Disturbance Classification Utilizing Dynamic Time Warping Classifier
Why this work is in the frame
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Bibliographic record
Abstract
The application of deregulation policies in electric power systems results in the absolute necessity to quantify power quality. This fact highlights the need for a new classification strategy which is capable of tracking, detecting, and classifying power-quality events. In this paper, a new classification approach that is based on the dynamic time warping (DTW) algorithm is proposed. The new algorithm is supported by the vector quantization (VQ) and the fast match (FM) techniques to speed up the classification process. The Walsh transform (WT) and the fast Fourier transform (FFT) are adopted as feature extraction tools. The application of the combined fast match-dynamic time warping (FM-DTW) algorithms provides superior results in speed and accuracy compared to the traditional artificial neural networks and fuzzy logic classifiers. Moreover, the proposed classifier proves to have a very low sensitivity to noise levels.
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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 | 0.000 | 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) | 0.000 | 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 it