Formalization of Cyber-Physical System Interface using Discrete Event System Specifications
Bibliographic record
Abstract
Cyber-Physical systems are complex engineered systems that integrate embedded computing into the physical environment.These systems are considered to be "safetycritical" due to their application in the field of medical, transportation, or building control.CPS is composed of tight coupling of the physical and cyber worlds, and the interfaces are interactions that are defined as a bridge between these worlds.The interface is a fundamental characteristic of a CPS and CPS cannot function without it.In this thesis, we propose an architecture of the CPS interface in Discrete EVent System Specification (DEVS) that mimics the functionality of CPS interactions.DEVS provides a formal platform for M&S of discrete event dynamic systems.We propose a DEVS simulation model named DCIF (DEVS CPS Interface Framework) that portrays the complete working of the CPS interface.Later, we also implement and evaluate this interface on real-time hardware.The architecture is verified by creating a synthetic environment that includes multiple test cases in the simulation as well as real-time implementation.Additionally, we apply this framework to a practical case study in the field of building information modeling.
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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 | 0.005 | 0.011 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".