Experimental measurement of the coupled motions of a ship and a deep sea remotely operated vehicle system
Bibliographic record
Abstract
Deep sea remotely operated vehicle systems (ROV) are used extensively throughout the ocean industry for ocean work and exploration. These systems typically consist of a large support vessel, a winch, umbilical cable, cage and ROV. The Space and Subsea Robotics lab has developed a mathematical model and computer simulation for simulating the coupled dynamic interaction between the cage and ship. To validate this model and acquire a greater understanding of the coupled motions, the motions of the ship and cage and tension in the umbilical cable were measured simultaneously and recorded in real time. Data was collected for the ROPOS ROV system while it was operating over the Juan De Fuca Ridge, approximately 200 km off the West Coast of British Columbia, Canada. The Ocean Data Acquisition System (ODAS) was used to acquire the data because it can collect data from several remote instruments and transmit the data in real time to a shipboard computer. For this experiment, two sites were instrumented, the top of the ship's A-frame and the top of the cage. The authors measured all six directions of motion of the cage and ship using three accelerometers and three gyros mounted along perpendicular axes at both sites. Additionally, a pressure sensor mounted on the cage measured depth. Both the pressure and its analog derivative were sampled. Also, a strain gauge load cell placed on the sheave at the A-frame measured tension in the umbilical cable. The mathematical model was developed to model the coupled motion of the ship and cage in the vertical direction; hence, validation required the measurement of these motions.
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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) | 0.000 | 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 | 0.001 | 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".