Synthesizing data by transferring information in data‐intensive regions to enhance process monitoring performance in data‐scarce region
Bibliographic record
Abstract
Abstract Sufficient data are necessary for valid process monitoring results. However, modern industrial processes sometimes switch to new modes to meet the changes in market demand. The available data in such a new mode are initially quite scarce and it brings huge obstacles to data‐based model construction. In this paper, a novel data synthesis method based on variational autoencoders is proposed to generate synthetic data for the data‐scarce region. The proposed method utilizes not only the original data in the data‐scarce region but also the data in other data‐intensive regions, which share some common information with the scarce data. To avoid model biases caused by the data imbalance between these regions, a model correction mechanism is also developed. Once the ultimate synthetic data of the data‐scarce region are acquired, they are combined with the original data to establish a local monitoring model. Finally, the effectiveness of the proposed method is demonstrated through a real ammonia synthesis process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".