MUTE-DSS: A digital-twin-based decision support system for minimizing underwater radiated noise in ship voyage planning
Bibliographic record
Abstract
We present a novel MUTE-DSS, a digital-twin-based decision support system for minimizing underwater radiated noise (URN) during ship voyage planning. The system is built on the Robot Operating System (ROS2), an open-source middleware framework designed for modular, distributed, and real-time communication between software components. The framework integrates near-field acoustic modeling using a semi-empirical spectrum with 3D ray tracing for far-field propagation, enabling real-time ship noise prediction and whale distribution tracking. The proposed DSS performs a two-stage optimization pipeline: Batch Informed Trees for collision-free ship routing and a genetic algorithm for adaptive ship speed profiling under voyage constraints that minimizes cumulative URN exposure to marine mammals. The effectiveness of MUTE-DSS is demonstrated through case studies of ships operating between the Strait of Georgia and the Strait of Juan de Fuca, comparing optimized voyages against baseline trajectories derived from automatic identification system data. Results show substantial reductions in noise exposure level, up to 7.14 dB, corresponding to approximately an 80.68 % reduction in a simplified scenario, and an average 4.90 dB reduction, corresponding to approximately a 67.6 % reduction in a more realistic dynamic setting. These results illustrate the adaptability and practical utility of the proposed decision support system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".