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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.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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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ORAU, ANS, others to host workshops on nuclear academic programs
Oak Ridge Associated Universities (ORAU), in partnership with the American Nuclear Society, the Nuclear Energy Institute, and the Institute for Nuclear Power Operators, has announced it will host an online workshop called “Shaping the Future of Nuclear Academic Programs.” The 90-minute program is designed for university department heads and faculty interested in enhancing nuclear science and technology programs through best practices.
Shengyuan Yan, Kai Yao, Fengjiao Li, Yingying Wei, Cong Chi Tran
Nuclear Technology | Volume 208 | Number 10 | October 2022 | Pages 1540-1552
Technical Paper | doi.org/10.1080/00295450.2022.2049965
Articles are hosted by Taylor and Francis Online.
The accurate assessment of human error probability (HEP) has an important impact on the safety of nuclear power plants. Therefore, it is necessary to develop a HEP model. This study analyzes the validity, sensitivity, and relationship between HEP and the indices of eye response and the subjective rating method. The analysis result showed that there is a correlation between HEP and the indices of eye response, subjective workload, and situation awareness level. Therefore, a back propagation neural network model was developed based on these indices. The correlation coefficient is more than 0.95 between the predicted data of the developed model and the target data. Also, the root mean square error was 0.0073, 0.0083, and 0.0077, and the determination coefficient was 0.965, 0.933, and 0.931 for the training, validation, and testing data sets, respectively. Therefore, the developed back propagation neural network model has reliable prediction accuracy for HEP.