ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
September 2024
Nuclear Technology
August 2024
Fusion Science and Technology
Latest News
Taking shape: Fusion energy ecosystems built with public-private partnerships
It’s possible to describe fusion in simple terms: heat and squeeze small atoms to get abundant clean energy. But there’s nothing simple about getting fusion ready for the grid.
Private developers, national lab and university researchers, suppliers, and end users working toward that goal are developing a range of complex technologies to reach fusion temperatures and pressures, confounded by science and technology gaps linked to plasma behavior; materials, diagnostics, and electronics for extreme environments; fuel cycle sustainability; and economics.
Byoungil Jeon, Jinhwan Kim, Myungkook Moon
Nuclear Technology | Volume 209 | Number 1 | January 2023 | Pages 1-14
Technical Paper | doi.org/10.1080/00295450.2022.2096389
Articles are hosted by Taylor and Francis Online.
Radioisotope identification (RIID) is a representative application of deep learning for radiation measurements. Deep learning-based RIID models have been implemented in various types of radiation detectors; however, very few of these models have been interpreted using explainable artificial intelligence (XAI) methods. This paper presents an explanation of a deep learning–based RIID model for a plastic scintillation detector. The RIID task is defined as a multilabel binary classification problem, and the dataset is generated using a random sampling procedure. The identification performance is verified using experimental data. The experimental results demonstrate that the performance of the RIID models increased with the increase in the total counts of the dataset. Additionally, XAI methods are implemented, and their explanatory performance is verified for the spectral input. The domain knowledge of RIID for the plastic scintillation detector is that patterns near the Compton edge can be used as evidence for the existence of radioisotopes. Among the implemented XAI methods, integrated gradient and layerwise relevance propagation exhibited concurrence with the domain knowledge, with the Shapley value explanation method presenting the most reliable results.