Machine Learning Enabled LED Lighting Using Scattering Optics
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
Abstract Illumination receives a great deal of attention as white light‐emitting diodes become energy‐efficient light sources in households and commercial buildings, on streets and highways, and at stadiums and construction sites. In general, lenses and mirrors are used to control the spatial distribution of white LED (WLED) light. Here, it is proposed to use an optical diffuser, the key device in scattering optics, to achieve a pre‐defined WLED brightness distribution by nanocrystals and machine learning. Optical diffusers are typically used to create soft light (similar brightness from any angle of view), however, here the concentration of nanocrystals in a nanocomposite film (optical diffuser) to tune its optical property at different regions is altered. Machine learning is employed to achieve the inverse design of the optical diffuser pattern controlling the WLED brightness distribution, and this design task is beyond human capacities which are carried out using the brute force approach. In the end, several pre‐defined WLED brightness distributions are demonstrated for showing the success of this efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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