Industrial prediction method based on graph sampling and aggregation of temporal features
Bibliographic record
Abstract
Abstract The soft sensor technique is extensively used to manage nonlinear and dynamic industrial process variables for predicting critical quality variables. Traditional soft sensor models face limitations in feature extraction and fail to capture the sequential dependencies among process variables. In order to resolve these difficulties, a graph sampling and aggregating temporal convolutional network (GTTL) is proposed that integrates a transformer encoder and a long short‐term memory (LSTM) network incorporating dual attention mechanisms. First, relevant feature variables with strong correlation are selected using the maximum information coefficient (MIC) and kernel density estimation (KDE) methods. Next, GraphSAGE is employed to sample and aggregate features from the local neighbourhood of nodes, serializing the extracted features. Subsequently, the filtered response normalization (FRN) technique is applied to optimize the residual structure in the temporal convolutional network (TCN), enhancing the model's generalization and robustness when processing heterogeneous data. The transformer encoder and the LSTM network with dual‐attention mechanisms effectively capture long‐range dependencies within the sequence, identifying process variables and time points associated with quality variable, thus enhancing the accuracy of quality variable predictions. Finally, the proposed soft sensor model's effectiveness and advantages are demonstrated using real industrial process datasets. The source code of this study is available at: https://github.com/Forgun/model.git .
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".