Abstract A Machine Learning (ML) platform is proposed to assist the operator in diagnosing
the severe accident progression, where some of the signal is corrupted, and/or estimation of …

A machine learning platform is proposed for the diagnosis of a severe accident progression
in a nuclear power plant. To predict the key parameters for accident management including …

We propose a simulation and machine learning informed model (SMLIM) for the diagnosis of
severe accidents. A machine learning model which consisted of one hidden Long Short …

Several studies have proposed machine learning models to diagnose and predict accidents
in nuclear power reactors. However, the training data in these studies are deterministic, and …

Fourteen selected parameters of the reactor response were used to train a multi-connected
Support Vector Machines (SVM) model. With proper optimization, the SVM model …

This paper suggests a machine learning strategy for accident data in nuclear power plants
(NPPs) to assess accident conditions and provide informative real-time predictions for the …

We have developed a diagnostic methodology using machine learning (ML) technology to
figure out the important information including the break size, location, other remarkable …

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding
severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to …

In the event that any event such as a transient going beyond normal operating condition
happens in nuclear power plants (NPPs), accurately recognizing and identifying it is …

Emergency situations in nuclear power plants are accompanied by an automatic reactor
shutdown, which gives a big task burden to the plant operators under highly stressful …