The examination of operator performance when controlling a shipboard crane anti-sway control system within a virtual-reality simulator
Bibliographic record
Abstract
Anti-sway control systems are valuable tools for cranes to prevent unexpected payload sway and undesired motion. However, for shipboard cranes, where the operator moves with the ship, anti-sway control systems can result in significant relative motion between the payload and operator, particularly in rough seas while attempting to align the payload with an ocean-frame target. Therefore, an important question to ask is, do operators actually find anti-sway systems intuitive, or do they feel they have to “fight” the system to achieve their desired performance? To address the question, this paper presents a human factors study designed to evaluate the effectiveness of a shipboard crane anti-sway system with an operator-in-the-loop. Participants completed a series of tests in a virtual-reality simulator, in which they attempted to align the payload of a nine degree-of-freedom shipboard knuckle boom crane with targets in both the ocean/world coordinate frame and ship deck coordinate frame, using an anti-sway system that provided complete motion compensation in both coordinate frames. The study found that there was a statistically significant improvement in the participant’s ability to track a desired payload target with the use of the anti-sway system of up to 49.1%. In addition, as the participant had no knowledge of how the anti-sway system operated, or even if it was active, the results indicate anti-sway control systems can be intuitive for operators to use.
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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 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".