Locality preserving based data regression and its application for soft sensor modelling
Bibliographic record
Abstract
Abstract A new local‐based data regression technique named locality preserving regression (LPR) is developed and applied for soft sensor modelling in the present study. By taking the local variation obtained by locality preserving projections into consideration, the regression algorithm LPR is employed to construct a soft sensor model and applied to industrial case. Furthermore, to deal with the time‐varying behaviour of the process variables, just‐in‐time learning is also integrated to regularly update the soft sensor. Two case studies on a fermentation process for penicillin concentration prediction and the Tennessee Eastman process for output component prediction are provided to demonstrate the performance of the proposed method. Finally, the effectiveness and robustness of the proposed local‐based technique for soft sensor modelling are assessed and compared with the global‐based soft sensors based on the mean square error and the coefficient of determination. Experimental results showed that the novel soft sensor model could estimate the output with higher accuracy and generalization ability than the general soft sensor based on the global information.
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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".