A Modelling Framework to Design Executable Logical Architecture of Engineering Systems
Bibliographic record
Abstract
Modern production, logistic and assembly systems comprise of a considerable number of processes which operate by using diverse types of resources. Conceptual design of these systems has become more complicated because of the large scale and multi-disciplinary essence of their design knowledge. This paper proposes a modelling framework to support the conceptual design of such systems. The framework employs the principles of system engineering to fulfil the necessity of having a multi-disciplinary approach for the design of such systems. The framework realizes the essence of holistic design by modelling the structural and behavioral aspects of a system in one design artefact. Object Oriented (OO) method is employed to facilitate the complexity of holistic analysis and yielding proper logics for system architecting. This paper proposes incorporating the OO analyzing semantics into Finite State Machine formalism. Therefore, the logical architecture will be established in an FSM platform. In return, the resultant artefact can stand as an executable Meta-Architecture such that design alternatives are its instances. Moreover, the Meta-Architecture enables simulation of the alternatives which serve their early validation. Accordingly, this approach opens avenues regarding incorporation of the Meta- Architecture with computational and analyzing methods which can significantly support the decision making in the conceptual design stage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".