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Record W3194964220 · doi:10.11159/mhci21.301

Aerial Display Using Lenticular Lenses

2021· article· en· W3194964220 on OpenAlexvenueno aff
Kazuhisa Yanaka, Toshiaki Yamanouchi

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer graphics (images)OpticsPhysics

Abstract

fetched live from OpenAlex

An aerial display that can float a two-dimensional image or a three-dimensional object in an empty space is important as a noncontact interface.Aerial displays have two types: a reflection type that uses a mirror and a refraction type that involves a lens.The latter has the advantage of being easily miniaturized because the path of light does not bend much.The method using two commercially available lenticular lenses is inexpensive.However, the principle of the method is not yet fully understood.In this study, the rules of light rays passing through the lens were applied to explain this principle clearly.For further confirmation, a simulation was performed.Results revealed that the lens group consisting of two convex lenses arranged at the interval of twice the focal length as well as the lens group consisting of three convex lenses arranged at intervals the focal length reflected parallel rays coming from an angle as if they were mirrors.When the number of lenses was set to 3, the viewing range widened.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.008
GPT teacher head0.209
Teacher spread0.201 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicAdvanced Optical Imaging TechnologiesFrench-language works237,207