Development of the Simulator for Carrying a Lifted Load in Large Plant Construction
Bibliographic record
Abstract
Development of the Simulator for Carrying a Lifted Load in Large Plant Construction Yoshihito Mori, Masaomi Wada, Sayuri Maki and Satoshi Tsukahara Pages 610-615 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Large plant construction has numerous works, and the proportion of hoisting operations in the total construction work is high. In the hoisting operations, many things should be considered to keep the operations safe, and the work steps of the hoisting operations are planned by considering them. However, it is difficult for novice construction workers to understand the work steps with conventional two-dimensional drawings. Thus, to have those workers understand it well, we developed the hoisting-operation simulator considering the physical behavior of a load in conjunction with 3D viewer and physics engine. The simulator can visualize the work steps as three-dimensional animation. Moreover, by associating the simulator with mixed reality technology, we developed the system superimposing the 3D animation on reality space via head mount display. Experiments verify that the 3D animation of a load moves with the vibration due to the inertial force and that the 3D animation generated by the simulator is superimposed in the work site. Keywords: Simulator; Mixed reality; three-dimensional measurement DOI: https://doi.org/10.22260/ISARC2019/0081 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".