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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".