Soft sensor of processes based on dual attention spatio‐temporal interaction network
Bibliographic record
Abstract
Abstract Aiming at the problems of layer information loss as well as the effectiveness of process data spatio‐temporal feature fusion in stacked network‐based soft sensor methods, this paper proposes a dual‐attention spatio‐temporal interaction network (DA‐TSINET) method. Firstly, the dual‐attention stacked network is constructed to overcome the layer information loss. Self‐attention is added to different layers of stacked denoising autoencoder (SDAE) to enhance local denoising features, and global enhanced features are obtained by self‐attention fusion. A gated recurrent unit (GRU) and a convolutional neural network (CNN) are used in parallel to further extract spatio‐temporal relations, and an interactive gating module is designed for fusion to obtain globally enhanced spatio‐temporal features for constructing a soft sensor model. Simulation experiments are carried out by debutanizer column and thermal power plant and compared with stacked autoencoder (SAE), SDAE, stacked isomorphic autoencoder (SIAE), variable‐wise weighted SAE (VW‐SAE), and gated stacked target‐related autoencoder (GSTAE); the results show that the proposed method has high prediction accuracy, which verifies the effectiveness of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".