Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches
Bibliographic record
Abstract
The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer's control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results' robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".