Optimization Drive on a Flat Tire Vehicular System for Autonomous E-Vehicles Using Network Distribution Simulations
Post-publication record
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Bibliographic record
Abstract
The run-on flat tire is an important technology in the area of vehicle safety technology. Nowadays, traveling by a personal vehicle from one place to another has increased due to the increase in the global economy. The main problem that is faced while traveling is tire puncture. The tire may get punctured by any sharp objects on the road such as screws and iron pins that traps the tire surface. A situation like this may be overcome by the usage of the drive on a puncher tire device which will be used to reach the destination without any need for a puncher shop. Using this device will reduce the damage of the tire and tube. This device is portable and can be placed in a vehicle itself. For this reason, a design has been developed by the CATIA 3D experience. The drive on a puncher tire device was designed using the software developed by the Dassault systems named CATIA 3D experience. Aluminum alloy was chosen as the base material to design and simulate this product. Also, the network distribution simulations were used to develop the mathematical equations to check the adequacy of the model. This device is very useful while traveling long distances if any puncher will occur. The sensors were attached to the product to receive the signal from the vehicle, so that the vehicle will run smoothly for a short distance. Drive on a puncher tire will be suitable for both two- and four-wheeler vehicles and also useful for all the areas like villages, towns, panchayats, and cities. If the tire is puncher, we cannot drive the vehicle; hence, a skating device is fixed at a tired bottom, which is used to move the vehicle. The proposal will be beneficial for a huge number of people who own two and four wheelers. This design and simulation will address the problem of the two- and four-wheeler owners traveling long distances.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".