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Record W2375516755

An Approach of Chinese Ink Rendering Based on Fluid Dynamics Simulation

2010· article· en· W2375516755 on OpenAlexvenueno aff
Shaobo Wang, TV Arts

Bibliographic record

VenueMicrocomputer applications · 2010
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRendering (computer graphics)Computer scienceShaderComputer graphics (images)InkwellGraphics processing unitReal-time renderingPixelSoftware renderingGraphicsParallel renderingTexture memoryArtificial intelligenceParallel computing3D computer graphicsSpeech recognition
DOInot available

Abstract

fetched live from OpenAlex

An approach of Chinese Ink Rendering based on the fluid dynamics simulation is presented in this paper. Using the High Level Shader Language (HLSL) and Graphics Processing Unit (GPU), the multiple vertices and pixels in the texture is processed.By computing the acceleration caused by external forces interactively and applying them to the textures and the Chinese ink rendering through the simulating of the dynamic fluid is accomplished. Finally, the experimental results show that the simulating of the Chinese ink dynamic effects is realized successfully, controls and interventions can be done during the rendering process, and the rendering process can be displayed in the real-time.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.725

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 science0.0010.000
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 head0.293
Teacher spread0.284 · 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
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
Published2010
Admission routes1
Has abstractyes

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