Vibration Signal Features Prediction of GIS Equipment Based on Improved Slime Mold Optimization Algorithm Optimizing CNN-BiLSTM
Why this work is in the frame
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Bibliographic record
Abstract
The change of vibration signal of GIS equipment can reflect the internal mechanical state. In order to improve the prediction accuracy of vibration signal characteristics of GIS equipment, this paper proposes an improved slime mold algorithm to optimize CNN-BiLSTM GIS vibration characteristics prediction method. First, the vibration characteristic parameters are extracted in the frequency domain based on the GIS historical vibration signal through Fourier transformation. Secondly, in order to enhance the feature utilization ability of BiLSTM model, 1D CNN is used to extract feature parameters; the differential evolution strategy is integrated into the slime mold optimization algorithm and the parameters such as the number of hidden layer neurons and learning rate of CNN-BiLSTM are optimized. Finally, an improved slime mold algorithm is established to optimize the GIS feature prediction model of CNN-BiLSTM. The experimental results show that the root mean square error and mean absolute percentage error of the DESMA-CNN-BiLSTM model are 1.7915 and 0.1317%, respectively, which are better than other methods in prediction accuracy. The improved algorithm proposed in this paper has strong global search ability and fast convergence speed.
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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) | 0.001 | 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 it