Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility-Reference-Cited by-同舟云学术

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Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility

Published:2024-08 Issue:8 Volume:71 Page:1966-1977
ISSN:0018-9499
Container-title:IEEE Transactions on Nuclear Science
language:
Short-container-title:IEEE Trans. Nucl. Sci.

Author:

Zhang Hongfang¹^ORCID,Stavola Adam¹,Ferguson Hal¹^ORCID,Budavari Bence²,Kwan Chiman³^ORCID,Wu Hongyi⁴,Li Jiang¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA

2. Applied Research LLC, Rockville, MD, USA

3. Force Projection Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA

4. Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA

Funder

U.S. Department of Energy, Office of Science Continuation Funding for Small Business Innovation Research (SBIR)/Small Business Technology Transfer

High-Performance Computing (HPC) Clusters Provided by Old Dominion University

U.S. Department of Energy, Office of Science, Office of Nuclear Physics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/23/10638189/10599097-aam.pdf

Reference50 articles.

1. U.S. DEPARTMENT OF ENERGY POLICIES, DIRECTIVES, AND GUIDANCE FOR RADIOLOGICAL CONTROL AND RELEASE OF PROPERTY

2. Three principles for radiation safety: time, distance, and shielding

3. A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting

4. Prediction of weather induced background radiation fluctuation with recurrent neural networks

5. EMI Radiation Prediction and Structure Optimization of Packages by Deep Learning

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