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Record W2150317538 · doi:10.5555/1161734.1162056

Two-step 3-dimensional sketching tool for new product development

2004· article· en· W2150317538 on OpenAlexaff
Ali Akgündüz, Hang Yu

Bibliographic record

VenueWinter Simulation Conference · 2004
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsConcordia University
Fundersnot available
KeywordsSketchComputer sciencePoint (geometry)Engineering drawingParametric designCADProduct designDevelopment (topology)Parametric statisticsProduct (mathematics)Conceptual designVirtual realityTracingSolid modelingComputer graphics (images)Human–computer interactionArtificial intelligenceEngineeringAlgorithmMathematicsProgramming languageGeometry

Abstract

fetched live from OpenAlex

This paper discusses a two-step virtual reality based conceptual design tool that enables industrial designers to create sketches of their ideas in 3-dimensional space in real time. In the developed sketching tool, the rough shapes of products are generated by tracing the trajectory of the data-gloves worn by the designer. In the model a practical solution is provided to reduce the generation of unnecessary control points. This is achieved by representing each control point by a spherical volume. Once the rough sketching is completed, NURBS surfaces are constructed by the limited number of reference points that are selected from the initial sketch by using a virtual pen. The two-steps sketching technique enables designers to perform their artistic characteristics freely in an intuitive environment and also enables designers to generate parametric representations of the surfaces to be used in CAD/CAM systems for further analysis.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.038
GPT teacher head0.307
Teacher spread0.269 · 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

Citations7
Published2004
Admission routes1
Has abstractyes

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