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Record W2343332554 · doi:10.1109/tpel.2015.2512586

Load-Source Matching with Dielectric Isolation in High Frequency Switch Mode Power Supplies

2015· article· en· W2343332554 on OpenAlexaff
Adrian Z. Amanci, Harry E. Ruda, F.P. Dawson

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIsolation transformerTransformerDelta-wye transformerDistribution transformerEnergy efficient transformerElectronic engineeringTransformer typesTopology (electrical circuits)Flyback transformerCapacitive sensingLeakage inductanceInductorElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

This paper presents a new hybrid passive power device, denoted as the hybrid transformer, which is an improved alternative to the classical two-winding high-frequency transformer (100's kHz-MHz). The hybrid transformer is comprised of a coupled inductor topology, that provides load-source matching, denoted as the mutual branch topology, and a dielectric isolation device denoted as the multilayer capacitive isolation device. The main focus of this paper is on the design and operation of the mutual branch topology. Simulation results are shown to highlight the improved efficiency of the hybrid transformer over a conventional high-frequency transformer. Finally, the efficiency of the hybrid transformer is compared with that of a classical two-winding transformer using the same resonant converter, highlighting the improved efficiency of the hybrid transformer (up to 10% improved efficiency for light loads).

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 science0.0010.001
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 head0.005
GPT teacher head0.203
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

Citations6
Published2015
Admission routes1
Has abstractyes

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