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Record W2980042650 · doi:10.1049/iet-pel.2018.5459

Single‐stage single‐switch high power factor driving circuit for lighting applications

2019· article· en· W2980042650 on OpenAlexaff
Shangzhi Pan, John Lam, Praveen Jain

Bibliographic record

VenueIET Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's UniversityYork University
Fundersnot available
KeywordsSingle stageStage (stratigraphy)Power factorPower (physics)Factor (programming language)Computer scienceElectrical engineeringEngineeringVoltagePhysicsGeology

Abstract

fetched live from OpenAlex

Although compact fluorescent lamps (CFLs) have been in the market for a long time, many of them do not achieve the same power factor as the incandescent lamps do, which appears to be a significant problem for the utility with the current widespread use of CFLs for household lightings. A single‐stage single‐switch electronic ballast with active power factor correction is presented in this study for CFL applications. Unlike other single‐switch ballast circuits, the presented integrated dual‐boost converter circuit can produce a symmetric bipolar square‐wave voltage, thus generating a near‐pure high‐frequency sinusoidal voltage on the lamp, which is beneficial to the CFL lifetime. Moreover, its soft‐switching variant can achieve the ZVZC turn‐on and ZV turn‐off for the MOSFET to improve the conversion efficiency. Detailed operating principles and circuit analysis of the proposed circuit have been provided in this study. Simulation and experimental results on a prototype of a 15 W CFL validate the theoretical analysis and highlight the merits of the presented work.

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 score0.009
Threshold uncertainty score0.031

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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