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Record W2610678595 · doi:10.1049/joe.2017.0092

Methodology for reducing the filtering capacitor in low‐flicker LED drivers

2017· article· en· W2610678595 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Journal of Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitorElectrolytic capacitorFilter capacitorFlickerFlyback transformerFilter (signal processing)Ceramic capacitorCapacitanceFilm capacitorElectrical engineeringLight-emitting diodeMaterials scienceFlyback converterElectronic engineeringComputer scienceVoltageOptoelectronicsEngineeringBoost converterPhysicsElectrodeTransformer

Abstract

fetched live from OpenAlex

The amount of light flicker in an AC–DC light‐emitting diode (LED) driver is dependent on the size of filter capacitors. In this study, a study is conducted on reducing the size of filter capacitor in an AC–DC buck–boost/flyback LED driver using flicker index and per cent flicker light measures. Using this approach, a procedure for minimising the filter capacitance is presented. It is then concluded that relatively small filter capacitors such as film or ceramic capacitors can be chosen while meeting light flicker requirements. Hence, an LED drive with a longer lifetime can be achieved when compared with a driver using electrolytic capacitors. Experimental studies are presented for a 20 W AC–DC buck–boost/flyback LED driver prototype which utilises ceramic capacitors for driving Cree CR22‐32L and XLamp XP‐G LED strings.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.629
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.036
GPT teacher head0.276
Teacher spread0.240 · 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