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Record W1983484424 · doi:10.1109/ecce.2012.6342245

Failsafe smart LED module with thermal management, string current balancing and commutation for lifetime extension

2012· article· en· W1983484424 on OpenAlexaff
Shuze Zhao, Ke Cao, Saleh Firwana, Anton Swaris, Ronald Content, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRedundancy (engineering)CommutationLight-emitting diodeConstant currentLED lampComputer scienceConvertersVoltageElectrical engineeringElectronic engineeringEmbedded systemEngineeringReliability engineering

Abstract

fetched live from OpenAlex

A new LED driver architecture is presented for increasing the lifetime and reliability of solid-state lighting modules. The architecture targets indoor/outdoor industrial applications, where the overhead cost of a smart LED driver is outweighed by the added value of improved lifetime and fault tolerance. The system includes two complete sets of white LEDs and associated current-mode drivers. The digitally controlled dc-dc converters communicate with each-other to smoothly transfer current from one set of LEDs to another at a regular commutation interval. A low-power pulse-frequency-modulation mode is used to conserve power in the LED set that is off. The redundancy is also exploited to perform precise current balancing and health monitoring of the individual LED strings, while the total light output remains constant at all times. The architecture is demonstrated on a 25 W module with 154 white LEDs, which forms 1/4 of a full luminaire.

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 score0.003
Threshold uncertainty score0.009

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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