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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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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Deep Space: The new frontier of radiation controls
In commercial nuclear power, there has always been a deliberate tension between the regulator and the utility owner. The regulator fundamentally exists to protect the worker, and the utility, to make a profit. It is a win-win balance.
From the U.S. nuclear industry has emerged a brilliantly successful occupational nuclear safety record—largely the result of an ALARA (as low as reasonably achievable) process that has driven exposure rates down to what only a decade ago would have been considered unthinkable. In the U.S. nuclear industry, the system has accomplished an excellent, nearly seamless process that succeeds to the benefit of both employee and utility owner.
Se Woo Cheon, Soon Heung Chang
Nuclear Technology | Volume 102 | Number 2 | May 1993 | Pages 177-191
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34815
Articles are hosted by Taylor and Francis Online.
Expert systems that have neural networks for their knowledge bases are called connectionist expert systems. Several powerful advantages of connectionist expert systems over conventional rule-based expert systems are discussed. The backpropagation network (BPN) algorithm is applied to the connectionist expert system for the identification of transients in nuclear power plants. In this approach, the transient is identified by mapping or associating patterns of symptom input vectors to patterns representing transient conditions. The general mapping capability of the neural network allows one to identify a transient easily. A number of case studies are performed with emphasis on the applicability of the neural network to the classification problems. Based on the case studies, the BPN algorithm can identify the transient well, although untrained, incomplete, sensor-failed, or time-varying symptoms are given. Also, multiple transients are easily identified with a given symptom input vector.
