Study on Methodology for Detecting the Status of General Excavator without Renovation
Bibliographic record
Abstract
Study on Methodology for Detecting the Status of General Excavator without Renovation Min-Sung Kang, Myeongsu Gil, Seunghoon Lee, Dongsu Shin, Chang-Soo Han, Jung Soo Han, Bo-Hyun Yu Pages 1334-1341 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Excavation and earthmoving equipment mostly operate in accident-prone areas. These areas normally have rollover, confinement, and fall or may be considered harmful work areas due to demolitions and sewage removals. It is inevitable that excavation and earthmoving systems should be developed to secure the safety of operators through unmanned and remote control systems. Therefore, this research proposes a system that enables excavators to be unmanned and remote controlled without any renovation, remodeling, change, or transformation by applying attachable and separable mechanism and modules. Moreover, this research proposes the sensor module to evaluate the dynamic status without processing remodeling or transformation the existing hydraulic drive excavation system by adopting the easy attachable and separable mechanism. The study verifies this proposal based on observed possibility and feasibility through the experiments. Keywords: Installation type, Tele-operated Excavation System, Sensor-based estimation, Human-Robot Cooperation DOI: https://doi.org/10.22260/ISARC2013/0150 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 0.001 |
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".