Academic Journal
MTL_TX: A Multi-Task Transformer Model for Improved Radiation Time-Series Estimation
| Τίτλος: | MTL_TX: A Multi-Task Transformer Model for Improved Radiation Time-Series Estimation |
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| Συγγραφείς: | Zhang, Hongfang, Stavola, Adam, Ferguson, Hal, Budavari, Bence, Wu, Hongyi, Kwan, Chiman, Li, Jiang |
| Πηγή: | Electrical & Computer Engineering Faculty Publications |
| Στοιχεία εκδότη: | ODU Digital Commons |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | Old Dominion University: ODU Digital Commons |
| Θεματικοί όροι: | Multi-task learning, Radiation dose estimation, Transformer, Artificial Intelligence and Robotics, Engineering Physics, Radiation Medicine |
| Περιγραφή: | Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed model: hierarchical feature embedding (HFE) and multi-level decomposition attention (MDA). Additionally, the multi-task learning (MTL) framework effectively leverages correlations among multiple sensors, enabling individual estimations for each sensor. MTL_TX achieved outstanding results on data collected in 2018, with an MSE of 0.1464, an RMSE of 0.2353, and an R² score of 0.8584. Furthermore, when trained on 2018 data, MTL_TX exhibited excellent generalization capability to unseen datasets from 2016 to 2019, achieving an MSE of 0.1407, an RMSE of 0.2263, and an R² score of 0.8831. These results demonstrate a significant improvement over existing state-of-the-art models. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | unknown |
| Relation: | https://digitalcommons.odu.edu/ece_fac_pubs/591; https://digitalcommons.odu.edu/context/ece_fac_pubs/article/1603/viewcontent/Zhang__2026__MTL_TX_A_Multi_Task_TransformerOCR.pdf |
| DOI: | 10.3390/s26051439 |
| Διαθεσιμότητα: | https://digitalcommons.odu.edu/ece_fac_pubs/591 https://doi.org/10.3390/s26051439 https://digitalcommons.odu.edu/context/ece_fac_pubs/article/1603/viewcontent/Zhang__2026__MTL_TX_A_Multi_Task_TransformerOCR.pdf |
| Rights: | © 2026 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License . |
| Αριθμός Καταχώρησης: | edsbas.5B016179 |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.3390/s26051439 |
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