An Intelligent Control System for Thermal Processing of Biomaterials
Bibliographic record
Abstract
An intelligent control system for thermal processing of natural biomaterials, based on machine vision, sensor fusion and neural network was developed. Experiments with ginseng drying showed advantages of machine vision for real-time imaging of morphological, colour and texture attributes, providing sufficient discriminatory information about biomaterial moisture and quality in the range of 3.2-0.1 g/g and temperatures from 30 to 50degC. Both moisture and quality was estimated by using neural network models: moisture with 6-8% error and quality with 10-16% error. Online estimates of moisture and quality were used for temperature control in pilot batch dryer. Testing of the intelligent control system with embedded machine-vision observer (IMAQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> Vision Builder) and controller (Lab View 7.0) showed stability and robustness, combined with high accuracy of temperature control. Multi-stage optimization of temperature with respect to quality allowed decrease of drying time from 240 to 90-110 hours with appropriate final quality.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".