Bibliographic record
Abstract
Accurate modeling of industrial processes is an important topic in process systems engineering for further anomaly detection and fault diagnosis. Dynamics is inevitable in these processes, and several dynamic variants were proposed in the literature to extract both cross-correlations and auto-correlations between process variables and quality variables. However, all of them focus on the auto-correlations in process variables only, while the valuable auto-regressive information between collected quality variables is ignored. In this paper, a new dynamic auto-regressive partial least squares (DAPLS) method is proposed to capture the auto-correlations of both process and quality variables as well as the cross-correlations between them. In DAPLS, quality-relevant dynamics are exploited by maximizing the covariance between current quality sample and the weighted combinations of both past process and quality samples. Its inner modeling objective is also explicit and consistent with its outer model. The case studies with the numerical simulations and the Tennessee Eastman process have demonstrated the effectiveness of the proposed model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".