Accurate chromatic control and color rendering optimization in LED lighting systems using junction temperature feedback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it