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Record W2021106781 · doi:10.1109/apec.2013.6520305

A new high power factor, soft-switched LED driver without electrolytic capacitors

2013· article· en· W2021106781 on OpenAlexaff
Ting Hao, John Lam, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectrolytic capacitorCapacitorPower factorInductorRippleFilm capacitorElectrical engineeringDiodePower (physics)Tantalum capacitorEnergy storageFilter capacitorDecoupling capacitorElectronic engineeringComputer scienceEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

A novel high power factor, soft-switched Light Emitting Diode (LED) driver without electrolytic capacitors is proposed in this paper. Conventional electrolytic capacitors used in LED drivers as the energy storage element have relatively short lifespan and affect the lifetime of the entire LED lighting system. In the proposed design, coupled inductors are used to provide power factor correction (PFC) and to provide part of the required energy with the DC-link capacitor to the output. As a result, low ripple output current is still achieved even when the size of the DC-link capacitor is reduced, which allows the film capacitor to be used to replace the conventional unreliable electrolytic capacitor. At the same time, high input power factor is also achieved. The operating principles and theoretical analysis of the proposed circuit will be described in this paper. An experimental prototype is built and tested in the laboratory. All the experimental results are provided to support the merits of the proposed 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 score0.002
Threshold uncertainty score0.006

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 science0.0010.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.183
Teacher spread0.179 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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