A real-time ship safety early warning method based on trajectory prediction
Bibliographic record
Abstract
To achieve real time early warning of ship safety,assist the ship officer in estimating the risk timely and avoiding the ship accident,a novel method is developed for predicting ship trajectory. Based on collected real time information,such as AIS information,hydrological and weather information,foundational navigation elements information,ship 's trajectories could be predicted. After computing ship 's dynamic values of DCPA and TCPA,the ship's dynamic space risk and time risk can be calculated to obtain the real-time navigation risk,the real time early warning can be achieved finally. The accident involving Liyuan 2 and the Wuhan Yangtze River Bridge is analyzed as a case study. By using the proposed method,ships in target area could be supervised. When an urgent situation is about to happen,it can be judged automatically,whilst the accident risk can be distinguished in advance,the real-time early warning function can be achieved. The results show that proposed method can make up for shortcomings of existing systems,with detecting the high risk navigation behaviors and giving an alarm timely and effectively.
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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) | 0.002 | 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 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".