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Record W4211071745 · doi:10.1002/cta.3240

High‐gain single‐switch single‐input dual‐output DC‐DC converter with reduced switching stress

2022· article· en· W4211071745 on OpenAlexfundno aff
Bekkam Krishna, P. Uma Maheswar Rao, V. Karthikeyan

Bibliographic record

VenueInternational Journal of Circuit Theory and Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsnot available
FundersFaculty of Graduate Studies and Research, University of Alberta
KeywordsConvertersInductorTopology (electrical circuits)CapacitorDiodeDual (grammatical number)Electronic engineeringComputer scienceVoltageControl theory (sociology)Electrical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract In light‐emitting diode (LED) driver applications, as an alternative of employing two separate conventional DC‐DC converters, single‐input dual‐output converters (SIDO) have excellent performance over traditional topologies. However, SIDO has the limitation of more number switches required, which leads to higher switching losses and deteriorating efficiency. In order to overcome this drawback, this paper presents a novel of a single‐switch single‐input dual‐output DC‐DC converter with a high‐gain ratio. It attains the two boosted outputs with a high‐gain conversion ratio using a single switch by incorporating the switched‐capacitor technique. In addition, the bulkiness of the converter was reduced by utilizing only one inductor and switch. Moreover, it improves efficiency by reduced switching stress and cross‐regulation at output ends. Furthermore, the performance of the converter in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) has been explained in this paper. Finally, a 120‐W hardware setup has been built and tested. The experiment results are presented to validate the merits of the proposed topology.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score0.751

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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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 designTheoretical or conceptual
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

Citations11
Published2022
Admission routes1
Has abstractyes

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