Tensor product‐based model transformation approach to tower crane systems modeling
Bibliographic record
Abstract
Abstract This paper presents the application of the tensor product (TP)‐based model transformation approach to produce Tower CRrane (TCR) systems models. The modeling approach starts with a nonlinear model of TCR systems as representative multi‐input–multi‐output controlled processes. A linear parameter‐varying model is next derived, and the modeling steps specific to TP–based model transformation are proceeded to obtain the TP model. The TP model is tested on TCR laboratory equipment in two open‐loop scenarios considering chirp signals and pseudorandom binary step signals applied to the three model inputs (control inputs). The nonlinear and TP model outputs in the two scenarios are the payload position, the cart position, and the arm angular position. The nonlinear and TP model outputs are collected, measured, and compared. The simulation results prove that the derived TP model approximately mimics the behavior of the nonlinear model; both system responses and numerical approximation errors are illustrated.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".