A comprehensive model to predict solid state lighting performance
Bibliographic record
Abstract
A tool to predict the behavior of LED-based luminaires is critical to their design. In the absence of such a tool, the design process becomes quite laborious and highly dependant on expensive experimental work. Unfortunately, thermal effects can make the system level behavior very difficult to predict: a change in temperature causes a change in spectral characteristics, which in turn causes an adjustment to the balance of the LEDs, affecting the heat load, and thereby once again changing the spectral characteristics. In order to accurately predict how a SSL luminaire will behave, it is necessary to model it at the system level. An accurate model must consider heat loading/dissipation, the response of electrical components to temperature, the effect of temperature on spectral characteristics (including intensity, spectral bandwidth, and peak wavelength), and then recursively recalculate the heat load. We have developed just such a model for a luminaire employing optical feedback and thermal feedforward. The model makes use of measured data for the components, and computes its system-level behavior. The model also computes the change in behavior due to aging, based on the junction temperature. The model has been verified by experiment, and found to agree to within ten percent. The aging predictions have not yet been verified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".