Improving Site and Materials Handling Operations through Autonomous Vehicle-Related Technologies
Bibliographic record
Abstract
Improving Site and Materials Handling Operations through Autonomous Vehicle-Related Technologies Ashley Tews Pages 1129-1138 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: In 2005, we automated a forklift-based Hot Metal Carrier to be capable of typical metal transfer operations around a smelter. The project was highly successful and the vehicle has demonstrated hundreds of hours of live autonomous operations to thousands of people from industry and the public. As a result of the exposure and success of the project, we have expanded its focus to demonstrate how various technology components can be utilised to gain benefits in other areas of industrial operations. These include asset tracking, vehicle usage analysis, pedestrian detection and infrastructure profiling. Each of these components was derived from the core autonomous vehicle technology suite and has shown a high potential for improving safety and efficiency of vehicle-related operations, and improved diagnostic processes for measuring deformations of bakes and furnaces. The autonomous vehicle project and its extended technologies are described in this paper. Keywords: Site automation, vehicle operations, efficiency, site safety DOI: https://doi.org/10.22260/ISARC2013/0124 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".