The examination of operator performance when controlling a shipboard crane anti-sway control system within a virtual-reality simulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it