Bibliographic record
Abstract
University control engineering coursesusually focus on Proportional Integral Derivative (PID)controllers. Moreover PID controllers are used in morethan 90% of the industrial control applications, becausethey are relatively cheap, easy to use, and robust enoughfor most industrial applications. However, universityteaching attaches most importance to the theoreticalknowledge of PID controllers, rather than the practicalskills required to support the use of these controllers inindustry. In addition, the cost and space challengesassociated with hands on laboratories make simulationbased laboratories a more attractive option for teachingPID controllers. Unfortunately, simulations do notcapture the complexity of control systems that areimportant to develop the practical skills of students. Inthis paper, we present a laboratory setup that is used toteach practical skills in PID tuning. The system controlsthe temperature of a small fictitious house whosetemperature is affected by an uncontrolled heating sourceand blow fan. The PID data is accessible to the systemuser through OLE( Object Linking & Embedding) forProcess Control, also referred as Open ProcessControl(OPC) technology. This technology allows thesystem to be used as a hands-on or remote laboratory,which allows students to learn the complexity of PIDcontrollers, while removing the time and spaceconstraints imposed by purely hands on laboratories.Being accessible remotely, the setup enables andencourages instructors to include demonstrations of PIDtuning into their lectures
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 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".