Intelligent Fusion of Sensor Data for Product Quality Assessment in a Fishcutting Machine
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
This article presents three techniques of knowledge-based fuzzy sensor fusion, which are based on (1) Mamdani sup-prod composition, (2) degree of certainty, and (3) compatibility of data. The first method of sensor fusion uses Mamdani's sup-prod (or max-prod) composition, and it places equal weights on all the data sources, without considering their merit or importance. The second method uses the concept of degree of certainty. It assigns weights proportional to the degree of certainty of sensor data, and in addition to the fused output, it provides information about the certainty of the output. The third method of sensor fusion uses the idea of compatibility of data. It provides a fused output and additional knowledge about the degree of confidence in that output. This method is particularly effective when sensors provide conflicting information. The three techniques are implemented in an automated machine for mechanical processing of salmon, to determine the level of product quality (i.e., the quality of processed fish), and thereby evaluate the relative performance of the techniques. In this machine, process information is available from disparate sensors like CCD cameras, optical encoders, and an ultrasonic displacement sensor. Three sets of fish-cut data for a good, a bad, and a conflicting data cut are used in the illustrative example. The results indicate that the three methods are equally effective, but method 2, which is more sophisticated, has a slight advantage in performance over the other, at the expense of added complexity.
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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 | 0.002 | 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 | 0.001 | 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 it