A Study on the Cases of the Application of 3D Apparel CAD System to the Domestic and Overseas Fashion Education
Bibliographic record
Abstract
The development of computer graphics and Internet technology has created a 3D Web-based virtual world that has transformed the global fashion industry environment. In this study, the application cases of 3D virtual fashion education were analyzed to discuss the necessity and application of a 3D apparel CAD curriculum as part of a special education for global fashion talent at Korean fashion-related colleges. Prior studies, literature, photo data and Internet data (in and out of Korea) were used for this study. The demonstration case studies were conducted for the virtual fashion education ``SLCC 2007`` of Buffalo State College (U.S.), ``Ratava`s Line (2004)`` of SFU/FIT Collaborative Design Project (Canada and U.S.) and ``2011 Graduation Fashion Show`` of Ueda Fashion College (Japan). The results of the study show that the 3D apparel CAD system (as a core technology of the IT fashion industry) would allow the current mass production concept to change to a new paradigm of ``mass customization`` along with new fashion business types that include global fashion companies and Web-based Internet, mobile and virtual-world shopping malls. In addition, it appears that the system should be included in the curriculum of fashion-related colleges and institutes to educate technical designers for the global fashion industry and global fashion talent with comprehensive system operation and management ability, and to promote single proprietor companies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".