Bibliographic record
Abstract
An asymmetric damping force generation algorithm is originally proposed to yield the asymmetric force-velocity characteristics for the symmetric magneto-rheological (MR) dampers. The command current is formulated in an asymmetric manner to excite the symmetric MR dampers by employing the on-off control law in response to the direction of velocity, and a smooth modulation function is developed without phase shift to suppress strong transients in the damping forces caused by the current-switching discontinuity. The effectiveness of the proposed algorithm is evaluated by analyzing the dynamic responses of a quarter-vehicle suspension system with a symmetric MR-damper by modulating the command current into the asymmetric manner. The simulation results show that the proposed algorithm could achieve a better compromise between the conflicting requirements of the asymmetric damping force ratio and the force-velocity curve smoothness, and the asymmetric damping MR-suspension design can ideally improve the road holding and ride performances of vehicle motion. The proposed algorithm can be generally incorporated with a controller synthesis to realize an intelligent vehicle suspension design with the symmetric MR dampers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".