Automated critical point identification for PIV data using multimodal particle swarm optimization
Bibliographic record
Abstract
SUMMARY A hybrid sequential niche algorithm is used for the automated identification of critical points of velocity fields. This method combines an adaptive sequential niche technique with deterministic local optimization to detect critical points: focus, node and saddle points. A particle swarm algorithm performs a global search whereas vortex core identification functions compute the precise location as the extremum of the corresponding function. Once a critical point is found, a rectangular niche is constructed around the point. The particle swarm then proceeds to explore different regions of the velocity field. The process advances sequentially, avoiding areas near previously found critical points by blocking niches obtained from previous steps. The niche size is automatically adjusted each time a search enters inside an existing niche. Vortex core functions are used for critical point identification and calculating its precise location inside each niche. The procedure is validated on particle image velocimetry data obtained with two types of flows, an impinging jet flow and a flow downstream of a model building. The hybrid algorithm proved to be very efficient and robust for automated detection and identification of critical points. It can be used as a first step for studying the time‐dependent dynamic behavior of instantaneous velocity fields by tracking topological critical points. This is the first study that uses a multi‐modal particle swarm algorithm for critical point identification. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".