Self-Manageable System Architecture Design for Distributed Intelligent Automation
Bibliographic record
Abstract
A major challenge of extant industrial systems designed under traditional engineering techniques and running on legacy automation platforms is that these systems are unable to automatically discover alternative solutions, flexibly coordinate reconfigurable modules, and actively deploy corresponding functions, to quickly respond to frequently changes and intelligently adapt to evolving requirements in dynamic environments. This paper extends our research on the design of industrial cyber-physical systems to introduce a multi-layer self-manageable system architecture for IEC 61499 based distributed intelligent automation. The proposed architecture is designed to enable system-level run-time intelligence in the cloud and device-level real-time adaptation at the edge by integrating multi-agent modelling and IEC 61499 function block modelling. The proposed architecture is demonstrated and evaluated through various simulation tests on the agent-based simulation model of an automated conveyor system. The results show the ability of the proposed architecture to adapt the system autonomously to respond to frequent changes and evolving requirements typical of modern industrial environments in Industry 4.0.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".