Fast Match-Based Vector Quantization Partial Discharge Pulse Pattern Recognition
Bibliographic record
Abstract
A novel approach for the classification of cavity size in terms of their apparent charge versus applied voltage (/spl Delta/Q-V) partial discharge pattern characteristics is described. The method makes use of the fast match-based vector quantization procedure, wherein a given partial discharge pattern is matched against a set of known partial discharge patterns in a database. The /spl Delta/Q-V partial discharge patterns for different cavity sizes are considered as a sequence of events rather than as /spl Delta/Q-V curve representations. In the training phase, each cavity size represents a unique class, which emits its own /spl Delta/Q-V sequence, and vector quantization (VQ) is used to assign labels for this sequence of events. In the testing phase, a fast match algorithm is proposed to determine the degree of similarity between the labels of the tested phenomena and the prestored labels for different partial discharge patterns previously stored during the training phase. The best-matched model pinpoints the cavity size class. The results demonstrate that while the implementation of such classifier is simple, it achieves high classification rates; this positions the method as a competitive alternative vis-a/spl grave/-vis other previously proposed classifiers, which suffer from both larger computational burdens and inherently more complicated structures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".