An adaptive fuzzy logic controller for intelligent drying
Why this work is in the frame
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Bibliographic record
Abstract
A systematic approach to the design of an adaptive fuzzy logic controller (AFLC) for intelligent drying with a computer vision system (CVS) in a feedback loop is proposed. Developed AFLC is based on an artificial neural network (ANN), geno-fuzzy algorithm, and multi-objective fuzzy cost function. Fuzzy sets for the moisture content and product quality are automatically generated by using principal component analysis (PCA) and fuzzy clustering. In addition, the concept of fuzzy time is introduced to optimize the duration of each control step. The fuzzy rule base for the controller was constructed through a two-stage process of (i) warming-up based on simulation and optimization (offline) and (ii) fine-tuning during real-time drying (online). The application of AFLC for shrimp drying showed advantages of the unsupervised fuzzy logic control, such as decreased drying time, less quality degradation, and smaller energy consumption.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it