Increasing the operating depth of an autonomous underwater vehicle using an intelligent magnetic field
Bibliographic record
Abstract
Designing and manufacturing a suitable body is one of the most effective factors in increasing the efficiency of autonomous underwater vehicles (AUVs). In fact, increasing the propulsive power of an AUV by reducing the frictional drag on its body and incre asing its maneuverability will positively affect key parts of the AUV’s hardware and software such as control system, sensors, AUV vision, batteries and thrusters. On the other hand, a suitable body should have features such as lightness, underwater vehicl e’s balance, high mechanical strength, and enough space for equipment. Therefore, the design and manufacture of the body requires a lot of analysis in terms of body material, aerodynamic calculations, etc., increases the overall cost. This paper aims to re duce the stress in the body of a Polytetrafluoroethylene ( PTFE ) underwater robot and to increase its operating depth without changing the body’s structure by using fuzzy logic to intelligently controlling the magnetic force generated by the repulsion betwe en the coil and the cylindrical magnet, which saves energy, reduces battery consumption, and increases system performance. The results show that the robot performance depth increases by more than 50% without changing the robot body structure.
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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 | 0.000 | 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 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".