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Record W2982337339 · doi:10.1109/tpel.2019.2910489

Manitoba Rectifier—Bridgeless Buck–Boost PFC

2019· article· en· W2982337339 on OpenAlexafffundabout
Ken King Man Siu, Carl Ngai Man Ho

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Manitoba
FundersCanada Research Chairs
KeywordsRippleTopology (electrical circuits)CapacitorElectronic engineeringPower factorComputer scienceBuck converterBuck–boost converterBoost converterVoltageControl theory (sociology)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a new bridgeless buck-boost power factor corrector with the use of the active virtual ground technique, which is named as the Manitoba Rectifier. The proposed topology can convert the grid ac voltage into a wide range of voltage outputs within a single-stage circuit. It is in a bridgeless structure and simple in design. During the operation, an LC filter is generated at the system input, where continuous grid current is guaranteed in a buck-boost characteristic topology. In addition, the filter capacitor helps to clamp the voltage ripple between the grid and the output bus terminal. Both leakage current and common mode noise are kept in a relatively small value. Thus, a single-stage and low common-mode buck-boost converter system is built. The proposed topology is successfully implemented on an 800 W prototype, and the performance is experimentally verified, which shows good agreement with the theoretical findings.

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 score0.007
Threshold uncertainty score0.023

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.188
Teacher spread0.184 · 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

Citations58
Published2019
Admission routes3
Has abstractyes

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