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Evaluation of a DNN-assisted Track Analysis System for CR-39 Based Space Radiation Dosimetry
https://repo.qst.go.jp/records/2003113
https://repo.qst.go.jp/records/20031133e9850ee-101c-48da-9dcd-3a51f6bfe512
| アイテムタイプ | 会議発表用資料 / Presentation(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2026-03-30 | |||||||||
| タイトル | ||||||||||
| タイトル | Evaluation of a DNN-assisted Track Analysis System for CR-39 Based Space Radiation Dosimetry | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_c94f | |||||||||
| 資源タイプ | conference presentation | |||||||||
| 著者 |
フウ クン
× フウ クン
× 小平 聡
|
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| 抄録 | ||||||||||
| 内容記述 | A Deep Neural Network (DNN)-assisted track analysis system was developed to automate the detection and segmentation of particle tracks in CR-39 detectors for space radiation dosimetry. The system demonstrated robust performance in handling overlapping tracks and significantly improved analysis efficiency. To evaluate the performance of the system, we conducted a comparative analysis with conventional methods. The results demonstrate strong agreement in track counts and absorbed dose estimates, with an average difference of 4.7% in valid track numbers and 11.2% in absorbed dose. Despite challenges in classifying small or low-contrast tracks, the proposed DNN-assisted method offers a promising solution for high-throughput, reliable, and standardized CR-39 track analysis in space radiation dosimetry. | |||||||||
| 会議概要（会議名, 開催地, 会期, 主催者等） | ||||||||||
| 内容記述 | 第38回固体飛跡検出器研究会における成果発表 | |||||||||
| 発表年月日 | ||||||||||
| 日付 | 2026-03-26 | |||||||||
