Multi-element direct design using a freeform surface for a compact illumination system
Bibliographic record
Abstract
An iterative optimization algorithm is introduced to address the surface iterative errors as well as source extension issues in a freeform illumination system for producing satisfactory illumination distribution. A unique two-parameter coordinate system is utilized to represent the emitted ray directions. Then, the direction vector for the incident rays, which propagate through several surfaces, is obtained using ray-tracing techniques. Based on the mapping between the incoming rays and a target grid, a freeform surface is generated as a good starting design. An iterative optimization strategy is further employed to alleviate the deterioration of illumination distribution on the target region, and the uniformity of the illumination system is evaluated during optimization. Very few variables are demanded, and more flexibility in the design of the freeform surface is offered. Successive iterations can be performed until the desired result is attained. An optical system is used as an example to demonstrate the validity of the proposed method, and numerical simulations are carried out to evaluate the optical performance. The simulation results show that a small angular intensity distribution and prescribed rectangular illumination pattern can be achieved simultaneously.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".