Analysis of Coordinates Decoupling of Multi-Dimensional Precision Optical Adjusting Frame in Optical Alignment
Bibliographic record
Abstract
Information technology has much promoted the development of optical technology. With the help of computer technology, people can design and manufacture more complex optical systems than before, to obtain ideal imaging quality. The complexity of the optical system brings enormous challenges to optical alignment. Optical system alignment is the crucial link of transforming the excellent optical design into instruments with good performance in reality. Precision alignment of an optical system requires precise adjustment of each component's degree of freedom using a specific adjusting mechanism. Due to the quantification and compensation correction for the coordinates coupling relationship among each dimension adjusting freedom of the adjusting frame can not be carried out. Generally, the coordinates coupling problem is usually ignored in optical system alignment, to cause the optical adjustment error. This paper carries out an analysis for the coupling relationship among each dimension motion freedom of multi-dimensional precision optical adjusting frame in details by mathematical modeling and simulation, the decomposed transformation for each dimension adjusting mount of multi-dimensional precision optical adjusting frame, and the compensation correction for the coordinates coupling among each dimension adjusting mount. The test results show that this method can effectively reduce the difference between the actual mechanical adjusting mount and the expected optical adjusting mount, to achieve more accurate optical adjustment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".