PHYSICAL AND VIRTUAL ENVIRONMENT FOR AUTOMATION EDUCATION ENGINEERS AND TECHNICIANS PART 1: ENGINEERING AN AUTOMATED RECYCLING FACILITY FOR BOTTLES AND CANS
Bibliographic record
Abstract
Nowadays, production equipments are controlled by programmable logic controllers (PLC) that can communicate over Ethernet. The technicians and engineers of the industry work together in order to develop, support, diagnose, solve, and optimize automated production systems. Current technology has attained such a level of sophistication that it is now possible to interact with a machine without having to move from ones chair. Faced by these observations, the Université du Québec à Rimouski(UQAR), the Cégep de Rivière-du-Loup, and Premier Tech Company, a world leader in the field of bagging equipment, have decided to join forces in order to improve the training of future engineers and technicians in the field of Industrial Automation and Control. The aim of the project is to allow university and CEGEP (college) students to work together on practical problems while being in two separate sites. The first step consists of designing an automated mini plant for the recycling of containers. The mini plant, designed and manufactured by UQAR's engineering students, has been installed in the CEGEP building located 100 km away from the University. The aim of the following step is to create a virtual environment allowing the follow up and visualization of the mini plant, to diagnose problems from a remote location, etc. Finally, the project focus on the development of training situation scenarios related to breakdown diagnostics, parameter adjustments, performance tests, security aspects, and process optimization. This article offers an overview of the project and of the mini plant as designed by the engineering students of UQAR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".