Digital Twin Application to Ocean Monitoring Equipment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integration of digital twin technology into the field of coastal monitoring will serve as a sustainable solution to the environmental challenges faced by coastal communities around the world. A strategy is proposed to digitally twin the motor-driven winch and its controller within a coastal monitoring sensor array. The strategy will allow for improved automatic control and therefore an increase in system performance. Background on the existing techniques of coastal monitoring as well as their limitations are discussed, along with an overview of the relevant structures, components, and theoretical applications of existing digital twins. The challenges of signal processing, connectivity, and managing time delay, as well as the proposed techniques to overcome them, are explained. Preliminary work involving the use of regression-based polynomial system modelling, low-pass filtering, and Kalman filtering on a DC motor plant provided a strong proof of concept for the digital twin of the motor-driven winch. Furthermore, the future work to be completed, as well as the justifications for doing so, and the potential future path and impact of the research are detailed. The proposed strategy provides the groundwork on which future research can be built, eventually allowing for the digital twinning of an entire network of coastal monitoring systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it