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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.
Hideki Kokame, Yoshikazu Nishikawa
Nuclear Science and Engineering | Volume 67 | Number 1 | July 1978 | Pages 8-18
Technical Paper | doi.org/10.13182/NSE78-A27233
Articles are hosted by Taylor and Francis Online.
The problem of rapid detection of an unexpected reactivity insertion into a nuclear reactor is studied assuming a stochastic point reactor model and noisy measurements of neutron density. The fundamental assumption is that the time dependence of the reactivity is given as in a ramp function with unknown coefficients. Thereupon, the present method applies a likelihood ratio test to the innovation sequence obtained by using a discrete Kalman filter, which is designed for the steady-state condition of reactor operation. By numerical experiment, the mean delay time for detection has been obtained under the condition that the mean time between false alarms takes on a prescribed constant. A comparative study with some typical existing methods shows that the proposed method is remarkably effective except for extremely large or small inputs of reactivity.