A review of just‐in‐time learning‐based soft sensor in industrial process
Bibliographic record
Abstract
Abstract Data‐driven soft sensing approaches have been a hot research field for decades and are increasingly used in industrial processes due to their advantages of easy implementation and high efficiency. However, nonlinear and time‐varying problems widely exist in practical industrial processes. Just‐in‐time learning (JITL) was proposed to solve these problems and has attracted great attention in practical applications. To present a comprehensive review of JITL‐based soft sensor studies and provide detailed technical guidance for new researchers, this paper introduces the recent research on JITL‐based soft sensor modelling methods in the industrial process from three aspects: similarity criterion, sample subset, and local model, which include the whole process of establishing a JITL‐based soft sensor. Moreover, the future research and innovation directions of JITL‐based soft sensors in industrial processes are also prospected.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".