Coordination of FPSO and tanker offloading operations
Bibliographic record
Abstract
There is no argument that the effects of control and automation technology on the marine transportation industry have been great. Since the invention of the first autopilot by Sperry early in the last century, we have seen the invention, innovation and improvement in new control technology, culminating in today's heavily automated vessels. Control and automation technologies, specifically Dynamic Positioning (DP), have benefited the industry by enabling vessels to do things that would not have otherwise been possible using manual methods. This paper looks at the application of intelligent or knowledge-based control (IC or KBC) technology to the control of marine vessels. The next section gives the motivation behind IC and how it differs from classical control systems theory. The following section discusses how marine vessel control can benefit from the application of IC, with the example of an FPSO and an offloading tanker operation. Finally, the current progress with regards to the proving of this concept using model testing and simulation is given.
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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 | 0.000 | 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) | 0.001 | 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".