Polynomial matrix sparse coding algorithm for extracting non-electrical signal characteristic parameters of key components of high-voltage direct current converter valves
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Bibliographic record
Abstract
Sparse decomposition has been generally emphasized in signal processing theory.In this paper, a nonelectrical signal feature dataset of key components of high-voltage DC converter valve is established by using principal component analysis to streamline the data volume.The compression-aware feature extraction algorithm based on polynomial matrix sparse coding is used to extract and collect the nonelectrical signal parametric data.Through the performance over the experimental signal analysis, it can be known that the eigenvalues of a total of 10 parameters, including the infrared temperature measurement results, the appearance, the presence of corrosion or dirt, and the presence of abnormal vibration and sound, are all greater than 1.Therefore, these 10 parameters are identified as the key parameters.When the number of measurement points is between 64 and 200, the algorithm in this paper can satisfy the need of feature extraction when the signal length is insufficient, compared with the traditional approach.In the empirical analysis of the vibration signal as an example, the method of this paper can effectively extract the frequency and time domain of the vibration signal.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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 it