Antirollover Experimental Method for a Liquid Tank Semitrailer
Bibliographic record
Abstract
The liquid tank semitrailer has higher centroid and poor stability, and the vehicle is prone to rollover when turning or changing lanes at high speed. Thus, many companies have developed active antirollover systems in recent years. But the systems’ antirollover capabilities are different. However, there are no specific test conditions and test standards for antirollover systems. Taking this as a starting point, first, an automotive intelligent security cloud terminal and a multiaxis sensor are selected for the test data acquisition, and a remote data acquisition system based on a mobile signal is established. Second, a vehicle road test scheme with a free choice of route is designed. Set the rollover trigger conditions, obtain the test data through the database, and classify the data into dangerous scenarios. Third, the typical scenarios with rollover risk are obtained by data fitting. Finally, the typical antirollover system test conditions of the liquid tank semitrailer are obtained by optimizing and analysing the typical scenarios through the simulation software. The results show that the J-steering test with a turning radius of 45 m in both clockwise and counterclockwise directions can be used as an accurate typical test condition of the antirollover system of liquid tank semitrailers.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".