AI-Based Verification of Measurement Results from the OFPS for CANDU Spent Fuel Inspection
Bibliographic record
Abstract
The International Atomic Energy Agency (IAEA) conducts regular nuclear safeguards to ensure the peaceful use of nuclear energy and the secure management of nuclear materials. As part of these efforts, Physical Inventory Verification (PIV) is periodically performed to verify spent nuclear fuel bundles discharged from reactors. To support this process, the Korea Institute of Nuclear Nonproliferation And Control (KINAC) developed the Optical Fiber Probe System (OFPS), which has been applied to the PIV of CANDU (CANada Deuterium Uranium) spent nuclear fuel bundles at the Wolsong Nuclear Power Plant in Korea. However, high radiation fields within the facility can reduce the reliability of measurements, often necessitating additional visual inspections. To address this challenge, we previously proposed a method to simulate the operation of the OFPS and to generate radiation profiles using Monte Carlo techniques. In this study, we constructed a radiation profile database using the proposed simulation method and developed an artificial intelligence (AI) algorithm based on a Long Short-Term Memory (LSTM) network to detect missing bundles. The performance of the algorithm was evaluated using experimentally measured radiation profiles as test data. The algorithm demonstrated high reliability in identifying missing bundles within the simulated profiles and showed predictive capability when applied to experimental data. Future study aims to optimize the algorithm by generating diverse missing bundle scenarios and developing preprocessing methods for experimental data. The AI-based verification algorithm developed in this study is expected to enhance the reliability of automated PIV inspections using the OFPS.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".