Electrical Impedance Computed Tomography -algorithms And Applications
Bibliographic record
Abstract
The reasons for the low resolution of the Electrical Impedance Computed Tomography(E1CT) algorithms have not been fully explored. Previous investigations focused on the dis- cussions of numerical features of an algorithm. This paper dis- cussed such problems by implementing Point-Accumulative Point-Iterative algorithms to multi-port resistive networks ac- cording to the similarities of the Finite Element method and lin- ear network analysis. The results indicate that improper mea- surement pattems in EICT can cause an EICT algorithm's failure although the number of independent measurements are still higher than the number of unknowns. With the help of graph theory, it is shown that the image quality of EICT is not only dependent on the numerical features of an EICT system, but also on its topological structure. An optimal excitationl measurement pattem algorithm in topological sense is then pro- posed. Successful simulations in the twociimensional field problems are performed. Suggestions to the three-dimensional applications of EICT are made based on the results from the multi-port resistive network recovery.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".