Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes
Bibliographic record
Abstract
Abstract The online measurement of key quality variables based on soft sensors plays a critical role in ensuring the safety and stability of batch processes. Recently, the relevant vector machine (RVM) was introduced into soft sensors for batch processes. However, the RVM‐based soft sensor has limitations in addressing the time‐varying, high‐dimensional, and dynamic data of batch processes. To address these issues, based on improved just‐in‐time learning and the relevant vector machine, an adaptive soft sensor, termed IJITL‐RVM, is proposed in this paper. The IJITL‐RVM integrates the IJITL algorithm and the RVM algorithm into a unified online modelling framework with the ability to perform adaptive updating and dynamic modelling. First, to enhance the performance of online prediction, an IJITL is designed to select modelling data based on the support vector data description (SVDD) algorithm and the kernel trick. Based on the comprehensive consideration of the strong nonlinearity and high dimensionality of process data, the IJITL can adaptively and accurately select the modelling data. Afterward, a local model is established by using the RVM for online prediction. Three applications, including a numerical simulation example, some UCI datasets, and a penicillin fermentation process, are provided to illustrate the superiority of the IJITL‐RVM‐based soft sensor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".