Bibliographic record
Abstract
This paper describes the design and implementation of a senior level course in mechatronic system integration for students completing a mechatronics engineering option in mechanical engineering. The course is designed to give students theoretical and practical experience with a large-scale mechatronic system, and a variety of control, sensing and actuating architectures. The lecture component of the course introduces students to large-scale project integration and interface design, as well as system architecture design. Students learn about alternative control hardware platforms commonly used in industry, such as motion control hardware, field programmable gate arrays and programmable logic controllers. The selection and system integration of various industrial sensors, including vision, are presented. Students also learn about networked control and discrete event control approaches for large-scale industrial systems. The course contains a significant practical laboratory component. In a series of laboratory sessions, students develop and implement subsystems of a part sorting machine, culminating in the integration and demonstration of an automated, autonomous, sensor driven electro-mechanical system for sorting randomly delivered parts. The course offers students a theoretical background as well as significant practical experience with large scale mechatronics systems, as would be encountered in industry. This paper describes the lecture and laboratory content, and the experiences from the first offering of the course.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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".