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Record W4362584232 · doi:10.3390/en16073194

Double Dual High Step-Up Power Converter with Reduced Stored Energy

2023· article· en· W4362584232 on OpenAlexaff
Héctor R. Robles–Campos, Antonio Valderrábano‐González, Julio C. Rosas‐Caro, Hossam A. Gabbar, Bhanu Babaiahgari

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsOntario Tech University
FundersUniversidad Panamericana
KeywordsInductorBoost converterĆuk converterPower (physics)Duty cycleVoltageBuck–boost converterElectrical engineeringElectronic engineeringDual (grammatical number)Forward converterBuck converterEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper introduces a dual-switchhigh step-up DC-DC power converter. The proposed converter features a high step-up voltage gain, relatively low cumulative stored energy over its inductors, low voltage stress on the active switches, and high efficiency, even at a relatively high duty ratio. An assessment of the proposed converter against conventional boost and a high step-up power converter is presented in terms of steady-state time, voltage gain, stored energy over its inductors, and efficiency. The assessment shows a reduction of 81.25% and 62.5% of stored energy in inductors to comply with the same operational conditions. Simulation and experimental results are provided to validate the benefits of the proposed dual-switch high step-up power converter.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.195
Teacher spread0.187 · 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 designNot applicable
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

Citations7
Published2023
Admission routes1
Has abstractyes

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