Evolutionary adaptive discovery of phased array sensor signal identification
Bibliographic record
Abstract
Tomography, used to create images of the internal properties and features of an object, from phased array ultrasonics is improved through many sophisticated methods of post processing of data. One approach used to improve tomographic results is to prescribe the collection of more data, from different points of few so that data fusion might have a richer data set to work from. This approach can lead to rapid increase in the data needed to be stored and processed. It also does not necessarily lead to have the needed data. This article describes a novel approach to utilizing the data acquired as a basis for adapting the sensors focusing parameters to locate more precisely the features in the material: specifically, two evolutionary methods of autofocusing on a returned signal are coupled with the derivations of the forumulas for spatially locating the feature are given. Test results of the two novel methods of evolutionary based focusing (EBF) illustrate the improved signal strength and correction of the position of feature using the optimized focal timing parameters, called Focused Delay Identification (FoDI).
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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 | 0.002 |
| 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) | 0.001 | 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".