3rd World Congress on Industrial Process Tomography, Banff, Canada Application of Simulated Annealing and Genetic Algorithms to the Reconstruction of Electrical Permittivity Images in Capacitance Tomography
Bibliographic record
Abstract
In this work we apply the simulated annealing (SA) and genetic algorithms (GA) inversion methods to the reconstruction of permittivity images from electrical capacitance tomography (ECT) measurements. The forward problem (i.e., to find the mutual capacitance data for a given permittivity distribution in the sensor) is calculated by using a finite-volume space discretization method in order to avoid singularity problems at the centre of the pipe (as occurs with the finite difference method), and to take advantage from the conservative formulation of finite element methods. We test the GA and SA inversion methods using static physical models simulating the typical distribution patterns of twocomponent flow. The GA and SA-based permittivity inversions have some advantages over other approaches based on damped least-squares inversion: they can find good solutions starting with poor initial models, can more easily implement complex a priori information, and do not introduce smoothing effects in the final permittivity distribution model. A major disadvantage comes from the fact that GA and SA are computationally intensive and lead to relatively slow reconstructions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.022 |
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".