Identification of large-scale systems with noisy data using an iterated cubature unscented Kalman filter
Bibliographic record
Abstract
Online structural health monitoring of large-scale models of infrastructures under hazardous environmental loadings— like earthquakes—has been a vital research topic during recent years. A linear Kalman filter has been employed in many cases in which the desired parameters are extracted in a propagated state vector during a recursive regime. Also, many other kinds of nonlinear filters have been developed for nonlinear systems identification following the linear Kalman filter concept, such as the unscented Kalman filter and the cubature Kalman filter. The main contribution of these two Kalman filtering techniques relies on the propagation of a covariance matrix instead of nonlinear transition and measurement functions. Our extensive literature review shows that divergence of estimated states for large degree-offreedom (DoF) models is the main drawback of these techniques. To overcome this weakness, these two filters’ predefined points, sigma points, are combined—with some modifications—to have more predetermined points for the propagation of states and output of covariance matrices. The proposed technique was developed to be used for large DoF systems with a high level of noisy measured data, which indicates a robust identification system. To evaluate the proposed method, a numerical model (10 DoF linear system) with high levels of noise in the measured response data are employed to evaluate the robustness of the proposed method. The results show that the proposed method is significantly superior to the traditional UKF for noisy measured data in systems with large degrees of freedom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".