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Record W2355601080

The Hardware Design of Automatic Collimation System Based on Image Processing

2006· article· en· W2355601080 on OpenAlexaff
Zhong Qi-yuan

Bibliographic record

VenueLaser & Infrared · 2006
Typearticle
Languageen
FieldEngineering
TopicImage Processing Techniques and Applications
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsComputer visionComputer scienceImage processingMissileArtificial intelligenceCollimated lightImage qualityImage (mathematics)SoftwareDepth of fieldAperture (computer memory)Computer graphics (images)Field (mathematics)OpticsMathematicsLaserPhysicsEngineeringAcoustics
DOInot available

Abstract

fetched live from OpenAlex

To realize the automatic collimation of missile by using image processing,direct focusing mode is used,and the part definition of image is computed to achieve automatic focusing.Considering the effect of exposure time,aperture size and the depth of field to image collection synthetically,the holistic definition of image is computed to choice the best exposure combination.To improve the quality of image and minish the burden of image processing software of automatic collimation system,image pretreatment is carried through image collection.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.003

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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
Published2006
Admission routes1
Has abstractyes

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