Design of Fire-extinguishing Car with Intelligent Tracking and Obstacle Avoidance
Bibliographic record
Abstract
With the rapid development of urbanization, the incidence of fire accidents has also soared, posing a significant threat to people's lives and property safety. In this context, the intelligent fire-extinguishing car was born. STC89C52 microcomputer unit (MCU) is the control core. The system circuit comprises the minimum system of MCU (including crystal oscillator circuit and reset circuit), sensor module, tracking module, infrared obstacle avoidance module, ultrasonic obstacle avoidance module, steering gear module, fire extinguishing fan module, and infrared remote-control module. The system has two control modes: manual and automatic. In manual mode, the car can be controlled by MP3 infrared remote control to move forward, backward, left, and right, and the steering gear can be rotated so that the car can go to the destination to carry out manual fire extinguishing. In the automatic mode, the car patrols along the set route and automatically avoids obstacles, detecting the temperature of the surrounding environment. When a fire occurs, the car's fire source detection module sends a signal to the MCU, and the MCU determines the flame position and drives the steering gear to rotate the corresponding angle so that the fan faces the fire source to extinguish the fire.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".