Prediction of the Hydrodynamic Parameters in the Coupled Heave and Pitch Motion Equations for Underwater Robotic Vehicles Using Measured Responses at Sea
Bibliographic record
Abstract
A method for the identification of the damping, restoring, and coupling parameters in the equations describing the coupled heave and pitch motions for an underwater robotic vehicle (URV) sailing near sea surface in random waves using only its measured responses at sea is presented. The random decrement equations are derived for the URV performing coupled heave and pitch motions in random waves. The hydrodynamic parameters in these equations are identified using a new identification technique called RDLRNNT, which uses a combination of a multiple linear regression algorithm and a neural networks technique. The combination of the classical parametric identification techniques and the neural networks technique provides robust results and does not require a large amount of computer time. The developed identification technique would be particularly useful in identifying the parameters for both moderately and lightly damped motions under the action of unknown excitations effected by a realistic sea. Numerically generated data for the coupled heave and pitch motion of a URV are used initially to test the accuracy of the technique. Experimental data are also used to validate the identification technique. It is shown that the developed technique is reliable in the identification of the parameters in the equations describing the coupled heave and pitch motions for an URV.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".