<title>Design and implementation of a dual-energy x-ray imaging system for organic material detection in an airport security application</title>
Bibliographic record
Abstract
In this paper, we describe the design and development of a dual-energy system used for x-ray screening of airport carry-on luggage. Dual-energy x-ray systems make it possible to measure the average atomic numbers of screened objects to enable their classification into three categories: inorganic, organic and mixed materials. Detection of organic materials, usually associated with dangerous compounds, mainly plastic explosives, is easier to achieve with dual- energy, as opposed to single-energy systems. The theory behind dual-energy systems is presented, followed by the design of a system based on a sandwich transmission detector arrangement with all its components, such as x-ray detectors, filter, operating tube, etc., and associated parameters are estimated according to simulation data. The process of generating the Z image, which includes the atomic number information from the two base images, is also described. After the prototype had been built, the unit was calibrated and images were taken with materials of known atomic number. Based on those measurements, the unit was tuned for optimal performance. Results comprise all decoding and compression tables for generating the images. Samples of Z and RO images taken from the unit are included and described in the report.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".