Development of an Information Security-Enforced EEG-Based Nuclear Operators' Fitness for Duty Classification System

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 33
  • Download : 0
In a nuclear power plant (NPP), operator performance is a critical to ensure safe operation of the plant. The fitness for duty (FFD) of the operators should be systematically assessed before they engage in duties related to reactor operations. This study proposes the use of an electroencephalography (EEG)-based deep learning algorithm to classify an operator's FFD. To determine the suitability of this approach, EEG data were collected during simple cognitive exercises designed to examine the mental readiness of nuclear operators. The EEG-based FFD classification system designed could successfully determine an operator's sobriety, stress, and fatigue in a timely and cost-effective manner. As protecting personal information of the operators while using their EEG data is important and necessary, this study also investigated schemes for providing information security to the EEG-based FFD status classification system by following the International Organization for Standardization/International Electrotechnical Commission standard. Data confidentiality, integrity, and unlinkability were considered in the resulting schemes of information security for the EEG data. The resulting system provides the necessary protection of personal information and the FFD databases without significantly affecting the overhead of FFD classification through near real-time analysis.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-05
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.9, pp.72535 - 72546

ISSN
2169-3536
DOI
10.1109/ACCESS.2021.3078470
URI
http://hdl.handle.net/10203/285461
Appears in Collection
NE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0