Development of A Smart Rescue Communication System for Drowning Personnel
Bibliographic record
Abstract
Recently, there is an alarming increase in the number of deaths resulting from late rescue of drowning personnel falling overboard. Most mechanisms deployed are faced with the inability to detect the exact location of the drowning person especially when completely submerged in water. This paper therefore describes the development of a smart rescue communication system for drowning personnel. The developed system considered two major activities involved in drowning: the first scenario considers when the individual is completely submerged in water and the second; when the victim is struggling to survive. Thus, water and vibration sensors are useful input devices in actualizing the work. Arduino microcontroller was also used for the control system mechanism. For drowning and drowned situations which is detected and specified by the readings from the relevant sensors, an SMS (short message system) alert is communicated to the rescue personnel’s phone indicating that there is an emergency. The exact location of the man overboard is also indicated in the SMS with the aid of the global positioning system (GPS) module. The SMS is sent at specified intervals to increase awareness of the current situation to aid fast rescue operation. The prototype is designed to be a wearable device.
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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) | 0.005 | 0.002 |
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".