Singular value decomposition of plant matrix in active noise and vibration—some examples
Bibliographic record
Abstract
In active noise and vibration control problems that involve many secondary sources and error sensors, the active control performance is largely related to the conditioning of the plant matrix (formed by the transfer functions between individual secondary sources and error sensors). The principal component transformation (or singular value decomposition) of the plant matrix is an interesting tool to extract dominant secondary paths and limit control efforts. Furthermore, it can be used in a feedforward LMS controller to prevent slow convergence due to ill-conditioning of the plant matrix, and adjust the convergence rate of individual system modes. This approach is discussed through two different applications: (1) the multi-harmonic active structural acoustic control of a helicopter main transmission noise using piezoceramic actuators; (2) the broadband, adaptive sound field synthesis using multiple reproduction sources. It is shown that the approach allows decreasing the required signal processing and limiting the magnitude of the control inputs. Furthermore, in the case of sound field reproduction, it allows a very elegant and insightful interpretation in terms of controlling independent radiation modes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".