On the Comparison of Capacitance-Based Tomography Data Normalization Methods for Multilayer Perceptron Recognition of Gas-Oil Flow Patterns
Bibliographic record
Abstract
Normalization is important for Electrical Capacitance Tomography (ECT) data due to the very small capacitance values obtained either from the physical or simulated ECT system. Thus far, there are two commonly used normalization methods for ECT, but their suitability has not been investigated. This paper presents the work on comparing the performances of two Multilayer Perceptron (MLP) neural networks; one trained based on ECT data normalized using the conventional equation and the other normalized using the improved equation, to recognize gas-oil flow patterns. The correct pattern recognition percentages for both MLPs were calculated and compared. The results showed that the MLP trained with the conventional ECT normalization equation out-performed the ones trained with the improved normalization data for the task of gas-oil pattern recognition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".