Development of an operation skill-training simulator for double-front work machine
Bibliographic record
Abstract
This paper reports a newly developed simulator for operation skill training in Double-Front Construction Machinery (DFCM) that allows novices to virtually experience tough operations repeatedly using DFCM under various conditions, including dangerous congestion. First, we selected several situations targeted where the DFCM needs to be used to provide a high level of operation skills: sorted dismantling for recycling and reusing resources, rescue and recovery work in disaster areas, and building construction. In addition, we developed an operation skill-training simulator that enables novice operators to repeatedly train with the high level of operation skills needed to easily and safely handle the DFCM in even more complicated works. This simulator system has two joysticks (set in front of a monitor) to dependently control the two fronts of the animated DFCM on the monitor. Several modes involving basic construction tasks are provided and the effects of improvement in operability achieved by the training simulator can be verified. Evaluation experiments indicated that repeated training using the simulator successfully decreased the operation time to complete a task and enhanced positioning accuracy in cooperative transportation with the two fronts. The results confirm the effectiveness of the developed 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".