Underdetermined Blind Identification of Structures by Using the Modified Cross-Correlation Method
Bibliographic record
Abstract
The modified cross-correlation (MCC) blind identification method is extended to handle the underdetermined case of structural system identification. The underdetermined case is one in which the number of sensors is less than the number of identifiable modes. The basic framework of the modified cross-correlation method is retained in cases in which multiple covariance matrices constructed from the correlation of the responses are diagonalized. The solution to the underdetermined blind identification consists of two stages: the generation of intrinsic mode functions (IMFs) from the measurements by using empirical mode decomposition (EMD) and the application of the modified cross-correlation method to the decomposed signals. The available measurements are first decomposed into IMFs by using the sifting process of EMD. Subsequently, the IMFs are used as initial estimates for the sources, and the MCC method is implemented in an iterative framework. Initial estimates for the mixing matrix necessary to start the iterative process are selected using assumed shape functions that satisfy the essential boundary conditions. The need for sensor measurements at all the relevant degrees of freedom (DOF) to identify the mode shapes is alleviated in this approach. This is the main advantage of the proposed method. Vibration responses collected from the apron control tower located at the Toronto Pearson International Airport are used for demonstration.
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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 | 0.004 |
| 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 | 0.001 |
| 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".