A novel adaptive dynamic weight distribution (ADWD) strategy applied to support vector regression (SVR)-based framework for dynamic load identification problems
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Bibliographic record
Abstract
In this paper, a novel framework for dynamic load identification based on support vector regression (SVR) is proposed. A dynamic weight distribution (ADWD) strategy is first introduced to provide an arbitrary weight distribution to the displacement series input to the SVR model. Afterwards, multiple SVR models are sequentially integrated to identify load values and be utilised to determine the entire load curve. Using an improved differential evolution (DE) process along with a dual self-adaptive mutation operator (DSADE), the optimal control parameters are found for SVR frameworks and ADWD modules. The identification accuracy of the proposed SVR-ADWD and related basic frameworks is compared and discussed based on a traditional non-linear load-displacement dataset derived from drop hammer impact tests on circle aluminium tubes. The results show that the present method has the best performance without complex initial definitions.
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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 | 0.000 |
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