Electrical Impedance Tomography using Differential Evolution integrated with a Modified Newton Raphson Algorithm
Bibliographic record
Abstract
Electrical impedance tomography (EIT) is a non-invasive medical imaging procedure. Image reconstruction in EIT is difficult because it involves solving a non-linear and ill-posed mathematical problem. One of the most commonly implemented inverse approaches is usually a variation of the Newton Raphson algorithm. However, this approach is not guaranteed to reach a global optimum or a local optimum and as such, it requires an accurate initial estimation of the resistance distribution, which is not always available in practice. In this paper, a new method is proposed to solve for the inverse problem in EIT while avoiding dependencies on the initial estimation of the resistance distribution. The proposed approach uses a differential evolution (DE) optimizer integrated with the Newton Raphson algorithm. The stochastic nature of DE allows the problem to be solved without having an accurate initial estimation and allows for solutions that will not be trapped in local minimal values. Simulation results indicate that the proposed approach outperforms the traditional differential evolution algorithm, and performs similarly to the traditional Modified Newton Raphson algorithm with accurate initial estimation. The proposed method does, however, have an advantage over the Modified Newton Raphson algorithm as it does not require an estimate of the initial resistance distribution.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".