Machine Learning Enabled LED Lighting Using Scattering Optics
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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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".