Sensor fusion: a rough granular approach
Bibliographic record
Abstract
The paper introduces an application of a particular form of rough granular computing in fusing (combining) sensor readings. The intent of the paper is to describe a system that engages in a form of knowledge discovery based on sensor fusion. Such a system responds to sensor outputs in a manner that is selective, determines the relevance of each sensor in a classification effort, and constructs information granules computationally useful in arriving at a decision (proposed solution to a problem) in a problem-solving system. A sensor is a device that responds to each stimulus by converting its measured input to some form of usable output. Relevance of a sensor is computed with a rough integral that computes a form of ordered weighted average of sensor values. The construction of an information granule depends on the selection of a threshold for sensor values. Only those sensors with rough integral values approaching a selected threshold are fused (i.e., used to construct a granule). The contribution of the paper is the introduction of a sensor fusion method based on rough integration. By way of practical application, an approach to fusion of homogeneous sensors deemed relevant in a classification effort is considered.
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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 | 0.001 | 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 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".