Application of Dempster-Shafer theory for fusion of lap joints inspection data
Bibliographic record
Abstract
In this work the Dempster-Shafer (DS) theory has been used for fusing nondestructive inspection (NDI) data. The success of a DS-based method depends on how the basic probability assignment (BPA) or probability mass function is defined. In the case of nondestructive inspection of aircraft lap joints, which is of interest here, the inspection data is presented in raster-scanned images. These images are discriminated by iteratively trained classifiers. The BPA is defined based on the conditional probability of information classes and data classes, which are obtained from ground truth data and NDI measurements respectively. Then, the Dempster rule of combination is applied to fuse multiple NDI inputs. The maximum mass outputs determine the final classification results. In this work, conventional eddy current (ET) and pulsed eddy current (P-ET) techniques were employed to inspect the fuselage lap joints of a service-retired Boeing 727 aircraft in order to map corrosion sites. Estimation of the remaining thickness from the inspection data is the aim of this work. The ground truth data was obtained by teardown inspections followed by a digital X-ray thickness mapping technique, which provides accurate thickness values. The experimental results verify the efficiency of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".