Validating a cloth simulator for measuring tight-fit clothing pressure
Bibliographic record
Abstract
Tight-fit cloth pressure provides important clue on how well a cloth fits to a body and thus on how comfortable the wearer feels with the cloth. Traditionally-used pressure sensor devices are expensive, sensitive to the experimental environment, and difficult to reproduce. In this paper, a physically-based cloth simulator has been tested for its usability as to measuring the cloth pressure, in order to replace physical measurement of cloth pressure that requires careful operation of pressure sensors. We use existing cloth simulator based on a particle system and measure spring forces exerted on each particle along its normal direction, divided by the summed area of triangles adjacent to that particle. To quantitatively validate the pressure values from the simulator, we have conducted comparative analysis on a set of thin-shell cylindrical tubes --- clothing pressure values have been measured by theoretical estimation and physical experiments using pressure sensors, and compared with those measured by the simulation. While their absolute pressure values differ from each other they exhibit a consistent tendency. From these comparative studies we concluded that cloth simulator can actually be used to measure tight-fit cloth pressure, and further conducted the clothing pressure measure on 3D human body models using the simulator.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".