Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission
Bibliographic record
Abstract
In this paper, the authors intend to attest the applicability of computational intelligence for tackling a demanding real-life engineering problem, that is analysing the amounts of exhaust gas temperature () and the engine-out hydrocarbon emission () during the coldstart operation of an automotive engine with respect to the variation of engine speed (), spark timing (Δ) and air/fuel ratio. It has been proven that the coldstart phenomenon is highly transient, nonlinear and uncertain and therefore has absorbed an increasing attention of the researchers of automotive society. In this paper, we prove that a proper integration of intelligent methods can result in a very efficient tool suited for identifying the characteristics of coldstart phenomenon. To do so, a recent spotlighted natural-inspired optimiser called mutable smart bee algorithm is utilised to evolve a Gaussian generalised regression neural network such that the resulted framework can conduct a fast, robust and accurate identification. The outcomes of the conducted experiments endorse on the applicability of intelligent techniques for engine coldstart identification, and also, pave the way for future investigations on intelligent-based analysis of demanding automotive problems.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".