Kalman filtering approach to multirate information fusion for soft sensor development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate and frequent measurements of quality variables are important for real-time process monitoring and control. However, because online measuring instruments commonly have the limitations of high investment and low accuracy, while offline laboratory analyses are obtained manually and infrequently, neither online instruments nor offline analyses can satisfy the requirements of real-time applications in industries alone. In order to obtain more reliable information of quality variables, this paper develops two kinds of adaptive soft sensors in the framework of Kalman filter. The idea is to take the advantages of fast-rate sampling of online data and high-accuracy of lab data by synthesizing these two sources of measurements at different sampling rates. The CSTR case study and applications in a chemical production process demonstrate the effectiveness of the proposed methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it