MétaCan
Menu
Back to cohort
Record W2904753129 · doi:10.1109/ecce.2018.8558382

Parallel Energy Buffering LED Driver Achieves Electrolytic Capacitor-Less and Flicker-Free Operation

2018· article· en· W2904753129 on OpenAlexaff
Peng Fang, Bo Sheng, Wenbo Liu, Yan‐Fei Liu, Paresh C. Sen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsCapacitorElectrolytic capacitorElectrical engineeringFilm capacitorVoltageEnergy (signal processing)Computer scienceBuck converterElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

AC powered LED drivers experience imbalanced energy, between input and output, in a half line cycle. To achieve flicker-free operation, the imbalanced energy needs to be buffered, and often by energy-dense electrolytic capacitors. However, electrolytic capacitors are also well-known for short lifespan and the limiting factor of LED drivers' life. High voltage film capacitors and Buck converter had been used in the proposed LED driver to buffer imbalanced energy. When , the extra energy is transferred from AC input directly to the high voltage film capacitors. When Pin<; PLED, the shortage energy is transferred from the high voltage film capacitors to the output by the Buck converter. The imbalanced energy goes through two times power conversion in the proposed LED driver, which is one time less than other comparable electrolytic capacitor-less designs. Therefore, an improved efficiency can be achieved. A 28W Flyback topology based experimental prototype had been built and tested to verify the proposed design.

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced DC-DC ConvertersFrench-language works237,207