A parallel design optimisation method for articulated heavy vehicles with active safety systems
Bibliographic record
Abstract
This paper presents a parallel design optimisation method for Articulated Heavy Vehicles (AHVs) with Active Safety Systems (ASSs). In previous studies, a Genetic Algorithm (GA) has been applied to the design synthesis of AHVs and the objective function evaluations are computationally expensive. From a design point of view, the most challenging task is to deal with the trade-off relationship between unstable motion modes at high speeds and manoeuvrability at low speeds. To tackle the problem, a parallel computation technique with a master-slave system is proposed for the design of AVHs with ASSs. Active Trailer Steering (ATS), Differential Braking (DB) and Anti-Roll (AR) sub-systems are combined in an integrated ASS. Considering the interaction between the mechanical trailer and ASS, the proposed design method simultaneously optimises the active design variables of the controllers and passive design variables of the trailers in a single design loop using the masterslave computing system. The proposed method provides an effective approach to the design synthesis of AHVs with ASSs using a parallel computation technique.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".