{
  "annotation_id": "20d3fbd1-916b-4b91-b7fb-b549867db5e9",
  "date_created": "2026-03-02T18:00:36.121000Z",
  "date_modified": "2026-03-02T18:00:36.121000Z",
  "file_hash": "84900520cad6db2627c60dbbc45d7cbbae884cc478c3f2dafea42bad0c372953",
  "private": false,
  "record": {
    "abstract": "In this paper, an automatic sensor validation strategy for the measurements\nof plasma line density in the ENEA-FTU tokamak is presented. Density\nmeasurements are performed by a 5-channel DCN interferometer. The approach\nproposed is based on the design of a neural model of the observed system., i.e.\na model able to emulate the behavior of a fault-free sensor and of a two-stage\nfuzzy system able to detect the occurence of a fault by using a set of suitable\nindicators. The fault diagnosis and classification is also accomplished. The\nvalidation strategy has been implemented and embedded in an interactive\nsoftware tool installed at FTU. Statistics concerning the rate of fault\ndetection agree with the rate of uncertainty usually achieved in the post-pulse\nmanual validation.",
    "arxiv_id": "physics/0111193",
    "authors": [
      "Giuliano Buceti",
      "Luigi Fortuna",
      "Alessandro Rizzo",
      "Maria Gabriella Xibilia"
    ],
    "categories": [
      "physics.ins-det",
      "physics.acc-ph"
    ],
    "journal_ref": "eConf C011127 (2001) WEAP031",
    "title": "An Automatic Validation System for Interferometry Density Measurements   in the ENEA-FTU Tokamak Based on Soft- Computing",
    "url": "https://arxiv.org/abs/physics/0111193"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1f0e72e7-3712-4c4d-9358-fe541fd80f97",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}