Application of artificial neural networks to nuclear power plant transient diagnosis

Santosh, T.V.; Vinod, Gopika; Saraf, R.K.; Ghosh, Ashish; Kushwaha, H. S.

doi:10.1016/j.ress.2006.10.009

Cited by 102 publications

(28 citation statements)

References 5 publications

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“…The key point is to determine the balance between holism and reductionism. The major development trend in nuclear risk assessment in the future for systems methods will be deeply integrated with AI (63,(82)(83)(84)(85)(86)(87), big data (88), dynamic uncertain causality graphs (89), and other new technologies, with the goal of predicting the faults of complex systems.…”

Section: Technical Humility-based Risk Assessmentmentioning

confidence: 99%

Nuclear safety in the unexpected second nuclear era

Chen

Wang

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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Nuclear energy development has entered an unexpected second nuclear era, which is mainly driven by developing countries. Despite major efforts to pursue a safe nuclear energy system in the first nuclear era, severe nuclear accidents occurred. A basic problem is that we do not have an adequate understanding of nuclear safety. From the viewpoints of risk and the close coupling of technical and social factors, this paper reexamines the nature of nuclear safety and reviews how previous experts understood it. We also highlight the new challenges that we are likely to confront in the unexpected second nuclear era and clarify some of the refinements that need to be made to the concept of nuclear safety from a sociotechnical perspective. These include the following: 1) Risk decisions should be made based on integrating social and technical elements (i.e., “social rationality”); 2) risk needs to be controlled based on the “Wuli–Shili–Renli” framework; 3) systems thinking should be substituted for reductionism in risk assessment, and social mechanisms need to be combined to address uncertainties; and 4) public-centered risk communication should be established. This contribution can provide a theoretical foundation for improving our understanding of the nature of nuclear safety and for transforming the concept of nuclear safety in the unexpected second nuclear era.

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Section: Technical Humility-based Risk Assessmentmentioning

confidence: 99%

Nuclear safety in the unexpected second nuclear era

Chen

Wang

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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“…For the same purpose and in the same way as the response surfaces are used, neural networks may be applied (Pedroni et al, 2010;Montes et al, 2009;Cadini et al, 2008;Secchi et al, 2008;Santosh et al, 2007;Gozalvez, 2006;Na et al, 2004;Ortiz and Requena, 2004;Faria and Pereira, 2003). Compared to response surfaces, neural networks are a more sophisticated tool for describing nonlinear phenomena.…”

Section: Various Approaches For Reducing the Number Of Simulationsmentioning

confidence: 99%

Statistical analysis of fuel failures in large break loss-of-coolant accident (LBLOCA) in EPR type nuclear power plant

Arkoma

Hänninen

Rantamäki

et al. 2015

Nuclear Engineering and Design

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“…In the near future, neuromorphic networks are likely to find application in harsh, radiation prone environments such as space and at nuclear and military installations. Currently, researchers are developing neural networks that could be used in solar radiation forecasting, large data capturing, object classification and matching, event filtering, facial recognition, combat automation, target identification and weapon optimization [9,17,20,26,27]. Thus, it is important to understand and model the effect of radiation events on neuromorphic circuits.…”

Section: Introductionmentioning

confidence: 99%

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

Dahl

Ivans

Cantley

2019

Proceedings of the International Conference on Neuromorphic Systems

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In this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character 'B' by separating correlated and uncorrelated afferents. The network uses spike-timing-dependent plasticity (STDP) learning behavior, which is implemented using biphasic neuron spikes. A TiO2 memristor non-linear drift model is used to simulate synaptic behavior in the neuromorphic circuit. The network uses a many-to-one topology with 25 pre-synaptic neurons (afferent) each connected to a memristive synapse and one postsynaptic neuron. The memristor model is modified to include the experimentally observed effect of state-altering radiation. During the learning process, irradiation of the memristors alters their conductance state, and the effect on circuit learning behavior is determined. Radiation is observed to generally increase the synaptic weight of the memristive devices, making the network connections more conductive and less stable. However, the network appears to relearn the pattern when radiation ceases but does take longer to resolve the correlation and pattern. Network recovery time is proportional to flux, intensity, and duration of the radiation. Further, at lower but continuous radiation exposure, (flux 1x10 10 cm −2 s −1 and below), the circuit resolves the pattern successfully for up to 100 s. CCS CONCEPTS • Computer systems organization → Architectures →Other architectures → Neural networks • Hardware → Hardware test; Emerging technologies → Analysis and design of emerging devices and systems → Emerging architectures; Emerging Simulations

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Application of artificial neural networks to nuclear power plant transient diagnosis

Cited by 102 publications

References 5 publications

Nuclear safety in the unexpected second nuclear era

Nuclear safety in the unexpected second nuclear era

Statistical analysis of fuel failures in large break loss-of-coolant accident (LBLOCA) in EPR type nuclear power plant

Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation

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