bidirectional DC/DC converter with an isolated CLLC resonant circuit is presented in this paper for energy storage applications. In the proposed circuit, only one switch and two diodes are added to the conventional 4-switch string structured converter enabling the converter to operate in rectifying mode with lower output voltage ripples and higher efficiency. A CLLC resonant circuit is designed to allow stepping-up or stepping-down the voltage to desired value with the same operating frequency in both boost and buck modes with variable frequency control. All the switches and diodes are able to achieve soft-switching to enhance the circuit efficiency. Simulation results are given on a 1kW, 100VDC/300VDC system to highlight the merits of the proposed converter. Preliminary experimental results are also provided on a laboratory scale 100/300VDC prototype to further support the features of this work.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.010
GPT teacher head0.208
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
GenreMethods

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

Citations6
Published2019
Admission routes1
Has abstractyes

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