Identifying the behaviour of laser solid freeform fabrication system using aggregated neural network and the great salmon run optimisation algorithm
Bibliographic record
Abstract
Bio inspiration is a branch of artificial simulation science that shows pervasive contributions to variety of engineering fields such as automated pattern recognition, systematic fault detection, machine learning and applied optimisation. In this paper, a new bio-inspired optimisation algorithm which is the simulation of ‘the great salmon run’ (TGSR) is developed. Thereafter, it has been used to predict the efficient structure of an aggregated artificial neural network (AANN) to identify the behaviour of laser solid freeform fabrication (LSFF) system. Our experiments show that the combination of AANN and an appropriate supervised method is best suit for modelling cited engineering process. To prove the superiority of TGSR in both robustness and quality, it has been compared with most of the state-of-the-art optimisation techniques such as fast simulated annealing (FSA), parallel migrating genetic algorithm (PMGA), differential evolutionary with parent centric crossover (DEPCX), unified particle swarm optimisation (UPSO), shuffle frog leaping algorithm (SFLA), artificial bee colony (ABC), firefly algorithm (FA) and cuckoo search (CS). The obtained results confirm the acceptable potential of the proposed method to be applied on complex engineering systems.
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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 | 0.001 |
| 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 | 0.001 | 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".