Comparisons of Sensor Position for Electrical Capacitance Volume Tomography (Ecvt)
Bibliographic record
Abstract
Tomography is a technique used to produce true reconstructed images from signal data. This data projection is measured capacitance by numerous sensors located on the surface of the object at different position. Sensitivity matrix with three-dimensional variation, especially in axial (z-axiz) direction are required for imaging a three-dimensional object to differentiate the depth along the sensor length so that the electrical field intensity can be distributed equally all over the three dimension space. In ECVT, when a dielectric material is introduced into the vessel, the variation in the electrical capacitance between all possible combinations of electrodes are measured. These changes are caused by diference in the permittivity of that material. From these capacitance measurements, an image based on the variation of the permittivity of the cross section contents can be obtained. In this study a numerical model using combine COMSOL MULTIPHYSICS v3.5 and MATLAB 2008a for imaging of an object was developed. Three different position of rectangular sensor: 1-sided sensor, 3-sided and U-shape sensor was designed and analyzed. 1-sided sensor displayed comparatively more uniform in both radial and axial direction in the comparisons of sensitivity 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".