SENSORY INFORMATION ACQUISITION FOR MONITORING AND CONTROL OF INTELLIGENT MECHATRONIC SYSTEMS
Bibliographic record
Abstract
Mechatronic systems are electromechanical systems where an integrated design procedure is used with respect to mechanical and electrical aspects. These systems possess computational intelligence with capabilities such as perception, learning, reasoning, and making inferences (or, decisions) from incomplete information. Concepts of sensing and information acquisition are crucial for mechatronic systems in all stages of design, development, testing, operation, and maintenance. This paper outlines the considerations of sensing and information acquisition for intelligent mechatronic systems. A typical mechatronic system operates as a self-contained unit and involves direct information acquisition. There are situations, however, where a group of mechatronic systems have to operate in a coordinated manner, as in a mechatronic workcell. Also it may be necessary to monitor a mechatronic system remotely and to share common resources between several applications. Networked control is needed then. This issue is also addressed in the paper. Several industrial applications of intelligent mechatronics have been developed in our Industrial Automation Laboratory. Some of them involve cutting, inspection, and grading of fish products. The paper presents a representative application, by highlighting information acquisition and the integration of sensing and control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".