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Record W3017778577 · doi:10.5539/mas.v14n5p29

Analysis of Coordinates Decoupling of Multi-Dimensional Precision Optical Adjusting Frame in Optical Alignment

2020· article· en· W3017778577 on OpenAlexvenueno aff
Yong Luo

Bibliographic record

VenueModern Applied Science · 2020
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesJinan University
KeywordsDecoupling (probability)Computer scienceCompensation (psychology)Dimension (graph theory)Frame (networking)Coupling (piping)Optical axisOpticsControl theory (sociology)Artificial intelligencePhysicsMathematicsTelecommunicationsEngineeringControl engineeringMechanical engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.242
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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