Soft sensor of multiple operating condition processes based on input–output correlated difference focusing networks
Bibliographic record
Abstract
Abstract Aiming at the problems of information accumulation loss, quality correlation, and multiple operating condition feature extraction in soft sensor based on stacked networks, this paper proposes a method based on input–output correlation difference focusing networks (IO‐DFN). Constructing the input–output stacked isomorphic autoencoder (IOSIAE) network, it is proposed to reconstruct the original input variables and quality variables layer by layer in stacked autoencoder (SAE) to overcome the cumulative loss of the original input information and consider the quality correlation. A multi‐module architecture is constructed, where different modules all use input–output isomorphic autoencoders, and different loss functions are used during training to extract multiple operating condition features. It is proposed to use the inter‐module similarity self‐attention mechanism to highlight the differences of different module features, obtain the module focusing features, and establish multiple prediction models between them and the outputs. The correlation between each module focusing feature and the original input is calculated separately to further highlight the weights of the different module features, and the weights are used to fuse the different predicted values into the final predicted values. The results are validated by simulation of an industrial sulphur recovery process with a thermal power generation process, and the findings show the efficacy of the proposed approach.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".