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Record W2104938070 · doi:10.1109/ccece.2007.216

Output Voltage Regulation Curves for a 1-Switch Boost Converter Using Coupled Inductor Windings and Split DC-Rails Under Unbalanced Load Conditions

2007· article· en· W2104938070 on OpenAlexaff
John Salmon, Jeffrey Ewanchuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorVoltageElectromagnetic coilInductanceBoost converterCapacitorForward converterControl theory (sociology)Leakage inductanceTopology (electrical circuits)Voltage dropWaveformElectrical engineeringComputer scienceEngineeringControl (management)

Abstract

fetched live from OpenAlex

A 1-switch split dc-rail boost converter topology is described that uses a mutually coupled two winding inductor to balance the voltage drop across both of its capacitor smoothed input and output voltage terminals. The resultant converter obtains balanced input and output voltages under extreme unbalanced load conditions using no extra converter control features. A current sharing action between the two coupled windings is described, during both the on-time and off-time of the switch, and shown to be responsible for the voltage balancing feature. Simulations and experimental results are used to illustrate the nature of the winding current waveforms that produce this current sharing action. Load imbalances exceeding 2:1 can easily be compensated. Simulations and experimental results show that deviations of the converter input and output voltages, largely determined by the winding leakage inductance and the dc voltage gain, can be limited to within the range 1% to 8% for load imbalances of up to 2:1.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.266
Teacher spread0.243 · 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 designSimulation or modeling
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

Citations1
Published2007
Admission routes1
Has abstractyes

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