Accurate chromatic control and color rendering optimization in LED lighting systems using junction temperature feedback
Bibliographic record
Abstract
Accurate color control of LED lighting systems is a challenging task: noticeable chromaticity shifts are commonly observed in mixed-color and phosphor converted LEDs due to intensity dimming. Furthermore, the emitted color varies with the LED temperature. We present a novel color control method for tri-chromatic and tetra-chromatic LEDs, which enable to set and maintain the LED emission at a target color, or combination of correlated color temperature (CCT) and intensity. The LED color point is maintained over variations in the LED junctions’ temperatures and intensity dimming levels. The method does not require color feedback sensors, so to minimize system complexity and cost, but relies on estimation of the LED junctions’ temperatures from the junction voltages. If operated with tetra-chromatic LEDs, the method allows meeting an additional optimization criterion: for example, the maximization of a color rendering metric like the Color Rendering Index (CRI) or the Color Quality Scale (CQS), thus providing a high quality and clarity of colors on the surface illuminated by the LED. We demonstrate the control of a RGBW LED at target D65 white point with CIELAB color difference metric triangle;a,bE < 1 for simultaneous variations of flux from approximately 30 lm to 100 lm and LED heat sink temperature from 25°C to 58°C. In the same conditions, we demonstrate a CCT error <1%. Furthermore, the method allows varying the LED CCT from 5500K to 8000K while maintaining luminance within 1% of target. Further work is ongoing to evaluate the stability of the method over LED aging.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".