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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Young A Suh, Man-Sung Yim
Nuclear Technology | Volume 206 | Number 12 | December 2020 | Pages 1840-1860
Technical Paper | doi.org/10.1080/00295450.2020.1731405
Articles are hosted by Taylor and Francis Online.
Human error has been highlighted as main cause of industrial and nuclear accidents. One of the key issues related to human error is a worker’s fitness for duty (FFD). FFD refers to the mental and physical ability of employees to safely perform their job. The objective of this study is to investigate the feasibility of identifying a worker’s FFD status using biosignals. The FFD statuses examined were with respect to alcohol use, depression, stress, anxiety, and sleep deprivation. Biosignals examined in the study include electrical activity in the brain measured by electroencephalogram and referred to as EEG, electrical activity of the heartbeat measured by electrocardiogram and referred to as ECG, galvanic skin response (GSR), blood volume pulse (BVP), dynamic changes in blood pressure and referred to as BPHEG, and respiration. A total of 114 volunteers participated in the study as experimental subjects from whom biodata were collected during their resting states (eyes closed and eyes open). The steps followed in the study include signal preprocessing, power spectrum feature analysis, important feature selection, and support vector machine (SVM) classification using 5-fold cross validation to identify a worker’s FFD status. Among the 70 biosignal indicators, important features were selected by Multivariate Analysis of Variance (MANOVA). The best model developed with the SVM used 64 biosignal indicators and showed a binary (fit or unfit) classification accuracy of 99.4% and a multi-classification accuracy of 97.7%. While limitations of the current work remain, the study indicates the possibility of implementing an effective FFD management program to reduce human error in plant operations.
A thumbnail sketch of the study is as follows:
1. To reduce human error in nuclear operations, use of biosignals was investigated to identify FFD status of workers.
2. EEG, ECG, GSR, BVP, BPHEG, and respiration signals were used to identify a worker’s FFD status.
3. The SVM-based model was successfully implemented for multi-class and binary-class FFD classification.
