A Study on Augmented Reality Remote Maintenance Support System for Ships and Offshore Structures
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
From the viewpoint of safety and sustainability, the demand for autonomous vessels is increasing. Due to technical and administrative limitations, achieving a fully autonomous ship is through sequential development and application, and this can be confirmed through the 4 unmanned surface ship degrees As an intermediate step, the main concern is the operation of the ship with minimal onboard crews, and this is a similar situation for offshore structures. In a crew-minimized environment, one crew member should be able to perform multi-discipline techniques, but it is practically impossible to establish such an environment in a short period of time. For this reason, research and development are focused on systemic support to onboard crews that can operate and maintain in a minimal crew environment. And the activities define a vessel in this operating environment as a smart vessel and approach it as a prestage of fully autonomous vessels. In case of smart ships, studies like [2] are being conducted on monitoring and detecting abnormal situations in equipment that occur during operation on ships. In addition, studies [3] are being conducted to converge condition monitoring data and the cyber physical system and apply them to ships and offshore structures. These studies are related to systems supporting the maintenance in point of the Fail Safety, and the purpose of the Fail Safety is to support the sustainable operation of ships or offshore structures. The Fail Safety system consists of two main components, those are the diagnosis of the equipment status based on the monitoring information and supporting a proper maintenance plan based on the diagnosis result.
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