Nuclear energy system’s behavior and decision making using machine learning
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TLDR
In this paper, the authors used a neural network to predict the behavior of a small light water Reactor (SWR) during various core power inputs and a loss of flow accident.About:
This article is published in Nuclear Engineering and Design.The article was published on 2017-12-01 and is currently open access. It has received 41 citations till now. The article focuses on the topics: Artificial neural network & Group method of data handling.read more
Citations
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Journal ArticleDOI
Status of research and development of learning-based approaches in nuclear science and engineering: A review
Mario Gomez-Fernandez,Kathryn A. Higley,Akira Tokuhiro,Kent Welter,Weng-Keen Wong,Haori Yang +5 more
TL;DR: An overview of the fundamentals of artificial intelligence and the state of development of learning-based methods in nuclear science and engineering is presented to identify the risks and opportunities of applying such methods to nuclear applications.
Proceedings ArticleDOI
Tolerating Soft Errors in Deep Learning Accelerators with Reliable On-Chip Memory Designs
TL;DR: A Zero-Biased MNU-Aware SRAM Cell (ZBMA) is proposed for DNN accelerators based on two observations: the data in DNNs has a strong bias towards zero, and data flipping from zero to one is more likely to cause a failure of DNN outputs.
Journal ArticleDOI
Artificial neural network for predicting nuclear power plant dynamic behaviors
TL;DR: In this paper, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor.
Journal ArticleDOI
Artificial intelligence in nuclear industry: Chimera or solution?
TL;DR: This paper comprehensively analyses recent advancement in artificial intelligence for its applications in nuclear power industry and provides a critical assessment of various nuances of artificial Intelligence for nuclear industry.
Journal ArticleDOI
Integral PWR-Type Small Modular Reactor Developmental Status, Design Characteristics and Passive Features: A Review
TL;DR: This paper presents the design status, innovative features and characteristics of iPWR-type SMRs, delineate the common technology trends, and highlight the key features of each design.
References
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Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Journal ArticleDOI
Backpropagation applied to handwritten zip code recognition
Yann LeCun,Bernhard E. Boser,John S. Denker,D. Henderson,Richard Howard,W. Hubbard,Lawrence D. Jackel +6 more
TL;DR: This paper demonstrates how constraints from the task domain can be integrated into a backpropagation network through the architecture of the network, successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service.
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