Cyber-physical system architecture for real-time warehouse operations
Bibliographic record
Abstract
Cyber-Physical Systems (CPS) combine computational and physical processes to enable real-time monitoring, control, and decision-making in industrial environments. This paper presents a CPS architecture designed for real-time warehouse operations within the Intelligent Cyber Physical Systems (I-CPS) laboratory testbed at Polytechnique Montreal, Quebec, Canada. The proposed CPS architecture follows the IEC 62264 standard and integrates standards-based industrial protocols to enable communication between components, including Programmable Logic Controller (PLC), Decision Theater for real time interactive decision making, Autonomous Mobile Robots (AMR), Desktop Computer Numerical Control (CNC) machine, Radio-Frequency Identification (RFID), proximity sensors, Quick-Response (QR) code reader, and camera vision system. Real-time data acquisition demonstrated a processing rate of 25 cycles per second (0.04 seconds per batch), supporting synchronization with a Digital Twin for operational monitoring. The proposed CPS architecture lays the foundation for addressing challenges in anomaly detection by introducing anomaly injection techniques as a future direction to mitigate the lack of naturally occurring anomalies. It contributes to the evolution of CPS architecture tailored for Industry 4.0 applications.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".