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Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Seunghwan Kim, Yochan Kim, Sun Yeong Choi, Wondea Jung, Jinkyun Park
Nuclear Technology | Volume 202 | Number 2 | May-June 2018 | Pages 259-277
Technical Paper | doi.org/10.1080/00295450.2017.1409053
Articles are hosted by Taylor and Francis Online.
It is well-known that one of the main causes of problems affecting social-technical systems, including nuclear power plants (NPPs), is human error. For this reason, reducing human error through human reliability analysis (HRA) is important. Furthermore, sufficient and reliable human performance data collection is a prerequisite for ensuring the safety of NPPs. The Korea Atomic Energy Research Institute developed the Human Reliability data EXtraction (HuREX) framework to provide a standard guideline for the collection and analysis of human performance data from operators in main control rooms based on simulator training records of NPPs. To do this, the development of a computerized software interface is required to collect simulator-based human performance data systematically and then to enter/analyze/quantify the various forms of data obtained from the simulator. In addition, a HRA database is needed for the effective management of the data generated during this process. In this research, we develop an interface that supports HuREX analysis so that HRA practitioners can conduct more effective HRA data analyses by integrating various types of raw data (e.g., audiovisual records, plant parameters, and operator action logs) collected from simulators. In addition, we expand the OPERA database to store a standardized data structure for more effective analyses of unsafe acts via the HuREX data analyzer and the HuREX video analyzer.