Designing and implementing a multi-function board to increase the operation time of mobile robots using solar panels
Bibliographic record
Abstract
Today, the use of mobile robots and autonomous vehicles has increased due to their use in various industries, and their performance and duration of operation largely depend on the amount of energy consumed and their batteries. One of the ways to increase the operation time of robots is the use of solar panels that can charge their batteries while moving, but the amount of energy received from solar panels reduces their efficiency due to factors affecting them, such as the angle of the sun, weather conditions, and their use in mobile robots alone is not recommended. In this research, we introduce an electric circuit with very low losses to increase the received power of solar panels and increase their efficiency, which is able to supply the power of the robot through solar panels when the sunlight and the angle of radiation are suitable and charge the batteries through the maximum power point controller (MPPC), and by reducing the amount of energy received from the panels by changing the energy source to the battery, the duration of the system’s dependence on the battery has decreased, which increases the duration of the mobile robots.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".