<title>Novel event identification for SHM systems using unsupervised neural computation</title>
Bibliographic record
Abstract
This paper explores the use of unsupervised neural networks and frequency sensitive competitive learning for novel event identification in structural health monitoring (SHM) systems. Our approach assigns a novelty metric based upon the output states of an SHM system. The technique can be applied in data decimation schemes, to enhance the monitoring of such systems, and as an aide to SHM data analysis. Learning units provide a means of characterizing an SHM system, and are subsequently used to assign a novelty metric to new SHM data. The system has been evaluated using data from the Taylor Bridge and Golden Boy statue in Winnipeg, Canada and the Portage Creek bridge in Victoria, Canada. The system is capable of analyzing SHM data from a 14-channel system, recording data at 32 Hz, using 32 learning units at approximately 30 times real-time on an AMD AthlonXP 2500+ based computer. The event identification system is most sensitive to SHM data which exhibits unusual power spectra, including data which shows abrupt changes in sensor outputs. The system may be cascaded in order to perform basic classification of events after identification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".