A Conceptual Design of a Vision-Based Fire Fighting Robot for Smart City Application
Bibliographic record
Abstract
In the world today, fire incidence has been a frequent occurrence, this has caused the loss of many lives.Also, valuable properties, public utilities, and facilities have been destroyed.The study conceptualized a proposed design of an intelligent vision based robot for curbing the menace caused by fire outbreaks.The proposed design entails consistent remote interaction between a robot and sensor nodes.The robot node denotes the firefighting robot situated in the fire station.In its idle state, it operates in a passive node, by listening to the incoming beacons from the sensor nodes.The sensor nodes installed on designated sites consists of flame, wireless sensors with a mini-controller.Whenever the robot receives a distress alert from any of the sensor nodes, it automatically switches to an active mode and simultaneously navigates to the location in distress.The activity of the firefighting robot can be remotely monitored and controlled in real-time by a human operator via androidbased application.However, modifications have been proposed, based on the identified flaws of existing systems.Successful implementation of this design will provide a reliable and efficient means of monitoring multiple sites in real-time and also ensure environmental safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".