Bibliographic record
Abstract
Automatic control of marine vessels is a field that has been growing for many years.Currently, there exist many leisure boats equipped with the functionality to automatically stay in a chosen position with a constant heading.This is usually referred to as station keeping and tries to compensate for disturbing forces from weather phenomena such as waves and wind.In commercial applications it is usual to measure properties of the weather forces and use that information to compensate for them during station keeping.However, most leisure boats lack these kinds of sensors.There exist methods for estimating the weather forces heading by rotating a boat until it faces the weather forces.However, this might not always be possible due to space restrictions in e.g.ports.Therefore, the following question can be posed.Is it possible to estimate the weather forces heading without measuring anything outside the vessel or moving it in a certain way?Three different methods for estimating the weather heading without affecting the control of the boat are evaluated.All methods are model-based which means that a dynamic model of the boat performing station keeping is required.Such modelling is described and a number of system identification steps to find numerical values for the models are also presented.The proposed system identification steps need to be performed during times when there are little to no disturbances which is undesired.It was found that varying degrees of system knowledge gave different accuracy in the weather heading estimation.Little knowledge gave a rather weak estimation.The highest accuracy achieved was within about half a quadrant.Future work can be done to see if it is possible to remove disturbances from data collected at times when disturbances are present to be able to perform system identification at any time.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".