Articulated heavy vehicle lateral dynamic analysis using an automated frequency response measuring technique
Bibliographic record
Abstract
This paper proposes the application of an automated frequency response measuring (AFRM) technique to the lateral dynamic analysis in the frequency-domain for articulated heavy vehicles (AHVs). The AFRM method can be used to automatically acquire the frequency response function of the rearward amplification (RWA) ratio, an important performance measure of the lateral stability of AHVs. A numerical simulation in the time-domain can only achieve the corresponding dynamic response at a specified frequency; to achieve dynamic responses over a frequency band, numerous simulations at various frequencies have to be conducted, and this is a tedious and time consuming process. The RWA measure is frequency-dependent, and the measure in the frequency-domain is of critical importance for the stability evaluation and design synthesis of AHVs. The feasibility and effectiveness of the proposed method is examined using numerical simulations of a tractor/semitrailer combination represented by a linear and a nonlinear AHV model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".