Bibliographic record
Abstract
Accuracy and efficiency of alarm systems are of paramount importance in safe operations of industrial processes. Accuracy is measured by false and missed alarm rates; while efficiency relates to the detection delay and complexity of the technique used. Moving average filters are often employed in industry for improved alarm accuracy at the expense of some detection delay. Can one do better than moving average filters? The following problem is studied in this paper: Given statistic distributions of both normal and abnormal conditions, and relatively fixed filter complexity, design an optimal alarm filter for best alarm accuracy, minimizing a weighted sum of false and missed alarm rates (probabilities). It turns out that the general form of such optimal alarm filters is the so called log-likelihood ratio (LLR) filters, which can be highly nonlinear and difficult to implement in practice. With fixed filter structures (first or second order), design of optimal linear alarm filters and optimal quadratic alarm filters is studied, and numerical optimization based procedures are proposed. Some key elements in the optimal design include use of characteristic functions from probability theory to facilitate computation of the objective function, and a DE algorithm for optimization (the optimization problem is non-convex and with small gradients in some area). The validity of the proposed methods is illustrated by several design examples in which the optimal filters in the general, linear and quadratic forms are computed, and their relative performance in alarm accuracy is fully discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".