A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next
Q Huang, S Peng, J Deng, H Zeng, Z Zhang, Y Liu… - Heliyon, 2023 - cell.com
As a form of clean energy, nuclear energy has unique advantages compared to other energy
sources in the present era, where low-carbon policies are being widely advocated. The …
sources in the present era, where low-carbon policies are being widely advocated. The …
[HTML][HTML] Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities
Deep learning algorithms provide plausible benefits for efficient prediction and analysis of
nuclear reactor safety phenomena. However, research works that discuss the critical …
nuclear reactor safety phenomena. However, research works that discuss the critical …
Near miss prediction in commercial aviation through a combined model of grey neural network
Owing to unprecedented level of safety management, there have been few commercial
aviation accidents recently, obstructing accurate prediction of safety trends. The innovative …
aviation accidents recently, obstructing accurate prediction of safety trends. The innovative …
Real-time prediction of nuclear power plant parameter trends following operator actions
J Bae, G Kim, SJ Lee - Expert Systems with Applications, 2021 - Elsevier
Operators in the main control room of a nuclear power plant (NPP) oversee all plant
operations, and thus any human error committed by the operators can be critical. If the …
operations, and thus any human error committed by the operators can be critical. If the …
Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting
G Lin, A Lin, J Cao - Expert Systems with Applications, 2021 - Elsevier
Stock time series forecasting is a universal purpose of academic researchers, even a slight
improvement in the accuracy of the forecast may have a fabulous impact on participants' …
improvement in the accuracy of the forecast may have a fabulous impact on participants' …
Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders
The anomalies in the high voltage converter modulator (HVCM) remain a major down time
for the spallation neutron source facility, that delivers the most intense neutron beam in the …
for the spallation neutron source facility, that delivers the most intense neutron beam in the …
Learning new physical descriptors from reduced-order analysis of bubble dynamics in boiling heat transfer
Understanding bubble dynamics during boiling is challenging due to the drastic changes in
system parameters, such as nucleation, bubble morphology, temperature, and pressure. In …
system parameters, such as nucleation, bubble morphology, temperature, and pressure. In …
Deep learning in nuclear industry: A survey
As a high-tech strategic emerging comprehensive industry, the nuclear industry is committed
to the research, production, and processing of nuclear fuel, as well as the development and …
to the research, production, and processing of nuclear fuel, as well as the development and …
Diagnosis and prediction for loss of coolant accidents in nuclear power plants using deep learning methods
J She, T Shi, S Xue, Y Zhu, S Lu, P Sun… - Frontiers in Energy …, 2021 - frontiersin.org
A combination of Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM),
and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and …
and Convolutional LSTM (ConvLSTM) is constructed in this work for the fault diagnosis and …
[HTML][HTML] Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code
Abstract Following the Fukushima Daiichi accident, enhancing the safety of nuclear power
plants has become the priority mission for the future of nuclear energy. Probabilistic safety …
plants has become the priority mission for the future of nuclear energy. Probabilistic safety …