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Record W2105964970 · doi:10.1049/iet-pel.2013.0409

A transformerless modular step‐up dc–dc converter for high power applications

2014· article· en· W2105964970 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Power Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsMuscular Dystrophy CanadaUniversity of Toronto
Fundersnot available
KeywordsĆuk converterForward converterModular designBuck–boost converterFlyback converterBoost converterElectronic engineeringCapacitorTransformerBuck converterComputer scienceInductorElectrical engineeringVoltageEngineeringTopology (electrical circuits)

Abstract

fetched live from OpenAlex

This study presents a new step‐up dc–dc converter topology suitable for medium voltage, megawatt scale applications. The proposed converter interconnects unipolar or bipolar dc networks using a single inductor and a modular active switching network. The active switching network contains multiple series‐connected identical converter modules. Each module consists of four switches and one capacitor. The modular nature of the switching network allows scalable implementation of the converter, and utilisation of low‐voltage switches and capacitors. The proposed converter avoids the use of medium‐frequency isolation transformers. Soft switching is utilised to reduce the converter's switching losses. An analytical model of the converter is developed to facilitate converter design. Theoretical predictions are supported by a 7.6 kW scaled laboratory prototype achieving an efficiency of as high as 93.2%.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.204
Teacher spread0.201 · 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