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Record W2373877747

Laser Atmosphere Propagation Spot Image Pre-processing Method Based on Fuzzy Enhancement

2011· article· en· W2373877747 on OpenAlexvenueno aff
Cai Hui-zhi

Bibliographic record

VenueMicrocomputer applications · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Systems and Laser Technology
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceAtmosphere (unit)Image processingFuzzy logicLaserNoise (video)Filter (signal processing)Computer visionProcess (computing)Artificial intelligenceImage (mathematics)Median filterImage qualityOpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

This paper introduced the model of laser atmosphere propagation measurement,analyzed the noise of laser atmosphere propagation characteristic measurement.According to the analysis of the characteristic and the insufficiency of classical fuzzy enhancement,an improved fuzzy enhancement method was put forward.A pre-processing method which combines the improved method and mean filter was put forward to process laser spot image get from experiment.The results shows that compared with the common pre-processing methods,the new method can enhance the image contrast and improve the image quality effectually.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.946
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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 designOther design
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
Published2011
Admission routes1
Has abstractyes

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