MétaCan
Menu
Back to cohort
Record W2610678595 · doi:10.1049/joe.2017.0092

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

2017· article· en· W2610678595 on OpenAlexafffund
Ali Shagerdmootaab, Mehrdad Moallem

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.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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

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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of EngineeringSame topicAdvanced DC-DC ConvertersFrench-language works237,207