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Record W2908822928 · doi:10.1109/tpel.2019.2892981

A Multiplexing Ripple Cancellation LED Driver With True Single-Stage Power Conversion and Flicker-Free Operation

2019· article· en· W2908822928 on OpenAlexafffund
Peng Fang, Samuel Webb, Yang Chen, Yan‐Fei Liu, Paresh C. Sen

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRippleFlickerElectrical engineeringVoltageElectronic engineeringEngineeringPower (physics)Power factorLED lampComputer sciencePhysics

Abstract

fetched live from OpenAlex

Although a single-stage off-line power light-emitting diode (LED) driver can achieve low cost and high efficiency, the notorious double-line-frequency flicker issue with a single-stage LED driver limits its usage in high-quality lighting applications. To solve lighting flicker, as well as maintain a low cost and high efficiency, a multiplexing ripple cancellation (MRC) LED driver is proposed in this paper. One switching cycle is divided into two intervals. During the first interval, the proposed LED driver operates as a conventional LED driver that transfers energy from the ac input to LED output, performs power factor correction, and generates the main output voltage. The main output voltage has a double-line-frequency ripple like in a conventional design. During the second interval, the proposed LED driver transfers energy from the ac input again to generate an opposite ripple voltage to cancel the ripple voltage from the main output. In this way, the voltage across the LED load is a dc to achieve flicker-free LED driving performance. More than 99% of the output power goes through one-time power conversion, while less than 1% goes through two-time power conversion. A 7.5-W experimental prototype is built and tested to verify the design concept.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
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 head0.183
Teacher spread0.178 · 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 teacher head, not a consensus.

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

Citations22
Published2019
Admission routes2
Has abstractyes

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